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Principles of software testing – Software Testing

Software testing is an important aspect of software development , ensuring that applications function correctly and meet user expectations.

In this article, we will go into the principles of software testing , exploring key concepts and methodologies to enhance product quality . From test planning to execution and analysis, understanding these principles is vital for delivering robust and reliable software solutions.

Table of Content

Principles of Software Testing

Types of Software Testing

Frequently asked questions on principles of software testing.

Below-mentioned are the principles of software testing:

Testing shows the presence of defects
Exhaustive testing is not possible
Early testing
Defect clustering
Pesticide paradox
Testing is Context-Dependent
Absence of Errors fallacy

1. Testing shows the Presence of Defects

The goal of software testing is to make the software fail. Software testing reduces the presence of defects. Software testing talks about the presence of defects and doesn’t talk about the absence of defects. Software testing can ensure that defects are present but it can not prove that software is defect-free. Even multiple tests can never ensure that software is 100% bug-free. Testing can reduce the number of defects but not remove all defects.

2. Exhaustive Testing is not Possible

It is the process of testing the functionality of the software in all possible inputs (valid or invalid) and pre-conditions is known as exhaustive testing. Exhaustive testing is impossible means the software can never test at every test case. It can test only some test cases and assume that the software is correct and it will produce the correct output in every test case. If the software will test every test case then it will take more cost, effort, etc., which is impractical.

3. Early Testing

To find the defect in the software, early test activity shall be started. The defect detected in the early phases of SDLC will be very less expensive. For better performance of software, software testing will start at the initial phase i.e. testing will perform at the requirement analysis phase.

4. Defect Clustering

In a project, a small number of modules can contain most of the defects. The Pareto Principle for software testing states that 80% of software defects come from 20% of modules.

5. Pesticide Paradox

Repeating the same test cases, again and again, will not find new bugs. So it is necessary to review the test cases and add or update test cases to find new bugs.

6. Testing is Context-Dependent

The testing approach depends on the context of the software developed. Different types of software need to perform different types of testing. For example, The testing of the e-commerce site is different from the testing of the Android application.

7. Absence of Errors Fallacy

If a built software is 99% bug-free but does not follow the user requirement then it is unusable. It is not only necessary that software is 99% bug-free but it is also mandatory to fulfill all the customer requirements.

Unit Testing
Integration Testing
Regression Testing
Smoke Testing
System Testing
Alpha Testing
Beta Testing
Performance Testing

1. Unit Testing

Unit tests are typically written by developers as they write the code for a given unit. They are usually written in the same programming language as the software and use a testing framework or library that provides the necessary tools for creating and running the tests. These frameworks often include assertion libraries, which allow developers to write test cases that check the output of a given unit against expected results. The tests are usually run automatically and continuously as part of the software build process, and the results are typically displayed in a test runner or a continuous integration tool.

Unit Testing has several benefits, including:

Unit testing offers several benefits to software development:

Early Detection of Bugs : Unit tests can uncover bugs early in the development process, making them easier and cheaper to fix.
Improved Code Quality : Writing unit tests encourages developers to write modular, well-structured code that is easier to maintain and understand.
Regression Testing : Unit tests serve as a safety net, ensuring that changes or updates to the codebase do not introduce new bugs or break existing functionality.
Documentation : Unit tests can serve as documentation for the codebase, providing examples of how the code should be used and what behavior is expected.
Facilitates Refactoring : Unit tests give developers the confidence to refactor code without fear of introducing bugs, as they can quickly verify that the refactored code behaves as expected.

2. Integration Testing

Integration testing is a software testing method in which individual units or components of a software application are combined and tested as a group. The goal of integration testing is to validate that the interactions between the units or components of the software work as expected and that the software as a whole functions correctly.

Integration testing is typically performed after unit testing and before system testing. It is usually done by developers and test engineers, and it is usually carried out at the module level. Integration tests are typically automated and run frequently, as part of the software build process, to ensure that the software remains stable and free of defects over time.

Integration Testing has several benefits, including:

Detection of defects that may not be discovered during unit testing, as it examines the interactions between components.
Improved system design, as integration testing can help identify design weaknesses.
Improved software quality and reliability, as integration testing helps to ensure that the software as a whole functions correctly.
Facilitation of continuous integration and delivery, as integration testing helps to ensure that changes to the software do not break existing functionality.
Overall, integration testing is an essential part of software development that helps to ensure the quality and reliability of the software by identifying defects in the interactions between the units and components of the software early on in the development process.

3. Regression Testing

Regression testing is a software testing method in which previously developed and tested software is retested after it has been modified or changed. The goal of regression testing is to ensure that any changes to the software have not introduced new bugs or broken existing functionality. It is typically done to verify that changes such as bug fixes, new features, or updates to existing features have not affected the overall functionality of the software.

Regression testing is typically performed after unit testing and integration testing. It is usually done by developers and test engineers and it is usually carried out by re-running a suite of previously passed test cases. The test cases are chosen to cover the areas of the software that were affected by the changes and to ensure that the most critical functionality of the software is still working correctly. Regression testing is typically automated and run frequently, as part of the software build process, to ensure that the software remains stable and free of defects over time.

Regression Testing has several benefits , including:

Early detection and isolation of defects, can save time and money by allowing developers to fix errors before they become more costly to fix.
Improved software quality and maintainability, as regression testing helps to ensure that code changes do not break existing functionality.
Increased developer and user confidence, as regression testing helps to ensure that the software is still working correctly after changes have been made.
Facilitation of continuous integration and delivery, as regression testing helps to ensure that changes to the software can be safely released.
Overall, regression testing is an essential part of software development that helps to ensure

4. Smoke Testing

Smoke testing, also known as “Build Verification Testing” or “Build Acceptance Testing”, is a software testing method in which a minimal set of tests are run on a new build of a software application to determine if it is stable enough to proceed with further testing. The goal of smoke testing is to quickly identify and isolate major issues with the software build so that development can be halted if the build is found to be too unstable or unreliable.

Smoke testing is typically performed early in the software testing process, after the software has been built and before more extensive testing is done. It is usually done by developers and test engineers and it is usually carried out by running a small set of critical test cases that exercise the most important functionality of the software. Smoke tests are usually automated and can be run as part of the software build process.

Smoke Testing has several benefits , including:

Early identification of major issues, can save time and money by allowing developers to fix errors before they become more costly to fix.
Improved software quality and reliability, as smoke testing helps to ensure that the software is stable enough to proceed with further testing.
Facilitation of continuous integration and delivery, as smoke testing helps to ensure that new builds of the software are stable and reliable before they are released.
Overall, smoke testing is an important part of software development that helps to ensure the quality and reliability of the software by identifying major issues early on in the development process.
It helps to quickly determine if a new build of the software is stable enough to proceed with further testing, providing increased confidence in the software to the development team and end-users.

5. System Testing

System testing is a software testing method in which an entire software system is tested as a whole, to ensure that it meets the requirements and specifications that it was designed for. The goal of system testing is to validate that the software system behaves as expected when it is used in its intended environment and that it meets all the requirements for functionality, performance, security, and usability.

System testing is typically performed after unit testing, integration testing, and regression testing. It is usually done by test engineers and it is usually carried out by running a set of test cases that cover all the functionality of the software. The test cases are chosen to cover the requirements and specifications of the software and to ensure that the software behaves correctly under different conditions and scenarios. System testing is typically automated and run frequently, as part of the software build process, to ensure that the software remains stable and free of defects over time.

System Testing has several benefits , including:

Early detection and isolation of defects, which can save time and money by allowing developers to fix errors before they become more costly to fix.
Improved software quality and reliability, as system testing helps to ensure that the software meets all the requirements and specifications that it was designed for.
Increased user confidence, as system testing helps to ensure that the software behaves correctly when it is used in its intended environment.
Facilitation of acceptance testing, as system testing helps to ensure that the software is ready for release.
Overall, system testing is an essential part of software development that helps to ensure the quality and reliability of the software by identifying defects early on in the development process.
It helps to ensure that the software meets all the requirements and specifications that it was designed for, providing increased confidence in the software to the development team and end-users.

Software testing is essential for ensuring applications meet user expectations and function correctly. Understanding key principles like detecting defects early and recognizing the impossibility of exhaustive testing is vital for delivering reliable software.

Various types of testing , including unit, integration, regression, smoke, and system testing, offer unique benefits like early bug detection and improved code quality. By embracing these principles and employing diverse testing methods, developers can enhance product quality and user satisfaction.

What is the principle of testing?

Testing shows the presence of defects, not their absence.

What is first principle in testing?

Fast, Independent, Repeatable, Self-Checking and Timely.

What is TDD full form?

Test-driven development (TDD)

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Software Testing

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Software Testing Profession Report (Assessment)

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Software Testing Skills and Mindset

Top five assumptions in systems test plans, challenging the assumptions.

Software testing is an integral part of the software development process. Without testing software prior to its application and promotion to the market, it is very likely to be filled with various bugs and malfunctions. Receiving an incomplete product would upset the customers and damage the company’s name. There are many examples of poorly tested software causing massive negative feedback and loss of profit. Some of the recent examples involve the release of Rome 2 – Total War and Star Wars Battlefront 2 computer games, which came under fire for numerous bugs and crashes that were missed by the software testing team due to various internal issues and project constraints (Gault & Maiberg, 2017). The profession of a software tester is paramount to the programming industry, as it allows for spotting and fixing issues that were missed during the product creation stage.

Prior to watching the video about software testing, I assumed that the most valued skill in software testing was the ability to methodically check and perform many basic and redundant testing sequences in order to spot any potential flaws that might occur when performing basic functions. My reasoning behind this assumption was that the majority of programming errors could be spotted this way, whereas more complicated and hidden errors would not be visible to the naked eye and would only be revealed after a series of reported user complaints. However, this assumption was incorrect. The video made me realize that spotting basic functionality errors is not that difficult and does not require any exceptional skills and dedication. The most important skills in a software testing are the ability to critically think and analyze the situation in order to spot hidden and complex programming errors, and communication skills in order to properly relay and explain these findings to the developers and customers (EuroSTAR, 2012).

Although every system test plan is individually fitted to a specific product or software, there are always assumptions that must be made in order for the system to operate efficiently. The majority of the assumptions regarding a particular project are outlined during the project planning phase (Merkow & Raghavan, 2010). However, there are several assumptions that are considered universal and can be largely applied to any systems test plans. These assumptions are as follows (Jorgensen, 2016):

Testing will be conducted within the implementation timeframes outlined by the testers and approved by the customers.
The test team will be provided with all the tools necessary for conducting quality systems tests specific to the software in question.
The configurations of testing hardware and the software will be similar to those present in the production environment.
The test team will be provided with an acceptable environment in which the testing will be conducted.
The test team will be provided with all the requirements necessary for the completion of the project, which includes business requirements, system requirements, and data requirements.

These basic assumptions are used as a foundation, upon which the majority of the testing projects are being based.

Although the five basic assumptions are theoretically applicable to any project, the realities of the market indicate that some assumptions cannot be met due to a series of events prior to or during the project that lies outside of the customers’ or test team’s control. Depending on the situation, some of the assumptions will not be met. This tends to have a negative effect on the quality of the software testing process.

In order to demonstrate this, we can once more re-assess the blunder that Rome 2: Total War was on release due to a series of errors and poor test runs on the part of the Creative Assembly. The first assumption on the list was that tests are to be conducted within the implementation timeframes outlined by the testers and approved by the customers. However, the core reason behind the number of bugs and glitches in the game was tied to the pressure by Sega on the Creative Assembly to release the game earlier (Grayson, 2013). Thus, the implementation timeframes were based solely on the needs of the customers and not on the realistic expectations of the development and testing teams. As a result, the product was unfinished upon release. This situation happens in projects that are urgent and have severe time constraints. It affects product testing in a negative way.

Another example of basic assumptions not being met can be found in the day-to-day work of testing and programming industries. The last assumption on the list states that the test team is to be provided with all the requirements necessary for the completion of the project, which includes business requirements, system requirements, and data requirements. However, as it often happens, the customers do not have a clear vision of what their software is expected to do. Therefore, a situation occurs where the requirements are not clear, which makes formulating software-specific assumptions harder. Without clear requirements, it is impossible to assess the presence or absence of certain functions that may later be needed by the end-users of the product, making it impossible for the test team to locate these errors (Stafford, 2014). In order to ensure quality, the test team must have a clear vision of what the end product is supposed to do as well as the requirements for the system and the quality of the provided data.

EuroSTAR. (2012). What is the most important skill a software tester should have? [Video file]. Web.

Gault, M., & Maiberg, E. (2017). ‘Star Wars Battlefront II’ is everything that’s wrong with big budget games. Web.

Grayson, N. (2013). Et tu, CA? – Rome II devs apologize for issues. Web.

Jorgensen, P. C. (2016). Software testing: A craftsman’s approach. New York, Boca Raton, FL: CRC Press.

Merkow, M. S., & Raghavan, L. (2010). Secure and resilient software development. Boca Raton, FL: CRC Press.

Stafford, W. (2014). The importance of requirements gathering for software projects. Web.

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IvyPanda. (2020, December 25). Software Testing Profession. https://ivypanda.com/essays/software-testing-profession/

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7 Principles of Software Testing

Software Testing is an integral part of SDLC (Software Development Life Cycle). Testing software provides insight into gaps, errors, and defects in the product, the quality of the software being developed, and the completeness of specifications in accordance with business, user, and product requirements. During testing, it is quite important to achieve the best results possible without deviating from the goal. In light of that, how can we be sure we are following the correct testing strategy?

So, in a situation like this, it is always a good idea to review previous processes and testing guidelines to ensure you follow best practices. You can start your testing journey by studying the seven principles of software testing outlined by ISTQB (International Software Testing Qualifications Board). Though we have been using testing principles for years, many of us may not realize how valuable they are.

In this article, we’ll look at seven principles of software testing that can help make the testing process more effective and lead to the development of higher-quality software. Before continuing, let’s first understand what software testing is all about, and why software testing principles matter.

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What is software testing, why are software testing principles important.

Principles of Software Testing

1. Testing Shows the Presence of Defects

2. exhaustive testing is impossible, 3. early testing, 4. defect clustering, 5. pesticide paradox, 6. testing is context-dependent, 7. absence of error – fallacy, q.1: how many principles of software testing are there, q.2: what are the different types of testing, q.3: what is software testing methodology, additional resources.

Software testing refers to the process of validating and verifying the artifacts and behaviour of the software under test. Software testing involves:

Verify and validate that the product is bug-free.
Determines if the product complies with the technical specifications, according to the design and development.
Ensure the product meets the user’s requirements efficiently and effectively.
Measure the product’s functionality and performance.
Find ways to improve software efficiency, accuracy, and usability.

When the testing is done, bugs or errors in the software can be found early on and can be fixed before the software is delivered. Properly tested software products deliver high performance, reliable security, and cost-effectiveness, ultimately leading to higher customer satisfaction. During this process, different aspects of a product are examined, analyzed, observed, and evaluated using manual testing or automated tools by software testers. As soon as the testers have finished conducting tests, they report the results to the development team. In the end, it’s all about delivering high quality to the customer, which is why software testing is so essential.

The belief is ubiquitous that success comes from adhering to a set of principles. Whether to stay fit, meet work-related goals, or achieve life goals, there are often specific targets and rules we must follow to accomplish them. The same holds true for software testing. As a whole, software testing principles describe how software testers or testing engineers should create bug-free, clear, and maintainable code. Engineering is not a science in which you can wave a magic wand and make a jumble of variables, classes, and functions into flawless code, but you can use some principles to determine if you are doing things right. Test principles will help you draft error-catching test cases and create an effective Test Strategy. Software testing is governed by the following seven principles:

Testing shows the presence of defects
Exhaustive testing is not possible
Early testing
Defect clustering
Pesticide paradox
Testing is context dependent
Absence of errors fallacy

Now, what are these principles of software testing? Let’s take a look at the 7 Software Testing Principles curated for you.

Software testing is an incredibly imaginative and intellectual activity for testers. Every software tester should review and understand these 7 principles, as this will help them achieve high-quality standards, as well as give their clients confidence that their software is production-ready. Living by these principles will help your project progress seamlessly. Check them out:

As stated in this testing principle, “Testing talks about the presence of defects and doesn’t talk about the absence of defects”. In software testing, we look for bugs to be fixed before we deploy systems to live environments – this gives us confidence that our systems will work correctly when goes live to users. Despite this, the testing process does not guarantee that the software is 100% error-free. It is true that testing greatly reduces the number of defects buried in software, however discovering and repairing these problems does not guarantee a bug-free product or system.

Even if testers cannot find defects after repeating regression testing, it does not mean the software is 100 % bug-free. For instance, an application may appear to be error-free after passing various stages of testing, but when it is deployed in the environment, an unexpected defect can be found. Team members should always adhere to this concept, and effort should be made to manage client expectations.

Exhaustive testing usually tests and verifies all functionality of a software application while using both valid and invalid inputs and pre-conditions. No matter how hard you try, testing EVERYTHING is pretty much impossible. The inputs and outputs alone have an infinite number of combinations, so it is 100% not possible to test an application from every angle.

Consider the case when we have to test an input field that accepts percentages between 50 and 55, so we test the field using 50, 51, 52, 53, 54, 55. Assuming that the same input field accepts values from 50 to 100, we will need to test using 50, 51, 52, 53, …., 99, 100. This is a basic example. You may think that an automation tool would be able to accomplish this. But imagine a field that accepts a billion values. Will it be possible to test all possible values?

As long as we continue to test all possible scenarios, the software execution time and cost will increase. In order to avoid doing exhaustive testing, we will take into consideration some important testing criteria effects such as risks and priorities as part of our testing efforts and estimates.

In software development, early testing means incorporating testing as early as possible in the development process. It plays a critical role in the software development lifecycle (SDLC). For instance, testing the requirements before coding begins. Amending issues during this stage of a project’s life cycle is much cheaper and easier than amending issues at the end of the project when we must write new sections of functionality, resulting in overruns and late deadlines. The cost to fix a bug increases exponentially with time as the development life cycle progresses as shown in the following figure.

Let’s consider two scenarios. In the first case, you found an incorrect requirement in the requirement gathering phase. In the second case, you found a defect in a fully developed functionality. It is less expensive to fix the incorrect requirement than fully developed functionality that isn’t working the way it should. Therefore, to improve software performance, software testing should begin at the initial phase, that is, during requirement analysis.

In software testing, defect clustering refers to a small module or feature that has the most bugs or operation issues. This is because defects are not evenly distributed within a system but are clustered. It could be due to multiple factors, such as the modules might be complicated or the coding related to such modules might be complex.

Pareto Principle (80-20 Rule) states that 80% of issues originate from 20% of modules, while the remaining 20% originate from the remaining 80% of modules. Thus, we prioritize testing on 20% of modules where we experience 80% of bugs.

For an effective testing strategy, it is necessary to thoroughly examine these areas of the software. The defect clustering method relies on the teams’ knowledge and experience to identify which modules to test. You can identify such risky modules from your experience. Therefore, the team only has to focus on those “sensitive” areas, saving both time and effort.

In software testing, the Pesticide Paradox generally refers to the practice of repeating the exact same test cases over and over again. As time passes, these test cases will cease to find new bugs. Developers will create tests which are passing so they can forget about negative or edge cases. This is based on the theory that when you repeatedly spray the same pesticide on crops in order to eradicate insects, the insects eventually develop an immunity, making the pesticide ineffective. The same is true for software testing.

Therefore, in order to overcome the Pesticide Paradox, it is imperative to regularly review and update the test cases so that more defects can be found. However, if this process is not followed, and the same tests are repeated over and over again, then eventually there will be no new bugs found, but it doesn’t mean the system is 100 % bug free. To make testing more effective, testers must constantly look for ways to improve the existing test methods. To test new features of the software or system, new tests must be developed.

Each type of software system is tested differently. According to this principle, testing depends on the context of the software developed, and this is entirely true. The reality is that every application has its own unique set of requirements, so we can’t put testing in a box. Of course, every application goes through a defined testing process, however, the testing approach may vary based on the application type.

Various methodologies, techniques, and types of testing are used depending on the nature of an application. For example, health industry applications require more testing than gaming applications, safety-critical systems (such as an automotive or airplane ECU) require more testing than company presentation websites, and online banking applications will require different testing approaches than e-commerce sites or advertising sites.

The software which we built not only must be 99% bug-free software but also it must fulfill the business, as well as user requirements otherwise it will become unusable software. Even bug-free software may still be unusable if incorrect requirements are incorporated into the software, or if the software fails to meet the business needs.

If you build it, they will come!!! There is a myth that if you build a bug-free system, users will come and use it, but this is not true. In order for software systems to be usable, it must not only be 99% bug-free software but also fulfill the business needs and user requirements. So, irrespective of how flawless or error-free a system may be, if it lacks usability and is hard to use, or if it does not match business/user needs, it is only a failure.

As you have seen, the seven principles of software testing lead to high-quality products. Incorporating these thoughtful principles into your testing can help you gain greater efficiency and focus, as well as improve your overall testing strategy. Added to that, you’ll often find that applying one principle will result in other principles naturally falling into place. Early testing, for example, can help mitigate the “absence of errors fallacy”- incorporating testers at the requirements stage can help ensure the software meets client expectations/needs. Combining all these principles can help you utilize your time and effort efficiently and effectively.

With this, we conclude our “Principles of Software Testing” blog. Hope you enjoyed reading this article and got a good understanding of what the different principles are.

Ans: To increase the efficiency of software testing, seven principles are used. Using the seven principles, testing teams can make the most of their time and effort. Following these principles will assist your project is progressing smoothly.

Ans: The following are seven principles of software testing:

Testing shows the presence of defects in the software
Testing is context-dependent

Ans: A software testing methodology refers to a set of strategies and approaches used to test an application to ensure it functions and appears in a way the user expects and meets business requirements. They include everything from front-end to back-end testing, as well as unit and system testing.

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1 A Brief Essay on Software Testing

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Test case generation is among the most labour-intensive tasks in software testing and also one that has a strong impact on the e ectiveness and e ciency of software testing. For these reasons, it has also been one of the most active topics in the research on software testing for several decades, resulting in many di erent approaches and tools. This paper presents an orchestrated survey of the most prominent techniques for automatic generation of software test cases, reviewed in self-standing sections. The techniques presented include: (a) structural testing using symbolic execution, (b) model-based testing, (c) combinatorial testing, (d) random testing and its variant of adaptive random testing, and (e) search-based testing. Each section is contributed by worldrenowned active researchers on the technique, and briefly covers the basic ideas underlying the technique, the current state of art, a discussion of the open research problems, and a perspective of the future development in th...

Richard J Webber

The maturity of object-oriented methods has led to the wide availability of container classes: classes that encapsulate classical data structures and algorithms. Container classes are included in the C++ and Java standard libraries, and in many proprietary libraries. The wide availability and use of these classes makes reliability important, and testing plays a central role in achieving that reliability. The large number of cases necessary for thorough testing of container classes makes automated testing essential. This paper presents a novel approach for automated testing of container classes based on combinatorial algorithms for state generation. The approach is illustrated with black-box and white-box test drivers for a class implemented with the red-black tree data structure, used widely in industry and, in particular, in the C++ Standard Template Library. The white-box driver is based on a new algorithm for red-black tree generation. The drivers are evaluated experimentally, providing quantitative measures of their effectiveness in terms of block and path coverage. The results clearly show that the approach is affordable in terms of development cost and execution time, and effective with respect to coverage achieved. The results also provide insight into the relative advantages of black-box and white-box drivers, and into the difficult problem of infeasible paths.

2019 21st Symposium on Virtual and Augmented Reality (SVR)

Stevão Andrade

Background: Software testing is a critical activity to ensure that software complies with its specification. However, current software testing activities tend not to be completely effective when applied in specific software domains in Virtual Reality (VR) that has several new types of features such as images, sounds, videos, and differentiated interaction, which can become sources of new kinds of faults. Aims: This paper presents an overview of the main VR characteristics that can have an impact on verification, validation, and testing (VV&T). Furthermore, it analyzes some of the most successful VR open-source projects to draw a picture concerning the danger of the lack of software testing activities. Method: We compared the current state of software testing practice in open-source VR projects and evaluate how the lack of testing can be damaging to the development of a product. We assessed the incidence of code smells and verified how such projects behave concerning the tendency to ...

juan felipe

Department of Computer Science, King��s College London, Tech. Rep. TR-10-01

Mustafa Bozkurt

The Service-Oriented Computing (SOC) paradigm is allowing computer systems to interact with each other in new ways. According to the literature, SOC allows composition of distributed applications free from their platform and thus reduces the cost of such compositions and makes them easier and faster to develop. Currently web services are the most widely accepted service technology due to the level of autonomy and platform-independency they provide. However, web services also bring challenges. For example, ...

Maurice Frayssinet

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A Brief Essay on Software Testing

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Antonia Bertolino, Eda Marchetti

Abstract— Testing is an important and critical part of the software development process, on which the quality and reliability of the delivered product strictly depend. Testing is not limited to the detection of “bugs” in the software, but also increases confidence in its proper functioning and assists with the evaluation of functional and nonfunctional properties. Testing related activities encompass the entire development process and may consume a large part of the effort required for producing software. In this chapter we provide a comprehensive overview of software testing, from its definition to its organization, from test levels to test techniques, from test execution to the analysis of test cases effectiveness. Emphasis is more on breadth than depth: due to the vastness of the topic, in the attempt to be all-embracing, for each covered subject we can only provide a brief description and references useful for further reading.

Index Terms — D.2.4 Software/Program Verification, D.2.5 Testing and Debugging.

—————————— u ——————————

1. INTRODUCTION esting is a crucial part of the software life cycle, and related issues, we can only briefly expand on each argu- T recent trends in software engineering evidence the ment, however plenty of references are also provided importance of this activity all along the development throughout for further reading. The remainder of the chap- process. Testing activities have to start already at the re- ter is organized as follows: we present some basic concepts quirements specification stage, with ahead planning of test in Section 2, and the different types of test (static and dy- strategies and procedures, and propagate down, with deri- namic) with the objectives characterizing the testing activity vation and refinement of test cases, all along the various in Section 3. In Section 4 we focus on the test levels (unit, development steps since the code-level stage, at which the integration and system test) and in Section 5 we present the test cases are eventually executed, and even after deploy- techniques used for test selection. Going on, test design, ment, with logging and analysis of operational usage data execution, documentation,d management are described in and customer’s reported failures. Sections 6, 7, 8 and 9, respectively. Test measurement issues Testing is a challenging activity that involves several high- are discussed in Section 10 and finally the chapter conclu- demanding tasks: at the forefront is the task of deriving an sions are drawn in Section 11. adequate suite of test cases, according to a feasible and cost- effective test selection technique. However, test selection is 2. TERMINOLOGY AND BASIC CONCEPTS just a starting point, and many other critical tasks face test practitioners with technical and conceptual difficulties Before deepening into testing techniques, we provide here (which are certainly under-represented in the literature): some introductory notions relative to testing terminology the ability to launch the selected tests (in a controlled host and basic concepts. environment, or worse in the tight target environment of an 2.1 On the nature of the testing discipline embedded system ); deciding whether the test outcome is acceptable or not (which is referred to as the test oracle As we will see in the remainder of this chapter, there exist problem); if not, evaluating the impact of the failure and many types of testing and many test strategies, however all finding its direct cause (the fault), and the indirect one (via of them share a same ultimate purpose: increasing the Root Cause Analysis); judging whether testing is sufficient software engineer confidence in the proper functioning of and can be stopped, which in turn would require having at the software. hand measures of the effectiveness of the tests: one by one, Towards this general goal, a piece of software can be tested each of these tasks presents tough challenges to testers, for to achieve various more direct objectives, all meant in fact which their skill and expertise always remains of topmost to increase confidence, such as exposing potential design importance. flaws or deviations from user’s requirements, measuring We provide here a short, yet comprehensive overview of the operational reliability, evaluating the performance the testing discipline, spanning over test levels, test tech- characteristics, and so on (we further expand on test objec- niques and test activities. In an attempt to cover all testing tives in Section 3.3); to serve each specific objective, differ- ent techniques can be adopted. ———————————————— Generally speaking, test techniques can be divided into two · Antonia Bertolino is with the Istituto di Scienza e Tecnologie “A. Faedo” Area della ricerca CRD di Pisa, Via Moruzzi 1, 56124 Pisa Italy. classes: E-mail: [email protected] . · Static analysis techniques (expanded in Section 3.1), · Eda Marchetti is with the Istituto di Scienza e Tecnologie “A. Faedo” Area where the term “static” does not refer to the techniques della ricerca CRD di Pisa, Via Moruzzi 1, 56124 Pisa Italy. E-mail: [email protected] . themselves (they can use automated analysis tools), but

is used to mean that they do not involve the execution the observed outcomes of program execution are acceptable of the tested system. Static techniques are applicable or not. throughout the lifecycle to the various developed arti- facts for different purposes, such as to check the adher- 2.3 Fault vs. Failure ence of the implementation to the specifications or to To fully understand the facets of software testing, it is im- detect flaws in the code via inspection or review. portant to clarify the terms “fault”, “error”1 and “failure”: · Dynamic analysis techniques (further discussed in Sec- indeed, although their meanings are strictly related, there tion 3.2), which exercise the software in order to expose are important distinctions between these three concepts. possible failures. The behavioral and performance A failure is the manifested inability of the program to per- properties of the program are also observed. form the function required, i.e., a system malfunction evi- Static and dynamic analyses are complementary techniques denced by incorrect output, abnormal termination or unmet [1]: the former yield generally valid results, but they may time and space constraints. The cause of a failure, e.g., a be weak in precision; the latter are efficient and provide missing or incorrect piece of code, is a fault. A fault may more precise results, but only holding for the examined remain undetected long time, until some event activates it. executions. The focus of this chapter will be mainly on dy- When this happens, it first brings the program into an in- namic test techniques, and where not otherwise specified termediate unstable state, called error, which, if and when testing is used as a synonymous for “dynamic testing”. propagates to the output, eventually causes the failure. The Unfortunately, there are few mathematical certainties on process of failure manifestation can be therefore summed which software testing foundations can lay. The firmest up into a chain [42]: one, as everybody now recognizes, is that, even after suc- Fault®Error®Failure cessful completion of an extensive testing campaign, the which can recursively iterate: a fault in turn can be caused software can still contain faults. As firstly stated by Dijkstra by the failure of some other interacting system. as early as thirty years ago [22], testing can never prove the In any case what testing reveals are the failures and a con- absence of defects, it can only possibly reveal the presence sequent analysis stage is needed to identify the faults that of faults by provoking malfunctions. In the elapsed dec- caused them. ades, lot of progress has been made both in our knowledge The notion of a fault however is ambiguous and difficult to of how to scrutinize a program’s executions in rigorous and grasp, because no precise criteria exist to definitively de- systematic ways, and in the development of tools and proc- termine the cause of an observed failure. It would be pref- esses that can support the tester’s tasks. erable to speak about failure-causing inputs, that is, those Yet, the more the discipline progresses, the clearer it be- sets of inputs that when exercised can result into a failure. comes that it is only by means of rigorous empirical studies 2.4 The notion of software reliability that software testing can increase its maturity level [35]. Indeed, whether few or many, some faults will inevitably Testing is in fact an engineering discipline, and as such it escape testing and debugging. However, a fault can be calls for evidences and proven facts, to be collected either more or less disturbing depending on whether, and how from experience or from controlled experiments, and cur- frequently, it will eventually show up to the final user (and rently lacking, based on which testers can make predictions and take decisions. depending of course on the seriousness of its conse- quences). 2.2 A general definition So, in the end, one measure which is important in deciding Testing can refer to many different activities used to check whether a software product is ready for release is its reli- a piece of software. As said, we focus primarily on “dy- ability. Strictly speaking, software reliability is a probabilistic namic” software testing presupposing code execution, for estimate, and measures the probability that the software which we re-propose the following general definition in- will execute without failure in a given environment for a troduced in [9]: given period of time [44]. Thus, the value of software reliabil- Software testing consists of the dynamic verification of the behav- ity depends on how frequently those inputs that cause a ior of a program on a finite set of test cases, suitably selected from failure will be exercised by the final users. the usually infinite executions domain, against the specified ex- Estimates of software reliability can be produced via test- pected behavior. ing. To this purpose, since the notion of reliability is specific This short definition attempts to include all essential testing to “a given environment”, the tests must be drawn from an concerns: the term dynamic means, as said, that testing im- input distribution that approximates as closely as possible plies executing the program on (valued) inputs; finite indi- the future usage in operation, which is called the operational cates that only a limited number of test cases can be exe- distribution. cuted during the testing phase, chosen from the whole test set, that can generally be considered infinite; selected refers to the test techniques adopted for selecting the test cases (and testers must be aware that different selection criteria may yield vastly different effectiveness); expected points out to the decision process adopted for establishing whether 1 Note that we are using the term “error” with the commonly used mean- ing within the Software Dependability community [42], which is stricter than its general definition in [28]. 3

3. TYPES OF TESTS both research institutions and industries and it is foresee- able that proofs of correctness will be increasingly applied, The one term testing actually refers to a full range of test especially for the verification of critical systems. techniques, even quite different from one other, and em- One of the most promising approaches for formal verifica- braces a variety of aims. tion is model checking [18]. Essentially, a model checking tool 3.1 Static Techniques takes in input a model (a description of system functional As said, a coarse distinction can be made between dynamic requirements or design) and a property that the system is and static techniques, depending on whether the software expected to satisfy. is executed or not. Static techniques are based solely on the In the middle between static and dynamic analysis tech- (manual or automated) examination of project documenta- niques, is symbolic execution [38], which executes a program tion, of software models and code, and of other related in- by replacing variables with symbolic values. formation about requirements and design. Thus static tech- Quite recently, the automated generation of test data for niques can be employed all along development, and their coverage testing is again attracting lot of interest, and ad- earlier usage is of course highly desirable. Considering a vanced tools are being developed based on a similar ap- generic development process, they can be applied [49]: proach to symbolic execution exploiting constraint solving · at the requirements stage for checking language syntax, techniques [3]. A flowgraph path to be covered is translated consistency and completeness as well as the adherence into a path constraint, whose solution provides the desired to established conventions; input data. · at the design phase for evaluating the implementation We conclude this section considering the alternative appli- of requirements, and detecting inconsistencies (for in- cation of static techniques in producing values of interest stance between the inputs and outputs used by high for controlling and managing the testing process. Different level modules and those adopted by sub-modules). estimations can be obtained by observing specific proper- · during the implementation phase for checking that the ties of the present or past products, and/or parameters of form adopted for the implemented products (e.g., code the development process.. and related documentation) adheres to the established 3.2 Dynamic Techniques standards or conventions, and that interfaces and data Dynamic techniques [1] obtain information of interest about types are correct. a program by observing some executions. Standard dy- Traditional static techniques include [7], [50]: namic analyses include testing (on which we focus in the · Software inspection: the step-by-step analysis of the rest of the chapter) and profiling. Essentially a program pro- documents (deliverables) produced, against a compiled file records the number of times some entities of interest checklist of common and historical defects. occur during a set of controlled executions. Profiling tools · Software reviews: the process by which different aspects are increasingly used today to derive measures of coverage, of the work product are presented to project personnel for instance in order to dynamically identify control flow (managers, users, customer etc) and other interested invariants, as well as measures of frequency, called spectra, stakeholders for comment or approval. which are diagrams providing the relative execution fre- · Code reading: the desktop analysis of the produced code quencies of the monitored entities. In particular, path spectra for discovering typing errors that do not violate style or refer to the distribution of (loop-free) paths traversed dur- syntax. ing program profiling. Specific dynamic techniques also · Algorithm analysis and tracing: is the process in which include simulation, sizing and timing analysis, and proto- the complexity of algorithms employed and the worst- typing [49]. case, average-case and probabilistic analysis evalua- Testing properly said is based on the execution of the code tions can be derived. on valued inputs. Of course, although the set of input val- The processes implied by the above techniques are heavily ues can be considered infinite, those that can be run effec- manual, error-prone, and time consuming. To overcome tively during testing are finite. It is in practice impossible, these problems, researchers have proposed static analysis due to the limitations of the available budget and time, to techniques relying on the use of formal methods [19]. The exhaustively exercise every input of a specific set even goal is to automate as much as possible the verification of when not infinite. In other words, by testing we observe the properties of the requirements and the design. Towards some samples of the program’s behavior. this goal, it is necessary to enforce a rigorous and unambi- A test strategy therefore must be adopted to find a trade-off guous formal language for specifying the requirements and between the number of chosen inputs and overall time and the software architecture. In fact, if the language used for effort dedicated to testing purposes. Different techniques specification has a well-defined semantics, algorithms and can be applied depending on the target and the effect that tools can be developed to analyze the statements written in should be reached. We will describe test selection strategies that language. in Section 5. The basic idea of using a formal language for modeling re- In the case of concurrent, non-deterministic systems, the quirements or design is now universally recognized as a results obtained by testing depend not only on the input foundation for software verification. Formal verification tech- provided but also on the state of the system. Therefore, niques are attracting today quite a lot attention from both when speaking about test input values, it is implied that the 4 definition of the parameters and environmental conditions plies with its spec ified requirements]”. In practice, the that characterize a system state must be included when objective is to show that a system which previously necessary. passed the tests still does [51]. Notice that a trade-off Once the tests are selected and run, another crucial aspect must be made between the assurance given by regres- of this phase is the so-called oracle problem, which means sion testing every time a change is made and the re- deciding whether the observed outcomes are acceptable or sources required to do that. not (see Section 7.2). · Performance testing: this is specifically aimed at veri- fying that the system meets the specified performance 3.3 Objectives of testing requirements, for instance, capacity and response time Software testing can be applied for different purposes, such [51]. as verifying that the functional specifications are imple- · Usability testing : this important testing activity evalu- mented correctly, or that the system shows specific non- ates the ease of using and learning the system and the functional properties such as performance, reliability, us- user documentation, as well as the effectiveness of sys- ability. A (certainly non complete) list of relevant testing tem functioning in supporting user tasks, and, finally, objectives includes: the ability to recover from user errors [51]. · Acceptance/qualification testing: the final test action · Test-driven development: test-driven development is prior to deploying a software product. Its main goal is not a test technique per se, but promotes the use of test to verify that the software respects the customer’s re- case specifications as a surrogate for a requirements quirement. Generally, it is run by or with the end-users document rather than as an independent check that the to perform those functions and tasks the software was software has correctly implemented the requirements built for [51]. [6]. · Installation testing: the system is verified upon instal- lation in the target environment. Installation testing can be viewed as system testing conducted once again ac- 4. TEST LEVELS cording to hardware configuration requirements. In- During the development lifecycle of a software product, stallation procedures may also be verified [51]. testing is performed at different levels and can involve the · Alpha testing: before releasing the system, it is de- whole system or parts of it. Depending on the process ployed to some in-house users for exploring the func- model adopted, then, software testing activities can be ar- tions and business tasks. Generally there is no test plan ticulated in different phases, each one addressing specific to follow, but the individual tester determines what to needs relative to different portions of a system. Whichever do [36]. the process adopted, we can at least distinguish in principle · Beta Testing: the same as alpha testing but the system between unit, integration and system test [7], [51]. These are is deployed to external users. In this case the amount of the three testing stages of a traditional phased process (such detail, the data, and approach taken are entirely up to as the classical waterfall). However, even considering dif- the individual testers. Each tester is responsible for cre- ferent, more modern, process models, a distinction between ating their own environment, selecting their data, and these three test levels remains useful to emphasize three determining what functions, features, or tasks to ex- logically different moments in the verification of a complex plore. Each tester is also responsible for identifying software system. their own criteria for whether to accept the system in None of these levels is more relevant than another, and its current state or not [36]. more importantly a stage cannot supply for another, be- · Reliability achievement: as said in Section 2.4, testing cause each addresses different typologies of failures. can also be used as a means to improve reliability; in 4.1 Unit Test such a case, the test cases must be randomly generated according to the operational profile, i.e., they should A unit is the smallest testable piece of software, which may sample more densely the most frequently used func- consist of hundreds or even just a few lines of source code, tionalities [44]. and generally represents the result of the work of one pro- · Conformance Testing /Functional Testing: the test grammer. The unit test’s purpose is to ensure that the unit cases are aimed at validating that the observed behav- satisfies its functional specification and/or that its imple- ior conforms to the specifications. In particular it mented structure matches the intended design structure [7], checks whether the implemented functions are as in- [51]. tended and provide the required services and methods. Unit tests can also be applied to check interfaces (parame- This test can be implemented and executed against dif- ters passed in correct order, number of parameters equal to ferent tests targets, including units, integrated units, number of arguments, parameter and argument matching), and systems [50]. local data structure (improper typing, incorrect variable · Regression testing : According to [28], regression testing name, inconsistent data type) or boundary conditions. A is the “selective retesting of a system or component to good reference for unit test is [30]. verify that modifications have not caused unintended effects and that the syustem or component still com- 5

4.2 Integration Test 4.3 System Test Generally speaking, integration is the process by which System test involves the whole system embedded in its ac- software pieces or components are aggregated to create a tual hardware environment and is mainly aimed at verify- larger component. Integration testing is specifically aimed ing that the system behaves according to the user require- at exposing the problems that can arise at this stage. Even ments. In particular it attempts to reveal bugs that cannot though the single units are individually acceptable when be attributed to components as such, to the inconsistencies tested in isolation, in fact, they could still result in incorrect between components, or to the planned interactions of or inconsistent behaviour when combined in order to build components and other objects (which are the subject of in- complex systems. For example, there could be an improper tegration testing). Summarizing the primary goals of sys- call or return sequence between two or more components tem testing can be [13]: [7]. Integration testing thus is aimed at verifying that each · discovering the failures that manifest themselves only component interacts according to its specifications as de- at system level and hence were not detected during fined during preliminary design. In particular, it mainly unit or integration testing; focuses on the communication interfaces among integrated · increasing the confidence that the developed product components. correctly implements the required capabilities; There are not many formalized approaches to integration · collecting information useful for deciding the release of testing in the literature, and practical methodologies rely the product. essentially on good design sense and the testers’ intuition. System testing should therefore ensure that each system Integration testing of traditional systems was done substan- function works as expected, any failures are exposed and tially in either a non-incremental or an incremental ap- analyzed, and additionally that interfaces for export and proach. In a non-incremental approach the components are import routines behave as required. linked together and tested all at once (“big-bang” testing) System testing makes available information about the ac- [34]. In the incremental approach, we find the classical tual status of development that other verification tech- “top-down” strategy, in which the modules are integrated niques such as review or inspections on models and code one at a time, from the main program down to the subordi- cannot provide. nated ones, or “bottom-up”, in which the tests are con- Generally system testing includes testing for performance, structed starting from the modules at the lowest hierarchi- security , reliability, stress testing and recovery [34], [51]. In cal level and then are progressively linked together up- particular, test and data collected applying system testing wards, to construct the whole system. Usually in practice, a can be used for defining an operational profile necessary to mixed approach is applied, as determined by external pro- support a statistical analysis of system reliability [44]. ject factors (e.g., availability of modules, release policy, A further test level, called Acceptance Test, is often added to availability of testers and so on) [51]. the above subdivision. This is however more an extension In modern Object Oriented, distributed systems, ap- of system test, rather than a new level. It is in fact a test ses- proaches such as top-down or bottom-up integration and sion conducted over the whole system, which mainly fo- their practical derivatives, are no longer usable, as no “clas- cuses on the usability requirements more than on the com- sical” hierarchy between components can be generally pliance of the implementation against some specification. identified. Some other criteria for integration testing imply The intent is hence to verify that the effort required from integrating the software components based on identified end-users to learn to use and fully exploit the system func- functional threads[34]. In this case the test is focused on tionalities is acceptable. those classes used in reply to a particular input or system event (thread-based testing) [34]; or by testing together 4.4 Regression Test those classes that contribute to a particular use of the sys- Properly speaking, regression test is not a separate level of tem. testing (we listed it in fact among test objectives in Section Finally, some authors have used the dependency structure 3.3. ), but may refer to the retesting of a unit, a combination between classes as a reference structure for guiding integra- of components or a whole system (see Fig. 1 below) after tion testing, i.e., their static dependencies [40], or even the modification, in order to ascertain that the change has not dynamic relations of inheritance and polymorphism [41]. introduced new faults [51]. Such proposals are interesting when the number of classes is not too big; however, test planning in those approaches can begin only at a mature stage of design, when the classes System Test and their relationships are already stable. Unit Test Integration Test A different branch of the literature is testing based on the Acceptance Test Software Architecture: this specifies the high level, formal specification of a system structure in components and their connectors, as well as the system dynamics. The way in which the description of the Software Architecture could be Regression Test used to drive the integration test plan is currently under Fig. 1. Logical schema of software testing levels investigation, e.g., [45]. 6

As software produced today is constantly in evolution, always detect the same failures: in practice, the assumption driven by market forces and technology advances, regres- is rarely satisfied, and different set of test cases fulfilling a sion testing takes by far the predominant portion of testing same criterion may show varying effectiveness depending effort in industry. on how the test cases are picked within each subdomain. Since both corrective and evolutive modifications may be Many are the factors of relevance when a test selection cri- performed quite often, to re-run after each change all pre- terion has to be chosen. An important point to always keep viously executed test cases would be prohibitively expen- in mind is that what makes a test a “good” one does not sive. Therefore various types of techniques have been de- have a unique answer, but changes depending on the con- veloped to reduce regression testing costs and to make it text, on the specific application, and on the goal for testing. more effective. The most common interpretation for “good” would be Selective regression test techniques [53] help in selecting a “able to detect many failures”; but again precision would (minimized) subset of the existing test cases by examining require to specify what kind of failures, as it is well known the modifications (for instance at code level, using control and experimentally observed that different test criteria flow and data flow analysis). Other approaches instead trigger different types of faults [5], 0. Therefore, it is always prioritize the test cases according to some specified criterion preferable to spend the test budget to apply a combination (for instance maximizing the fault detection power or the of diverse techniques than concentrating it on just one, even structural coverage), so that the test cases judged the most if shown the most effective. effective with regard to the adopted criterion can be taken Paradoxically, test case selection seems to be the least inter- first, up to the available budget. esting problem for test practitioners. A demonstration of this low interest is the paucity of commercial automated 5. STRATEGIES FOR TEST CASE SELECTION tools for helping test selection and test input generation, in Effective testing requires strategies to trade-off between the comparison with a profusion of support tools (see Section two opposing needs of amplifying testing thoroughness on 7.3) for handling test execution and re-execution (or regres- one side (for which a high number of test cases would be sion test) and for test documentation. The most practiced desirable) and reducing times and costs on the other (for test selection criterion in industry probably is still tester's which the fewer the test cases the better). Given that test intuition, and indeed expert testers may perform as very resources are limited, how the test cases are selected be- good selection “mechanisms” (with the necessary warnings comes of crucial importance. Indeed, the problem of test against exclusively relying on such a subjective strategy). cases selection has been the largely dominating topic in Empirical investigations [5] showed in fact that tester's skill software testing research to the extent that in the literature is the factor that mostly affect test effectiveness in finding “software testing” is often taken as a synonymous for “test failures. case selection”. 5.1 Selection Criteria Based on Code A decision procedure for selecting the test cases is provided Code-based testing, also said “structural testing”, or “white by a test criterion. box” testing, has been the dominating trend in software A basic criterion is random testing, according to which the testing research during the late 70's and the 80's. One rea- test inputs are picked purely randomly from the whole in- son is certainly that in those years in which formal ap- put domain according to a specified distribution, i.e., after proaches to specification were much less mature and pur- assigning to the inputs different “weights” (more properly sued than now, the only RM formalized enough to allow probabilities ). For instance the uniform distribution does not for the automation of test selection or for a quantitative make any distinction among the inputs, and any input has measurement of thoroughness was the code. the same probability of being chosen. Under the operational Referring to the fault-error-failure chain described in Sec- distribution, instead, inputs are weighted according to their tion 2.3, the motivation to code-based testing is that poten- probability of usage in operation (as we already said in Sec- tial failures can only be detected if the parts of code related tion 2.4). to the causing faults are executed. Hence, by monitoring In contrast with random testing is a broad class of test crite- code coverage one tries to exercise thoroughly all “program ria referred to as partition testing. The underlying idea is elements”: depending on how the program elements to be that the program input domain is divided into subdomains covered are identified several test criteria exist. within which it is assumed that the program behaves the In structural testing, the program is modelled as a graph, same, i.e., for every point within a subdomain the program whose entry-exit paths represent the flow of control, hence either succeeds or fails: we also call this the “test hypothe- it is called a flowgraph. Finding a set of flowgraph paths sis”. Therefore, thanks to this assumption only one or few fulfilling a coverage criterion thus becomes a matter of points within each subdomain need to be checked, and this properly visiting the graph (see for instance [11]). Code is what allows for getting a finite set of tests out of the infi- coverage criteria are also referred to as path-based test cri- nite domain. Hence a partition testing criterion essentially teria, because they map each test input to a unique path p provides a way to derive the subdomains. on the flowgraph. A test criterion yielding the assumption that all test cases The ideal and yet unreachable target of code-based testing within a subdomain either succeed or fail is only an ideal, would be the exhaustive coverage of all possible paths and would guarantee that any fulfilling test set of test cases along the program control-flow. The underlying test hy- 7 pothesis here is that by executing a path once, potential A final warning is worth that “exercised” and “tested” are faults related to it will be revealed, i.e., it is assumed that not synonymous: an element is really tested only when its every input executing a same path will either fail or suc- execution produces an effect on the output; in view of this ceed (which is not necessarily true, of course). statement, under most existing code-based criteria even Full path coverage is not applicable, because banally every 100% coverage could leave some statement untested. program with unbounded loops would yield an infinite number of paths. Even limiting the number of iterations 5.2 Selection Criteria Based on Specifications within program loops, which is the usually practised tactic In specification-based testing, the reference model RM is in testing, the number of tests would remain infeasibly derived in general from the documentation relative to pro- high. Therefore, all the proposed code-based criteria at- gram specifications. Depending on how the latter are ex- tempt to realize cost/effective approximations to path cov- pressed, largely different techniques are possible [34]. Early erage, by identifying specific (control-flow or data-flow) approaches [46] looked at the Input/Output relation of the elements of a program that are deemed to be relevant for program seen as a “black-box” and manually derived: revealing possible failures, and by requiring that enough · equivalence classes: by partitioning the input domain test cases to cover all such elements be executed. into subdomains of “equivalent” inputs, in the sense The landmark paper in code-based testing is [52], in which explained in Section 5 that any input within a subdo- a family of criteria was introduced, based on both control- main can be taken as a representative for the whole flow and data-flow. A subsumption hierarchy between the subset. Hence, each input condition must be separately criteria was derived, based on the inclusion relation such considered to first identify the equivalence classes. The that a test suite satisfying the subsuming criterion is guar- second step consists of choosing the test inputs repre- anteed to also satisfy the (transitively) subsumed criterion. sentative of each subdomain; it is good practice to take Statement coverage is the most elementary criterion, requir- both valid and invalid equivalence classes for each ing that each statement in a program be exercised at least conditions. The Category Partition method that we de- once. The already mentioned branch coverage criterion in- scribe below in this section belongs to this approach. stead requires that each branch in a program be exercised · boundary conditions: i.e., those combinations of values (in other words, for every predicate its evaluation to true that are “close” (actually on, above and beneath) the and false should both be tested at least once). Note that borders of the equivalence classes identified both in the complete statement coverage does not assure that all input and the output domains. This test approach is branches are exercised (empty branches would be left out). based on the intuitive fact, also proved by experience, Branch coverage is also said “decision coverage”, because it that faults are more likely to be found at the boundaries considers the outcome of a decision predicate. When a of the input and output subdomains. predicate is composed by the logical combination of several · cause-effect graphs: these are combinatorial logic net- conditions, a variation to branch coverage is given by “con- works that can be used to explore in systematic way dition coverage”, which requires instead to test the true and the possible combinations of input conditions. By ana- false outcome of the individual conditions of predicates. lysing the specification, the relevant input conditions, Further criteria consider together coverage of decisions and or causes, and the consequent transformations and out- conditions under differing assumptions (see, e.g., [25]). put conditions, the effects, are identified and modelled It must be kept in mind, however, that code-based test se- into graphs linking the effects to their causes. A de- lection is a tautology: it looks for potential problems in a tailed description of this early technique can be found program by using the program itself as a reference model. in [46]. In this way, for instance, faults of missing functionalities Approaches such as the ones described above all require a could never be found. degree of “creativity” [46]. To make testing more repeat- As a consequence, code-based criteria should be more able, lot of researchers have tried to automatize the deriva- properly used as adequacy criteria. In other terms, testers tion of test cases from formal or semiformal specifications. should take the measures of coverage reached by the exe- Early attempts included algebraic specifications [8], VDM cuted tests and the signaling of uncovered elements as a [21], and Z [26], while a more recent collection of ap- warning that the set of test cases are ignoring some parts proaches to formal testing can be found in [27]. (and which ones) of the functionalities or of the design. Also in specification based testing a graph model is often Coverage of unexercised elements should hence be taken as derived and some coverage criterion is applied on this an advice for more thought and not as the compelling test model. A number of methods rely on coverage of specifica- target. tions modelled as a Finite State Machine (FSM). A review of A sensible approach is to use another artifact as the refer- these approaches is given in [14]. Alternatively, confor- ence model from which the test cases are designed and mance testing can be based on Labelled Transition Systems monitor a measure of coverage while tests are executed, so (LTS) models. LTS-based testing has been the subject of to evaluate the thoroughness of the test suite. If some ele- extensive research [16] and a quite mature theory now ex- ments of the code remain uncovered, additional tests to ists. Given the LTS for the specification S and one of its pos- exercise them should be found, as it can be a signal that the sible implementations I (the program to be tested), various tests do not address some function that is coded. test generation algorithms have been proposed to produce sound test suites, i.e., such that programs passing the test 8 correspond to conformant implementations according to a modified version of the program under test, differing from defined “conformance relation”. An approach for the it by a small, syntactic change. Every test case exercises automatic, on-the-fly generation of test cases has been im- both the original and all generated mutants: If a test case is plemented in the Test Generation and Verification (TGV) successful in identifying the difference between the pro- [54] tool. gram and a mutant, the latter is said to be killed. The un- As expectable, specification-based testing nowadays fo- derlying assumption of mutation testing , the coupling ef- cuses on testing from UML models. A spectrum of ap- fect, is that, by looking for simple syntactic faults, more proaches has been and is being developed, ranging from complex, but real, faults will be found. For the technique to strictly formal testing approaches based on UML state- be effective, a high number of mutants must be automati- charts [43], to approaches trying to overcome UML limita- cally derived in a systematic way. tions requiring OCL (Object Constraint Language) [55] ad- ditional annotations [15], to pragmatic approaches using · Based on operational usage the design documentation as is and proposing automated In testing for reliability evaluation, the test environment support tools [4]. The recent tool Agedis [24] supports the must reproduce the operational environment of the soft- model-driven generation and execution of UML-based test ware as closely as possible (operational profile ) [34], [44], suites, built on the above mentioned TGV technology. [51]. The idea is to infer, from the observed test results, the future reliability of the software when in actual use. To do 5.3 Other Criteria this, inputs are assigned a probability distribution, or pro- Specification-based and code-based test techniques are of- file, according to their occurrence in actual operation. In ten contrasted as functional vs. structural testing. These two particular the Software Reliability Engineered Testing approaches to test selection are not to be seen as alternative, (SRET) [44] is a testing methodology encompassing the but rather as complementary; in fact, they use different whole development process, whereby testing is “designed sources of information, and have proved to highlight dif- and guided by reliability objectives and expected relative ferent kinds of problems. They should be used in combina- usage and criticality of different functions in the field.” tion, depending on budgetary considerations [34]. More- over, beyond code or specifications, the derivation of test 6. TEST DESIGN cases can be done starting from other informative sources. Some other important strategies for test selection are briefly We have seen that there exist various test objectives, many overviewed below. test selection strategies and differing stages of the lifecycle of a product at which testing can be applied. Before actually · Based on tester’s intuition and experience commencing any test derivation and execution, all these As said, one of the most widely practiced technique based aspects must be organized into a coherent framework. In- on the tester intuition and experience is ad-hoc testing [36] deed, software testing itself consists of a compound proc- techniques in which tests are derived relying on the tester’s ess, for which different models can be adopted. skill, intuition, and experience with similar programs. Ad A traditional test process includes subsequent phases, hoc testing might be useful for identifying special tests, namely test planning, test design, test execution and test those not easily captured by formalized techniques. An- results evaluation. other emerging technology is Exploratory testing [37], which Test planning is the very first phase and outlines the scope is defined as simultaneous learning, test design, and test of testing activities, focusing in particular on the objectives, execution; that is, the tests are not defined in advance in an resources and schedule, i.e., it covers more the managerial established test plan, but are dynamically designed, exe- aspects of testing, rather than the detail of techniques and cuted, and modified. The effectiveness of exploratory test- the specific test cases. A test plan can be already prepared ing relies on the tester’s knowledge, which can be derived during the requirements specification phase. from various sources: observed product behavior during Test design is a crucial phase of software testing, in which testing, familiarity with the application, the platform, the the objectives and the features to be tested and the test failure process, the type of possible bugs, the risk associated suites associated to each of them are defined [7], [29], [30], with a particular product, and so on. [51]. Also the levels of test are planned. Then, it is decided what kind of approach will be adopted at each level and for · Fault-based each feature to be tested. This also includes deciding a With different degrees of formalization, fault-based testing stopping rule for testing. Due to time or budget constraints, techniques devise test cases specifically aimed at revealing at this point it can be decided that testing will concentrate categories of likely or pre-defined faults. In particular it is on some more critical parts. possible that the RM is given by expected or hypothesized An emerging and quite different practice for testing is test faults, such as in error guessing , or mutation testing. Spe- driven development, also called Test-First programming, cifically in error guessing [36] test cases are designed by which focuses on the derivation of (unit and acceptance) testers trying to figure out the most plausible faults in a tests before coding. This approach is a key practice of mod- given program. A good source of information is the history ern Agile development approaches such as Extreme Pro- of faults discovered in earlier projects, as well as the tester’s gramming (XP) and Rapid Application Development expertise. In Mutation testing [50], a mutant is a slightly (RAD) [6]. The leading principle of such approaches is to 9 make development more lightweight by keeping design the tester himself/herself, who can either inspect a poste- simple and reducing as much as possible the rules and the rior the test log, or even decide a priori, during test plan- activities of traditional processes felt by developers as ning, the conditions that make a test successful and code overwhelming and unproductive, for instance devoted to these conditions into the employed test driver. documentation, formalized communication, or ahead plan- When the tests cases are automatically derived, or also ning of rigid milestones. Therefore a traditional test design when their number is quite high, in the order of thousands, phase as described above does no longer exist, but new or millions, a manual log inspection or codification is not tests are continuously created, as opposed to a vision of thinkable. Automated oracles must then be implemented. designing test suites up front. In the XP way, the leading But, of course, if we had available a mechanism that knows principle is to “code a little, test a little, …” so that develop- in advance and infallibly the correct results, it would not be ers and customers can get immediate feedbacks. necessary to develop the system under test: we could use the oracle instead! Hence the need of approximate solu- tions. 7. TEST EXECUTION Different approaches can be taken [2]: assertions could be Executing the test cases specified in test design may entail embedded into the program so to provide run-time check- various difficulties. Below we discuss the various activities ing capability; conditions expressly specified to be used as implied in launching the tests, and deciding the test out- test oracles could be developed, in contrast with using the come. We also hint at tools for automating testing activities. same specifications (i.e., written to model the system behav- 7.1 Launching the tests ior and not for run-time checking); the produced execution traces could be logged and analyzed. Forcing the execution of the test cases (manually or auto- In some cases, the oracle can be an earlier version of the matically) derived according to one of the criteria presented system that we are going to replace with the one under test. in Section 5 might not be so obvious. A particular instance of this situation is regression testing, If a code-based criterion is followed, it provides us with in which the test outcome is compared with earlier version entry-exit paths over the flowgraph that must be taken, and executions (which however in turn had to be judged passed test inputs that execute the corresponding program paths or failed). Generally speaking, an oracle is derived from a need be found. Actually, as already said, code-based should specification of the expected behavior. Thus, in principle, be better used as an adequacy criterion, hence in principle automated derivation of test cases from specifications have we should not look for inputs ad hoc to execute the not the advantage that by this same task we get an abstract ora- covered entities, but rather use the coverage analysis results cle specification as well. However, the gap between the ab- to understand the weaknesses in the executed test cases. stract level of specifications and the concrete level of exe- However, in the cycle of testing, monitoring unexecuted cuted tests only allows for partial oracles implementations, elements, finding additional test cases, often conducted i.e., only necessary (but not sufficient) conditions for cor- under pressure, finding those test cases that increase cover- rectness can be derived. age can be very difficult. In view of these considerations, it should be evident that If a specification-based criterion is adopted, the test cases the oracle might not always judge correctly. So the notion correspond to sequences of events, which are specified at of coverage2 of an oracle is introduced to measure its accu- the abstraction level of the specifications; more precisely, racy. It could be measured for instance by the probability they are labels within the signature of the adopted specifi- that the oracle rejects a test (on an input chosen at random cation language. To derive concrete test cases, these labels from a given probability distribution of inputs), given that must be translated into corresponding labels at code level it should reject it [12], whereby a perfect oracle exhibits a (e.g., method invocations), and eventually into execution 100% coverage, while a less than perfect oracle may yield statements to be launched on the User Interface of the used different measures of accuracy. test tool. 7.3 Test Tools 7.2 Test Oracles Testing requires fulfilling many labor-intensive tasks, run- An important component of testing is the oracle. Indeed, a ning numerous executions, and handling a great amount of test is meaningful only if it is possible to decide about its information. The usage of appropriate tools can therefore outcome. The difficulties inherent to this task, often over- alleviate the burden of clerical, tedious operations, and simplified, had been early articulated in [57]. make them less error-prone, while increasing testing effi- Ideally, an oracle is any (human or mechanical) agent that ciency and effectiveness. Reference [33] lists suitable char- decides whether the program behaved correctly on a given acteristics for testing tools used for verification and valida- test. The oracle is specified to output a reject verdict if it tion. In the following of this section we present a repertoire observes a failure (or even an error, for smarter oracles), of typologies of most commonly used test tools, and refer and approve otherwise. Not always the oracle can reach a to[7], [33], [44], [50], [51] for a more complete survey. decision: in these cases the test output is classified as incon- clusive. In a scenario in which a limited number of test cases is exe- cuted, sometimes even derived manually, the oracle can be 2 It is just an unfortunate coincidence the usage with a quite different mean- ing of the same term adopted for test criteria. 10

· Test harness (drivers, stubs): provides a controlled envi- Test Case Specification ronment in which tests can be launched and the test Test case ID The unique identifier associated with the test case outputs can be logged. In order to execute parts of a Test items and purpose The items and features exercised system, drivers and stubs are provided to simulate Input data The explicit list of the inputs required for caller and called modules, respectively; executing the test case (values, files database etc) Test case behaviour Description of the expected test case behaviour · Test generators: provide assistance in the generation of Output data The list of the outputs admitted for each feature involved tests. The generation can be random, pathwise (based in the test case, possibly associated with tolerance values on the flowgraph) or functional (based on the formal Environmental set-up The hardware/software configurations required specifications); Specific procedural reqs The constraints and the special procedures required. · Capture/Replay: this tool automatically re-executes, or Test cases dependencies The IDs of the test cases that must be executed prior replays, previously run tests, of which it recorded in- this test case puts and outputs (e.g., screens). · Oracle/file comparators/assertion checking: these kinds of Fig. 2. Scheme of a possible test case tools assist in deciding whether a test outcome is suc- cessful or faulty; Test Procedure Specification: specifies the steps and the · Coverage analyzer/Instrumenter: a coverage analyzer as- special requirements that are necessary for executing a set sesses which and how many entities of the program of test case. flowgraph have been exercised amongst all those re- Test Log: documents the result of a test execution, includ- quired by the selected coverage testing criterion. The ing: the occurred failures (if any); the information needed analysis can be done thanks to program instrumenters, for reproducing them and locating and fixing the that insert probes into the code. corresponding faults; the information necessary for · Tracers: trace the history of execution of a program; establishing whether the project is complete; any · Reliability evaluation tools: support test results analysis anomalous events. See a summary in Fig. 3. and graphical visualization in order to assess reliability Test Incident or Problem Report: provides a description of related measures according to selected models. the incidents including inputs, expected and obtained re- sults, anomalies, date and time, procedure steps, environ- 8. TEST DOCUMENTATION ment, attempts to repeat the tests, observations and refer- ence to the test case and procedure specification and test Documentation is an integral part of the formalization of log. the test process, which contributes to the coordination and control of the testing phase. Several types of documents- may be associated to the testing activities [51], [29]: Test Test Log Plan, Test Design Specification, Test Case Specification, Test Test log ID The unique identifier associated with the test log Procedure Specification, Test Log, and Test Incident or Items tested Details of the items tested including environmental attributes Problem Report. We outline a brief description of each of Events the list of the events occurred including: them, referring to IEEE Standard for Software Test Docu- the start and end date and time of each event mentation [29] for a complete description of test documents ID of the test procedures executed and of their relationship with one another and with the test- personnel who executed the procedures ing process. description of test procedures results Test Plan: defines test items, features to be or not to be environmental details tested, approach to be followed (activities, techniques and Description of the anomalous events occurred tool to be used), pass/fail criteria, the delivered documents, task to be performed during the testing phase, environ- Fig. 3. Scheme of a possible test log mental needs, (hardware, communication and software facilities), people and staff responsible for managing de- signing, preparing, executing the tasks, staffing needs, schedule (including milestones, estimation of time required 9. TEST MANAGEMENT to do each task, period of use of each testing resources). The management processes for software development con- Test Design Specification: describes the features to be cern different activities mainly summarized into [32]: initia- tested and their associated test set. tion and scope definition, planning, execution and control, Test Case Specification: defines the input/output required review and evaluation, closure. These activities also con- for executing and a test case as well as any special con- cern the management of the test process even though with straints or intercase dependencies. A skeleton is depicted in some specific characterizations. Fig. 2. In the testing phase in fact a very important component of successful testing is a collaborative attitude towards testing and quality assurance activities. Managers have a key role in fostering a generally favorable reception towards failure discovery during development; for instance, by preventing 11 a mindset of code ownership among programmers, so that measures may cover such aspects as: number of test they will not feel responsible for failures revealed by their cases specified, number of test cases executed, code. Moreover the testing phases could be guided by vari- number of test cases passed, and number of test ous aims, for example: in risk-based testing, which uses the cases failed, among others. Evaluation of test prob- product risks to prioritize and focus the test strategy; or in lem reports can be combined with root-cause scenario-based testing, in which test cases are defined analysis to evaluate test process effectiveness in based on spec ified system scenarios. finding faults as early as possible. Such an evalua- Test management can be conducted at different levels tion could be associated with the analysis of risks. therefore it must be organized, together with people, tools, Moreover, the resources that are worth spending policies, and measurements, into a well-defined process on testing should be commensurate with the which is an integral part to the life cycle3. use/criticality of the application: specifically a de- In the testing context the main manager’s activities can be cision must be made as to how much testing is summarized as [[7], [36], [50], [51]: enough and when a test stage can be terminated. · Scheduling the timely completion of tasks Thoroughness measures, such as achieved code · Estimation of the effort and the resources needed to coverage or functional completeness, as well as es- execute the tasks: An important task in test plan- timates of fault density or of operational reliability, ning is the estimation of resources required which provide useful support, but are not sufficient in means organizing not only hardware and software themselves. The decision also involves considera- tools but also people. Thus the formalization of the tions about the costs and risks incurred by potential test process also requires putting together a test for remaining failures, as opposed to the costs im- team, which can involve internal as well as external plied by continuing to test. We detail better this staff members. The decision will be determined by topic in the next section. consideration of costs, schedule, maturity level of the involved organization and the criticality of the 10. TEST MEASUREMENTS application. · Quantification of the risk associated with the tasks Measurements are nowadays applied in every scientific · Effort/Cost estimation: The testing phase is a criti- field for quantitatively evaluating parameters of interest, cal step in process development, often responsible understanding the effectiveness of techniques or tools, the for the high costs and effort required for product productivity of development activities (such as testing or release. The effort can be evaluated for example in configuration management), the quality of products, and terms of person-days, months or years necessary more. In particular, in the software engineering context for the realization of each project. For cost estima- they are used for generating quantitative descriptions of tion it is possible to use two kinds of models: static key processes and products, and consequently controlling and dynamic multivariate models. The former use software behavior and results. But these are not the only historical data to derive empirical relationships, the reasons for using measurement; it can permit definition of a latter project resource requirements as a function of baseline for understanding the nature and impact of pro- time. In particular, these test measures can be re- posed changes. Moreover, as seen in the previous section, lated to the number of tests executed or the number measurement allows managers and developers to monitor of tests failed. Finally to carry out testing or main- the effects of activities and changes on all aspects of devel- tenance in an organized and cost/effective way, the opment. In this way actions to check whether the final out- means used to test each part of the system should come differs significantly from plans can be taken as early be reused systematically. This repository of test ma- as possible[23]. terials must be configuration-controlled, so that We have already hinted at useful test measures throughout changes to system requirements or design can be the chapter. It can be useful to briefly summarize them al- reflected in changes to the scope of the tests con- together. Considering the testing phase, measurement can ducted. The test solutions adopted for testing some be applied to evaluate the program under test, or the se- application types under certain circumstances, with lected test set, or even for monitoring the testing process the motivations behind the decisions taken, form a itself [9]. test pattern which can itself be documented for 10.1 Evaluation of the Program Under Test later reuse in similar projects. For evaluating the program under test the following meas- · Quality control measures to be employed: several urements can be applied: measures relative to the resources spent on testing, Program measurement to aid in test planning and design: con- as well as to the relative fault-finding effectiveness sidering the program under test, three different categories of the various test phases, are used by managers to of measurement can be applied as reported in [7]: control and improve the test process. These test · Linguistic measures: these are based on proprieties of the program or of the specification text. This category 3 In [32], testing is not described as a stand-alone process, but principles for testing activities are included along with both the five primary life cycle includes for instance the measurement of: Sources processes, and the supporting process. In [31], testing is grouped with other evaluation activities as integral to development throughout the lifecycle. 12

Lines of Code (LOC), the statements, the number of gram behavior, than we can make a statistical prediction of unique operands or operators, and the function points. what would happen for the next tests, should we continue · Structural measures: these are based on structural rela- to use the program in the same way. This reasoning is at the tions between objects in the program and comprise basis of software reliability. control flow or data flow complexity. These can include Documentation and analysis of test results require disc i- measurements relative to the structuring of program pline and effort, but form an important resource of a com- modules, e.g., in terms of the frequency with which pany for product maintenance and for improving future modules call each other. projects. · Hybrid measures: these may result from the combina- tion of structural and linguistic properties. 11. CONCLUSIONS Fault density: This is a widely used measure in industrial contexts and foresees the counting of the discovered faults We have presented a comprehensive overview of software and their classification by their type. For each fault class, testing concepts, techniques and processes. In compiling fault density is measured by the ratio between the number the survey we have tried to be comprehensive to the best of of faults found and the size of the program [50].. our knowledge, as matured in years of research and study Life testing, reliability evaluation: By applying the operational of this fascinating topic The approaches overviewed in- testing for a specific product it is possible either to evaluate clude more traditional techniques, e.g., code-based criteria, its reliability and decide if testing can be stopped or to as well as more modern ones, such as model checking or achieve an established level of reliability. In particular Reli- the recent XP approach. ability Growth models can be used for predicting the prod- Two are the main contributions we intended to offer to the uct reliability[44]. readers: on one side, by putting into a coherent framework all the many topics and tasks concerning the software test- 10.2 Evaluation of the Test Performed ing discipline, we hope to have demonstrated that software For evaluating the set of test cases implemented the follow- testing is a very complex activity deserving a first-class role ing measures can be applied: in software development, in terms of both resources and Coverage/thoroughness measure: Some adequacy criteria re- intellectual requirements. On the other side, by hinting at quire exercising a set of elements identified in the program relevant issues and open questions, we hope to attract fur- or in the specification by testing. ther interest from academy and industry in contributing to Effectiveness: In general a notion of effectiveness must be as- evolve the state of the art on the many still remaining open sociated with a test case or an entire test suite, but test effec- issues. tiveness does not yield a universal interpretation. In the years, software testing has evolved from an “art” [46] to an engineering discipline, as the standards, techniques 10.3 Measures for monitoring the testing process and tools cited throughout the chapter demonstrate. How- We have already mentioned that one intuitive and diffuse ever test practice inherently still remains a trial-and-error practice is to count the number of failures or faults de- methodology. We will never find a test approach that is tected. The test criterion that found the highest number guaranteed to deliver a “perfect” product, whichever is the could be deemed the most useful. Even this measure has effort we employ. However, what we can and must pursue drawbacks: as tests are gathered and more and more faults is to transform testing from “trial-and-error” to a system- are removed, what can we infer about the resulting quality atic, cost-effective and predictable engineering discipline. of the tested program? for instance, if we continue testing and no new faults are found for a while, what does this REFERENCES imply? that the program is “correct”, or that the tests are ineffective? [1] T. Ball, “The concept of dynamic analysis”, Proc.of joint 7th It is possible that several different failures are caused by a ESEC/7th AC M FSE, Toulouse, France, vol.24, no. 6, October 1999, single fault, as well as that a same failure is caused by dif- pp.: 216 – 234. ferent faults. What should be better estimated then in a pro- [2] L. 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An essay on software testing for quality assurance – Editor’s introduction

Published: January 1997
Volume 4 , pages 1–9, ( 1997 )

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Dick Hamlet 1

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This volume resulted from a call for papers to “... explore the state of the art of software quality assurance, with particular emphasis on testing to measure quality.” It is my belief that software testing as a discipline is ripe for theoretical breakthroughs. Researchers are considering the right questions, and there are promising new approaches and exciting new results. It seems that new understanding of the testing process can lead to practical tools and techniques that revolutionize software development. I don’t believe that testing will become easier or cheaper; rather, it will be more rational in the sense that expending effort will more dependably lead to better software. In this introductory essay I provide a personal view of testing, testing research, and their roles in software quality assurance.

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Hamlet, D. An essay on software testing for quality assurance – Editor’s introduction. Annals of Software Engineering 4 , 1–9 (1997). https://doi.org/10.1023/A:1018906509232

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The software testing process consists of seven steps: test plan creation, analysis of requirements, design of test cases, development of test scripts, execution of tests, bug fixes, and the last step is test completion which ensures all bugs are fixed and test summary reports are generated.

What are the 7 phases of SDLC?

The 7 phases of the Software Development Life Cycle (SDLC) include planning, requirement gathering, design, implementation (coding), testing, deployment, and maintenance. This approach guides the entire software development process, from initial project planning to ongoing support and improvement, ensuring efficient and high-quality software delivery.

What is STLC?

STLC stands for Software Testing Life Cycle, a structured software testing approach. It comprises various phases, including requirement analysis, test planning, test design, test execution, defect reporting and tracking, and test closure. STLC ensures that software testing is carried out efficiently, comprehensively, and in alignment with project goals, leading to higher-quality software products.

What is the SDLC and STLC?

The Software Development Life Cycle (SDLC) is a set of activities throughout the software development process. The Software Testing Life Cycle (STLC) is a set of actions throughout the software testing process.

What is entry and exit criteria?

The terms entry and exit criteria are commonly used in research and development but can be used in any sector. Benefits include ensuring that the process meets particular entry and exit criteria before moving on to the next level, including the last level before completion.

What does SDLC stand for?

SDLC stands for Software Development Life Cycle. It is a structured process that guides software development from inception to deployment.

What is the SDLC process?

The Software Development Life Cycle (SDLC) is a systematic approach used to develop software. It involves several stages, including requirements gathering, design, coding, testing, deployment, and maintenance. Each phase has specific activities and deliverables, ensuring a structured and efficient development process.

What is the design phase in the SDLC quizlet?

The design phase in the SDLC (Software Development Life Cycle) refers to the stage where the system’s architecture and specifications are planned and documented. It involves creating detailed technical designs and determining the best solution to meet the project’s requirements.

What is SDLC and its types?

The Software Development Life Cycle (SDLC) is a structured approach to developing software. It comprises various phases such as requirements gathering, design, development, testing, deployment, and maintenance. SDLC types include Waterfall, Agile, and DevOps, each with its own unique characteristics and methodologies.

Why is SDLC important?

The SDLC, or Software Development Life Cycle, is crucial as it provides a structured approach to developing high-quality software. It ensures effective project management, thorough requirements gathering, proper testing, and timely delivery, improving productivity, reduced costs, and customer satisfaction.

What is STLC in testing?

STLC, or Software Testing Life Cycle, is a series of testing activities conducted by a testing team to ensure software quality. It’s an integral part of the Software Development Life Cycle (SDLC) and encompasses diverse steps to verify and validate software for a successful release.

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Essay on Software Testing

This article provides the number and types of tests required to achieve high test coverage of the requirements outlined in the following sections for granting credit limit access to customers. The software under test belonged to WonderCard Ultd. Credit card operations division after updating the company’s business rules governing the awarding credit limit access procedure. The test sets outlined in this article represent the preliminary test cases inferred from the requirements gathered by the testing team before the software solution implementation. Therefore, the criteria used to identify these test cases focused on covering all the conformance and fault-directed testing options available to the testing group. The following section outlines the number and types of test sets identified based on the system requirements provided by the company and their justification over other approaches.

The testing approach in this project broke down the test requirements into test sets based on the testing design options, such as conformance-based test designs that provide the conformance test set and fault-directed tests that provide the boundaries and scope test sets for the data requirements. The selected test sets included the test set for determining credit limit increase-worthiness of a customer based on the new operational needs of the company, the business rules test set, and the data validity test set. Each of these test sets contains several test cases covering specific areas in the scopes outlined by the boundaries of the test sets. For example, the conformance-oriented test set for determining if the provides the required outputs for customers based on the criteria outlined in the new algorithm only consists of tests that cover a wide array of possible customer states as inputs. The business rules take different organizational states as inputs and tests for potential failures in the system, given invalid assumptions concerning the new business rules. The final test set developed for this project consisted of cases that determined whether the software held up under varying data conditions. Clearly, the software would require more than three test cases, but this criterion provided a sufficient starting stage for the process.

The choice to divide the testing protocol into test sets that contain multiple test cases was based on the approach’s ability to abstract complex test details involved in test design away from the protocol design to make the problem smaller and more manageable (González, 2015). An integrated test set that encompasses all test cases would force the testing team to lump all the diverse test details for multiple test cases in the same repository, introducing various points of failure in the testing process. Specific test cases divided along test design approaches available to the testers divided the problem into categorical sub-problems that could be analyzed individually to produce more coverage of the requirements. Therefore, this approach encourages the systematic processing of information at each testing stage (González, 2015), reducing the probability of leaving out crucial conformance requirements or conditional defects in the software. A testing model for each test case could help test the intended behavior of the system in the dimension under focus during subsequent stages in the software implementation process (Felderer & Herrmann, 2019). The following section outlines the test cases selected under each test set based on the functional requirements provided by the organization for the new system.

Conformance-oriented Test Set

The test set for determining how well the system conforms to the highlighted requirements will include test cases for deciding when customers do not qualify for credit limit increases and when they are eligible for ten, fifteen, or twenty percent credit limit increases. These four test cases would ensure that the organization always captures the correct customer status and produces the expected outcome given that they fulfill the suitable condition. The choice to include only four conformance-oriented test cases was to capture all the abstract details encoded in the requirements in the form of test case designs. Once the software product passed all test cases in this test set, the developers would have confidence it captured all the functional requirements described by the management to a satisfactory extent. The conformance-oriented test cases could also act as test sets, providing an avenue for further analysis of the system’s behavior under various functional states.

Fault-Oriented Test Cases

The other test cases focus on identifying possible faults that could make the system behave unexpectedly, such as not adhering to business rules or failing to account for various user data. Therefore, the test cases included these two broad approaches to fault-oriented software testing. The business rules test set included all test cases that sought to identify faults in the system’s implementation of the rules specified. These test cases were the test to determine if the software operates on the expected inputs and handles unexpected inputs correctly and whether the business rules exclude any customer demographic. The two test cases above support the system’s business logic and ensure it conforms to the outlined requirements by mitigating faults in the business logic.

On the other hand, the data validation tests sought to identify possible areas of failure in the data provided or produced by the system, such as invalid input types, invalid inputs in terms of relevance and currency, errors in data analysis and manipulation, and logical errors in the flow of data through the system. Test cases for output data included the test for errors in data samples with customer information based on random distribution. These five test cases could be split further into more granular test cases as the system’s data requirements become more apparent. This choice of test cases made it clear that the testing process would follow a systematic design, providing a clear pathway to implementation.

In conclusion, this software testing project would follow a systematic approach involving breaking down the test process into test sets that contain various test cases in specific domains. The test sets include conformance-oriented and fault-driven test design approaches and helped divide the problem into smaller groups that we solve separately (divide and conquer). After determining the test sets required for the project, this article outlined the number and type of test cases in each to provide a top-down view of the entire testing process.

Felderer, M., & Herrmann, A. (2019). Comprehensibility of system models during test design: a controlled experiment comparing UML activity diagrams and state machines. Software Quality Journal , 27 (1), 125-147.

González, M. R. (2015). Computational thinking test: Design guidelines and content validation. In EDULEARN15 Proceedings (pp. 2436-2444). IATED.

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Agile methodologies have become the preferred choice for modern software development. These methods focus on iterative and incremental development, where both requirements and solutions develop through collaboration among cross-functional software development teams. The success of a software system is based on the quality result of each stage of development with proper test practice. A software test ontology should represent the required software test knowledge in the context of the software tester. Reusing test cases is an effective way to improve the testing of software. The workload of a software tester for test-case generation can be improved, previous software testing experience can be shared, and test efficiency can be increased by automating software testing. In this chapter, the authors introduce a software testing framework (STF) that uses rule-based reasoning (RBR), case-based reasoning (CBR), and ontology-based semantic similarity assessment to retrieve the test cases from the case library. Finally, experimental results are used to illustrate some of the features of the framework.

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Software Testing Costs: Strategies for Efficiency and Optimization

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Thinking of launching your app or software in the market? While most development teams want to get the project up and running quickly, there is a minor hurdle.

Has the software or app been tested in multiple environments, business processes, and platforms? Does it work effectively in all possible scenarios?

If not, this minor lapse could cost the company a fortune. As a matter of fact, the 2022 Cost of Poor Software Quality Report found that in the US it was estimated at $2.41 trillion.

Several software testing processes can be implemented to ensure your software works optimally in all situations. While there are different software quality metrics that are vital to track, another important KPI is cost estimation in software testing. The ideal balance is where the company gets the best software quality while ensuring testing costs are within budget.

So, what is the average cost of software testing? This blog will delve into the details, explaining the factors that add to testing costs and the practical software testing cost-saving techniques that can help you achieve your goals.

Common Challenges in Testing Costs

The first step to minimizing software testing costs is understanding several factors and common challenges that add to your overall testing budget. These challenges can often include:

Scope Creep and Unclear Requirements

One of the most significant challenges in controlling testing costs is scope creep, which occurs when project requirements change or expand during development. Unclear or evolving requirements can lead to increased testing efforts, as additional test cases must be created and executed to accommodate new features or changes.

High dependency on Manual Testing

While automated testing can be a powerful tool for reducing costs, many organizations still rely heavily on manual testing. Overdependence on manual testing, especially for certain test types that are highly suitable for automation like regression testing, can often be time-consuming and lead to errors. The more time your testing and development teams have to spend on testing, the more it will add to your overall costs.

Delays or Late Discovery of Errors

A slight delay in identifying defects or in the development cycle is okay, but uncovering the cost of delay is crucial for managing testing costs. The later a severe defect is discovered, the more expensive it becomes to fix, as it may require significant rework and already damage the end-users’ experience.

Strategies for Cost Efficiency in Testing

Now that we know some of the common challenges that add to software testing costs, let’s understand how to reduce the cost of software testing. By implementing the following strategies, organizations can optimize their testing processes and reduce expenses.

Prioritize Risk-Based Testing

Risk-based testing focuses on identifying and testing the most critical areas of your software, allowing you to allocate resources where they will have the most significant impact. By analyzing potential risks—such as those associated with high-impact features, frequently used functionalities, or based on areas where in previous testing cycles critical bugs were found, you can prioritize test cases that address these risks.

This approach helps minimize the chances of costly defects slipping through while reducing the overall number of test cases you execute in a certain release to only those who matter the most, eventually lowering testing costs.

Implement Test Automation Strategically

While replacing 100% of manual testing with automation is impossible, you can take a strategic approach to test automation.

Begin by automating repetitive, time-consuming, and high-risk test cases executed frequently, such as regression and smoke tests. Over time, expand automation to other areas, but avoid automating tests that require a high degree of human judgment or are unlikely to be reused.

You can balance the initial investment with long-term savings and efficiency by carefully selecting which tests to automate.

Leverage Continuous Integration & Testing

Integrating testing into your continuous integration (CI) pipeline allows for early and frequent testing, catching defects as soon as they are introduced. This approach not only improves the overall quality of the software but also prevents the costly rework associated with late defect discovery.

Outsource When Appropriate

Outsourcing certain testing activities can be cost-effective, particularly for specialized testing tasks such as security or performance testing. External testing providers often have access to advanced tools and expertise that may not be available in-house, enabling you to achieve high-quality results at a lower cost.

Tools and Technologies for Cost-Effective Testing

To improve the testing process and minimize its costs, you can even choose software testing tools and automation methods to help you achieve efficiency and speed without compromising quality. Some of the test tools include:

Test Automation Tools

Test automation tools are essential for reducing manual testing efforts and speeding up the testing process. Popular tools like Selenium, JUnit, and TestNG offer robust automation capabilities, while some of them are even open-source, making them cost-effective choices. These tools allow teams to automate repetitive and time-consuming test cases, such as regression and unit tests, ensuring faster and more consistent results.

Continuous Integration (CI) Tools

Continuous integration tools like Jenkins, Bamboo, and GitLab CI enable teams to integrate code changes frequently and run automated tests continuously.

Performance Testing Tools

Performance testing is critical for ensuring software can handle expected load conditions without compromising speed or stability. Tools like JMeter, Gatling, and LoadRunner offer comprehensive performance testing capabilities , helping teams identify bottlenecks and optimize system performance.

Test Management Tools

In addition to automation, you need to optimize your testing efforts by reducing redundancy and providing a variety of test cases to help improve software quality.

Test case management tools like PractiTest enable teams to organize, track, and execute test cases systematically. These tools offer end-to-end traceability between tests, issues, and requirements, ensuring that nothing slips through the cracks. Additionally, they provide comprehensive reporting capabilities and integrate seamlessly with other testing tools, ensuring a unified workflow and better collaboration across different stages of the testing process.

Measuring and Monitoring Testing Costs

To accurately measure and monitor software testing costs, you need clear visibility in your organization’s testing protocols. These include:

Know the Metrics

Establish clear metrics that you will be tracking to measure the success of your testing processes, such as defect detection efficiency, time taken to execute a test case, defect resolution time, or others.

Track Costs Throughout the Testing Lifecycle

It’s important to track costs at each stage to understand where your budget is being spent. This includes:

Planning and Design: Costs associated with test planning, requirements analysis, and test case design.
Execution: Costs related to running test cases, including manual and automated testing efforts.
Defect Management: Costs incurred in identifying, reporting, and fixing defects during testing.
Reporting and Analysis: Costs associated with generating test reports, analyzing results, and refining testing strategies.

Implement Test Budgeting Methodologies

By implementing test budgeting methodologies, you must allocate resources to different testing activities. This enables you to track actual vs. planned expenditures and adjust budgets as needed. This empowers you to make informed decisions and control your testing costs.

To track and monitor your test budget, you can use a project management tool that tracks the time each resource spends on testing activities, helping you make informed decisions to automate the process.

Regularly Review and Adjust Budgets

Testing costs are not static; they can change throughout the development cycle due to factors like scope changes, unexpected defects, or resource availability. It’s essential to regularly review and adjust your testing budget to account for these changes.

Conducting periodic budget reviews allows teams to reallocate resources, prioritize high-impact areas, and avoid budget overruns.

Investments vs. Costs - How to avoid the costs of not investing in testing

While software testing involves upfront costs, the long-term savings and benefits far outweigh these initial investments. Failing to invest adequately in testing can lead to significant consequences, such as:

Poor Software Quality

One of the most significant risks of underinvesting in software testing is the potential for poor quality. When severe defects slip into production due to inadequate testing or not executing the right tests, they can lead to costly fixes and customer dissatisfaction with the software.

Addressing these issues after release is often much more expensive than thorough testing during development. Businesses may also face legal liabilities or regulatory fines if their software fails to meet industry standards.

Delayed Timelines

Software development projects are more likely to encounter delays without proper testing, as defects discovered late in the process require additional time to fix. These delays can push back release dates, leading to lost revenue opportunities and giving competitors a market advantage.

By investing in comprehensive testing early in the development cycle, organizations can identify and address issues sooner, ensuring a smoother path to market.

Poor Customer Experience

Releasing software with critical bugs or performance issues can directly impact customer satisfaction and retention. Users expect high-quality, reliable software, and failing to meet these expectations can result in negative reviews, increased support costs, and lost customers.

Thus, it is important to recognize that testing is not a cost to the company but an investment in your software. For example, investing in automation tools and continuous integration can reduce manual testing costs while improving efficiency.

By balancing costs with strategic investments, organizations can achieve optimal testing outcomes without overspending.

Effectively managing testing costs is crucial for businesses of all sizes. It allows your organization to optimize your testing process to achieve a delicate balance of quality and efficiency, helping you achieve your testing goals without exceeding the budget.

Here is a summary of the key strategies we have discussed:

Prioritize testing based on critical functionalities and high-risk areas.
Leverage test automation to improve efficiency and reduce costs.
Utilize appropriate tools and technologies to streamline testing processes.
Continuously measure and monitor testing metrics to identify areas for improvement.
Recognize the long-term benefits of investing in testing and avoid the costs associated with inadequate testing.

For additional savings on your testing processes, you can explore AI-powered software testing , which can help you leverage the right combination of human expertise with machine-learning automation.

PractiTest is a comprehensive test management platform that can empower your teams to use AI and streamline the testing process to deliver optimal results, all within the stipulated budget.

For more details on PractiTest’s test management platform, start your free trial or contact our team of experts today.

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Taming the chaos: how to manage testing in complex & robust environments, the 2024 state of testing™ report is now live, share this article.

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Principles of software testing – Software Testing

Types of Software Testing

1. Testing shows the Presence of Defects

2. Exhaustive Testing is not Possible

3. Early Testing

4. Defect Clustering

5. Pesticide Paradox

6. Testing is Context-Dependent

7. Absence of Errors Fallacy

1. Unit Testing

2. Integration Testing

3. Regression Testing

4. Smoke Testing

5. System Testing

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