Towards crash reproduction benchmark augmentation using mutation testing
Many applications are developed with a lot of different purposes and can provide quality output. Nevertheless, crashes still happen. Many techniques such as unit testing, peer-reviewing, or crash reproduction are being researched to improve quality by reducing crashes. This thesis contributes to the fast-evolving field of research on crash reproduction tools. These tools seek better reproduction with minimum information as input while delivering correct outputs in various scenarios. Different approaches have previously been tested to gather input-output data, also called benchmarks, but they often take time and manual e↵ort to be usable. The research documented in this thesis endeavours to synthesize crashes using mutation testing to serve as input for crash reproduction tools.
- JCrashPack2.0: Search-based crash reproduction hardness analysis
- Learning to assert in software testing using mutants
- MuTEd: A Comparative Study of Classic and Extreme Mutation Testing for Teaching Software Testing
- DeFlake: Exploring test flakiness debugging
- Génération de tests unitaires pour programmes Python