Generating Effective Test Suites by Combining Coverage Criteria

Gay, Gregory

doi:10.1007/978-3-319-66299-2_5

Cited by 34 publications

(46 citation statements)

References 15 publications

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“…However, these questions presuppose that only one fitness function can be used to generate test suites. Many search‐based generation algorithms can simultaneously target multiple fitness functions . Therefore, we also ask question 4—when does it make sense to employ a set of fitness functions instead of a single function?…”

Section: Studymentioning

confidence: 99%

“…The second is a combination of branch, exception, and method coverage (called the “BC‐EC‐MC combination”). This combination was identified as an effective baseline in our prior work studying combination efficacy on the five original systems from Defects4J .…”

Section: Studymentioning

confidence: 99%

“…Our updated study includes suites generated over those new case examples, adding further observations and points of discussion. We have also used the findings of our separate research into combinations of fitness functions to reformulate and extend our experiments and discussion of the effects of combining criteria. In addition, we have changed how we build and classify data in our treatment learning analysis, added the source code metric analysis, and have included a far deeper examination of the factors indicating success or lack thereof in test generation.…”

Section: Introductionmentioning

confidence: 99%

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Choosing the fitness function for the job: Automated generation of test suites that detect real faults

Salahirad

Almulla

2019

Software Testing Verif & Rel

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Summary Search‐based unit test generation, if effective at fault detection, can lower the cost of testing. Such techniques rely on fitness functions to guide the search. Ultimately, such functions represent test goals that approximate—but do not ensure—fault detection. The need to rely on approximations leads to two questions—can fitness functions produce effective tests and, if so, which should be used to generate tests? To answer these questions, we have assessed the fault‐detection capabilities of unit test suites generated to satisfy eight white‐box fitness functions on 597 real faults from the Defects4J database. Our analysis has found that the strongest indicators of effectiveness are a high level of code coverage over the targeted class and high satisfaction of a criterion's obligations. Consequently, the branch coverage fitness function is the most effective. Our findings indicate that fitness functions that thoroughly explore system structure should be used as primary generation objectives—supported by secondary fitness functions that explore orthogonal, supporting scenarios. Our results also provide further evidence that future approaches to test generation should focus on attaining higher coverage of private code and better initialization and manipulation of class dependencies.

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Section: Studymentioning

confidence: 99%

Section: Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Choosing the fitness function for the job: Automated generation of test suites that detect real faults

Salahirad

Almulla

2019

Software Testing Verif & Rel

Self Cite

View full text Add to dashboard Cite

show abstract

“…These coverage criteria ensure the sufficiency of testing and provide implications for the test case generation algorithm. Here are four test coverage criteria used in our design, for test case generation of SysML activity diagram [19,25,26]:…”

Section: Test Coverage Criteriamentioning

confidence: 99%