Improving Fault Localization Using Model-domain Synthesized Failing Test Generation

A test suite is indispensable for conducting effective fault localization, and has two classes of tests: passing tests and failing tests. However, in practice, passing tests heavily outnumber failing tests regarding a fault, leading to failing tests being a minority class in contrast to passing tests. Previous work has empirically shown that the lack of failing tests regarding a fault leads to a class-balanced test suite, which tends to hamper fault localization effectiveness.To address this issue, we propose MSGen: a Model-domain Synthesized Failing Test Generation approach. MSGen utilizes the widely used information model of fault localization (i.e., an abstraction of the execution information and test results of a test suite), and uses the minimum variability of the minority feature space to create new synthesized model-domain failing test samples (i.e., synthesized vectors with failing labels defined as the information model) for fault localization. In contrast to traditional test generation directly from the input domain, MSGen seeks to synthesize failing test samples from the model domain. We apply MSGen to 12 state-of-the-art localization approaches and also compare MSGen to 2 representative data optimization approaches. The experimental results show that our synthesized test generation approach significantly improves fault localization effectiveness with up to 51.22%.