SEDec 6, 2019

FlakiMe: Laboratory-Controlled Test Flakiness Impact Assessment. A Case Study on Mutation Testing and Program Repair

arXiv:1912.03197v18 citations
Originality Incremental advance
AI Analysis

This addresses the issue of non-deterministic test failures for researchers in software testing, particularly in mutation testing and program repair, though it is incremental as it builds on existing flakiness concerns.

The paper tackles the problem of flaky tests in software testing by introducing FlakiMe, a platform for seeding controllable flakiness into test suites, and finds that 5% flakiness modestly affects mutation scores (2-4% impact) but completely disrupts program repair for 50% of programs.

Much research on software testing makes an implicit assumption that test failures are deterministic such that they always witness the presence of the same defects. However, this assumption is not always true because some test failures are due to so-called flaky tests, i.e., tests with non-deterministic outcomes. Unfortunately, flaky tests have major implications for testing and test-dependent activities such as mutation testing and automated program repair. To deal with this issue, we introduce a test flakiness assessment and experimentation platform, called FlakiMe, that supports the seeding of a (controllable) degree of flakiness into the behaviour of a given test suite. Thereby, FlakiMe equips researchers with ways to investigate the impact of test flakiness on their techniques under laboratory-controlled conditions. We use FlakiME to report results and insights from case studies that assesses the impact of flakiness on mutation testing and program repair. These results indicate that a 5% of flakiness failures is enough to affect the mutation score, but the effect size is modest (2% - 4% ), while it completely annihilates the ability of program repair to patch 50% of the subject programs. We also observe that flakiness has case-specific effects, which mainly disrupts the repair of bugs that are covered by many tests. Moreover, we find that a minimal amount of user feedback is sufficient for alleviating the effects of flakiness.

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