Boosting search-based software testing with ML
Software testing is a critical yet expensive activity in software development. Test case generation techniques can be used to automate the process of synthesizing and executing test cases with very high code coverage.
However, coverage-adequate test suites not necessarily find more bugs than test suite with Lowe code coverage. While coverage is a prerequisite to fault detection capability, it is not a sufficient condition.
In this project, you will research how to use ML to measure/predict the fault-detection capabilities of generated test suites.
- AI (search-based) techniques
- Test case synthesis
- Fault detection capability
Contact for the project
- Annibale Panichella (TU Delft)