The Integration of Machine Learning into Automated Test Generation: A Systematic Literature Review

  title={The Integration of Machine Learning into Automated Test Generation: A Systematic Literature Review},
  author={Afonso Fontes and Gregory Gay},
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… 



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Quickly Generating Diverse Valid Test Inputs with Reinforcement Learning

This paper formalizes the problem of guiding random input generators towards producing a diverse set of valid inputs and proposes a solution based on reinforcement learning (RL), using a tabular, on-policy RL approach to guide the generator.