Machine Learning for Practical and Scalable Regression Test Selection and Prioritization

In the context of systems with a large codebase, Continuous Integration (CI) significantly reduces integration problems, speed up development time, and shorten release time. While regression testing is widely practiced in the context of CI, it can be time-consuming and resource intensive for large codebases where the execution of test cases is time and resource intensive. In this project, we try to devise and apply Machine Learning-based solutions for three critical problems related to regression testing in the context of CI: (1) test selection and prioritization, (2) test case minimization, (3) automatic refactoring and generation of test cases.

Faculty Supervisor:

Lionel Briand


Nafiseh Kahani


Huawei Technologies Canada


Engineering - computer / electrical



University of Ottawa



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