Machine Learning for Automated Flaky Test Diagnosis in JVMs

• Rishabh Singh

  Author

This paper explores the application of machine learning to address the pervasive issue of flaky tests in software development within Java Virtual Machines (JVMs). Existing flaky-test detection approaches primarily focus on identifying whether a test is flaky but do not provide diagnostic information about why flakiness occurs. We present a framework that integrates statistical and machine learning techniques with dynamic taint analysis to automatically identify and tag candidate root causes of nondeterministic test failures. By tracking data flow using bytecode modifications, this approach facilitates automated debugging by providing evidence for candidate sources of nondeterminism. The framework is evaluated in terms of classification performance, diagnostic localization, and runtime overhead. The results indicate that the hybrid approach can effectively identify flaky tests and localize potential sources of nondeterminism in JVM-based software systems.

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