FDG: a precise measurement of fault diagnosability gain of test cases
Gabin An, Shin Yoo
Abstract
The performance of many Fault Localisation (FL) techniques directly depends on the quality of the used test suites. Consequently, it is extremely useful to be able to precisely measure how much diagnostic power each test case can introduce when added to a test suite used for FL. Such a measure can help us not only to prioritise and select test cases to be used for FL, but also to effectively augment test suites that are too weak to be used with FL techniques. We propose FDG, a new measure of Fault Diagnosability Gain for individual test cases. The design of FDG is based on our analysis of existing metrics that are designed to prioritise test cases for better FL. Unlike other metrics, FDG exploits the ongoing FL results to emphasise the parts of the program for which more information is needed. Our evaluation of FDG with Defects4J shows that it can successfully help the augmentation of test suites for better FL. When given only a few failing test cases (2.3 test cases on average), FDG can effectively augment the given test suite by prioritising the test cases generated automatically by EvoSuite: the augmentation can improve the acc@1 and acc@10 of the FL results by 11.6x and 2.2x on average, after requiring only ten human judgements on the correctness of the assertions EvoSuite generates. CCS CONCEPTS • Software and its engineering → Software testing and debugging.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext fb9db23b-1007-4d06-ace6-ad3ee9986318Related papers
- Defect Prediction Guided Search-Based Software TestingAnjana Perera, Aldeida Aleti, Marcel Böhme, Burak TurhanASE 2020 · 16 citations
- Improving Spectrum-Based Localization of Multiple Faults by Iterative Test Suite ReductionDylan Callaghan, Bernd FischerISSTA 2023 · 16 citations
- Increasing the Effectiveness of Automatically Generated Tests by Improving Class ObservabilityGeraldine Galindo-Gutierrez, Juan Pablo Sandoval Alcocer, Nicolas Jimenez-Fuentes, Alexandre Bergel et al.ICSE 2025 · 1 citation
- How Does Killing Surviving Mutants Help Detect Real Bugs with Assertion Generation? A Controlled ExperimentHang Du, Vijay Krishna Palepu, James A. JonesISSTA 2026
- On the Effectiveness of Unified Debugging: An Extensive Study on 16 Program Repair SystemsSamuel Benton, Xia Li, Yiling Lou, Lingming ZhangASE 2020 · 35 citations
