Mutation Testing is a fault-based software testing technique that has been widely studied for over three decades The literature on Mutation Testing has contributed a set of approaches, tools, developments, and empirical results This paper provides a comprehensive analysis and survey of Mutation Testing The paper also presents the results of several development trend analyses These analyses provide evidence that Mutation Testing techniques and tools are reaching a state of maturity and applicability, while the topic of Mutation Testing itself is the subject of increasing interest

An Analysis and Survey of the Development of Mutation Testing

Objective measurement of test quality is one of the key issues in software testing. It has been a major research focus for the last two decades. Many test criteria have been proposed and studied for this purpose. Various kinds of rationales have been presented in support of one criterion or another. We survey the research work in this area. The notion of adequacy criteria is examined together with its role in software dynamic testing. A review of criteria classification is followed by a summary of the methods for comparison and assessment of criteria.

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Software unit test coverage and adequacy

Regression testing is a testing activity that is performed to provide confidence that changes do not harm the existing behaviour of the software. Test suites tend to grow in size as software evolves, often making it too costly to execute entire test suites. A number of different approaches have been studied to maximize the value of the accrued test suite: minimization, selection and prioritization. Test suite minimization seeks to eliminate redundant test cases in order to reduce the number of tests to run. Test case selection seeks to identify the test cases that are relevant to some set of recent changes. Test case prioritization seeks to order test cases in such a way that early fault detection is maximized. This paper surveys each area of minimization, selection and prioritization technique and discusses open problems and potential directions for future research. Copyright © 2010 John Wiley & Sons, Ltd.

Regression testing minimization, selection and prioritization: a survey

Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal. Various goals are possible; one involves rate of fault detection, a measure of how quickly faults are detected within the testing process. An improved rate of fault detection during testing can provide faster feedback on the system under test and let software engineers begin correcting faults earlier than might otherwise be possible. One application of prioritization techniques involves regression testing, the retesting of software following modifications; in this context, prioritization techniques can take advantage of information gathered about the previous execution of test cases to obtain test case orderings. We describe several techniques for using test execution information to prioritize test cases for regression testing, including: 1) techniques that order test cases based on their total coverage of code components; 2) techniques that order test cases based on their coverage of code components not previously covered; and 3) techniques that order test cases based on their estimated ability to reveal faults in the code components that they cover. We report the results of several experiments in which we applied these techniques to various test suites for various programs and measured the rates of fault detection achieved by the prioritized test suites, comparing those rates to the rates achieved by untreated, randomly ordered, and optimally ordered suites.

Prioritizing test cases for regression testing

The high cost of locating faults in programs has motivated the development of techniques that assist in fault localization by automating part of the process of searching for faults. Empirical studies that compare these techniques have reported the relative effectiveness of four existing techniques on a set of subjects. These studies compare the rankings that the techniques compute for statements in the subject programs and the effectiveness of these rankings in locating the faults. However, it is unknown how these four techniques compare with Tarantula, another existing fault-localization technique, although this technique also provides a way to rank statements in terms of their suspiciousness. Thus, we performed a study to compare the Tarantula technique with the four techniques previously compared. This paper presents our study---it overviews the Tarantula technique along with the four other techniques studied, describes our experiment, and reports and discusses the results. Our studies show that, on the same set of subjects, the Tarantula technique consistently outperforms the other four techniques in terms of effectiveness in fault localization, and is comparable in efficiency to the least expensive of the other four techniques.

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Empirical evaluation of the tarantula automatic fault-localization technique

The purpose of regression testing is to ensure that changes made to software, such as adding new features or modifying existing features, have not adversely affected features of the software that should not change. Regression testing is usually performed by running some, or all, of the test cases created to test modifications in previous versions of the software. Many techniques have been reported on how to select regression tests so that the number of test cases does not grow too large as the software evolves. Our proposed hybrid technique combines modification, minimization and prioritization-based selection using a list of source code changes and the execution traces from test cases run on previous versions. This technique seeks to identify a representative subset of all test cases that may result in different output behavior on the new software version. We report our experience with a tool called ATAC (Automatic Testing Analysis tool in C) which implements this technique.

A study of effective regression testing in practice

Finding a fault in a program is a complex process which involves understanding the program's purpose, structure, semantics, and the relevant characteristics of failure producing tests. We describe a tool which supports execution slicing and dicing based on test cases. We report the results of an experiment that uses heuristic techniques in fault localization.

Fault localization using execution slices and dataflow tests

The purpose of regression testing is to ensure that bug fixes and new functionality introduced in a new version of a software do not adversely affect the correct functionality inherited from the previous version. Efficient methods of selecting small subsets of regression test sets that can be used to ensure correct functionality are explored. >

Incremental regression testing

Size and code coverage are important attributes of a set of tests. When a program P is executed on elements of the test set T, we can observe the fault detecting capability of T for P. We can also observe the degree to which T induces code coverage on P according to some coverage criterion. We would like to know whether it is the size of T or the coverage of T on P which determines the fault detection effectiveness of T for P. To address this issue we ask the following question: While keeping coverage constant, what is the effect on fault detection of reducing the size of a test set? We report results from an empirical study using the block and all-uses criteria as the coverage measures.

Effect of test set minimization on fault detection effectiveness

Given a test set T to test a program P, there are at least two attributes of T that determine its fault detection effectiveness. One attribute is the size of T measured as the number of test cases in T. Another attribute is the code coverage measured when P is executed on all elements of T. The fault detection effectiveness of T is the ratio of the number of faults guaranteed to result in program failure when P is executed on T to the total number of faults present in P. An empirical study was conducted to determine the relative importance of the size and coverage attributes in affecting the fault detection effectiveness of a randomly selected test set for some program P. Results from this study indicate that as the size of a test set is reduced, while the code coverage is kept constant, there is little or no reduction in the fault detection effectiveness of the new test set so generated. For the study reported, of the two attributes mentioned above, the code coverage attribute of a test set is more important than its size attribute. © 1998 John Wiley & Sons, Ltd.

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Papers

A study of effective regression testing in practice

Fault localization using execution slices and dataflow tests

Incremental regression testing

Effect of test set minimization on fault detection effectiveness

Effect of test set minimization on fault detection effectiveness