The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study

• URL: http://arxiv.org/abs/2206.10210v5
• Date: Sun, 16 Apr 2023 03:43:15 GMT
• Title: The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study
• Authors: Afonso Fontes and Gregory Gay
• Abstract summary: We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges. ML generates input for system, GUI, unit, performance, and testing or improves the performance of existing generation methods.
• Score: 15.016047591601094
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context: Machine learning (ML) may enable effective automated test generation. Objective: We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges. Methods: We perform a systematic mapping on a sample of 124 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, and expected output oracles. Supervised learning - often based on neural networks - and reinforcement learning - often based on Q-learning - are common, and some publications also employ unsupervised or semi-supervised learning. (Semi-/Un-)Supervised approaches are evaluated using both traditional testing metrics and ML-related metrics (e.g., accuracy), while reinforcement learning is often evaluated using testing metrics tied to the reward function. Conclusion: Work-to-date shows great promise, but there are open challenges regarding training data, retraining, scalability, evaluation complexity, ML algorithms employed - and how they are applied - benchmarks, and replicability. Our findings can serve as a roadmap and inspiration for researchers in this field.

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