Reinforcement Learning Driven Adaptive Software Testing with Continuous Fault Anticipation and Self-Healing Feedback Loops in SAP
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I4P104Keywords:
Reinforcement Learning, Adaptive Testing, Fault Anticipation, Self-Healing Systems, Continuous FeedbackAbstract
With the changing dynamics in software development comes the need for a smart testing framework that can adapt to changes in the environment. Our approach brings a new way of viewing reinforcement learning (RL) and adaptive software testing, that combines them to constantly build a fault anticipation and self-healing mechanisms. The ultimate goal is to create some autonomous testing tool which understands and learns the defect patterns from history, and through feedback loops optimizes the evolution of the test case generation. We utilize Q-learning algorithms via Markov Decision Processes to create adaptive testing agents that learn from the results of fault detections. The hybrid model that is built-in to LSTM enables predictive fault prediction, and the self-healing mechanisms automatically change testing parameters. Over a half-a-year span of testing around 15,000 test cycles across five open-source Google projects of differing complexity, an illuminating study was performed. The results show an increase of 47.3% in fault detection rates and decrease of 38.6% in testing time in comparison with traditional approaches. NOTE: Statistical analysis presents strong associations between adaptation driven by RL & defect prediction. Our proposed framework gets 92.4% precision concerning vital faults to predict before deployment. This study adds value to autonomous software quality assurance by creating intelligent self-optimizing testing ecosystems
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