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Dozens of criteria have been proposed to judge testing adequacy. Such criteria are important, as they guide automated generation efforts. Yet, the current use of such criteria in automated generation contrasts how such criteria are used by humans. For a human, coverage is part of a multifaceted combination of testing strategies. In automated generation, coverage is typically the goal, and a single fitness function is applied at one time. We propose that the key to improving the fault detection efficacy of search-based test generation approaches lies in a targeted, multifaceted approach pairing primary fitness functions that effectively explore the structure of the class under test with lightweight supporting fitness functions that target particular scenarios likely to trigger an observable failure. This report summarizes our findings to date, details the hypothesis of primary and supporting fitness functions, and identifies outstanding research challenges related to multifaceted test suite generation. We hope to inspire new advances in search-based test generation that could benefit our software-powered society.
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Choosing the fitness function for the job: Automated generation of test suites that detect real faults
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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- Award ID(s):
- 1657299
- PAR ID:
- 10461204
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Software Testing, Verification and Reliability
- Volume:
- 29
- Issue:
- 4-5
- ISSN:
- 0960-0833
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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