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Transfer Learning for Automated Test Case Prioritization Using XCSF

With the rise of test automation, companies start to rely on large amounts of test cases. However, there are situations where it is unfeasible to perform every test case as only a limited amount of time is available. Under such circumstances a set of crucial tests has to be compiled. Recent research has shown that reinforcement learning methods such as XCSF classifier systems are well-suited for this task. This work investigates whether reusing knowledge of XCSF-based agents is beneficial for prioritizing test cases and subsequently selecting test suites in terms of performance. We developed a simplistic population transformation and evaluate it in a series of experiments. Our evaluation shows that XCSF may indeed benefit from transfer learning for this use case.

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