Abstract:Context: Machine learning (ML) may enable effective automated test generation.Objectives: We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges.Methods: We perform a systematic literature review on a sample of 97 publications.Results: ML generates input for system, GUI, unit, performance, and combinatorial testing or improves the performance of existing generation methods. ML is also used to generate test verdicts, property-based, an… Show more
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