Adaptive Test Optimization: Using Reinforcement Learning to Improve Software Testing Strategies

Abstract

By executing adaptive test optimization through dynamic test case selection and ranking and test case creations, reinforcement learning technology improves software testing methodologies. Software testing methods that rely on human or automated processes have their limitations due to their inefficiency, high execution costs, and inability to adapt to rapidly changing software systems. Reinforcement learning-enabled self-learning testing frameworks address these concerns by optimizing test execution based on past performance outcomes and present software changes. Prioritizing test cases, detecting defects, and generating dynamic testing code are all aspects of software testing that are covered in the study on key RL approaches. In this investigation, we look at real-world examples of how RL-based testing methods boost productivity and find bugs in programs far more effectively. There are three main obstacles to testing based on AI technologies: complicated computations, restricted data availability, and ethical constraints associated to the system. In order to build more trust in AI testing systems, experts plan to combine RL with DevOps toolchains, and they will also work to develop explainable AI capabilities. These two trends will impact AI-driven software testing in the future.