Adaptive Test Optimization: Using Reinforcement Learning to Improve Software Testing Strategies
Keywords:
Reinforcement learning, adaptive test optimization, software testing, machine learning in testing, AI-driven test automation, test case prioritization, defect detection, DevOps integrationAbstract
Software testing strategies receive an enhancement through reinforcement learning technology which delivers adaptive test optimization by performing dynamic test case selection and ranking and test case productions. Software testing techniques that use manual and automated approaches encounter difficulties because they are inefficient while being costly to execute and unable to reconfigure for fast-moving software systems. Self-learning testing frameworks enabled by reinforcement learning provide solutions to these issues through optimization of test execution using historic performance results and current software modifications. The research describes essential RL methods implemented in software testing which cover test case prioritization together with defect detection capabilities and dynamic testing code generation. The analysis presents actual cases where RL-based testing tools significantly improve both testing efficiency and faulty program identification capabilities. Testing based on AI technologies encounters three primary challenges which include complex calculations and limited data access and system-related ethical limits. AI-driven software testing will face two evolving trends in the future as experts intend to merge RL with DevOps toolchains and improve explainable AI capabilities to establish higher trust in AI testing systems.
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