Article
INTELLIGENT TEST MANAGEMENT SYSTEMS FOR OPTIMIZING DECISION-MAKING DURING SOFTWARE TESTING: A NARRATIVE REVIEW
Background: The increasing complexity of software systems and accelerated development cycles have intensified the need for intelligent test management systems that optimize decisionmaking during software testing processes. Objective: This narrative review examines the current state of intelligent test management systems, evaluating their effectiveness in optimizing testing decisions, identifying key technologies, and assessing implementation challenges. Methods: We conducted a comprehensive review of 25 verified studies published between 2007-2024, sourced from IEEE Xplore, ACM Digital Library, ScienceDirect, and other reputable databases. Studies were selected based on their focus on AI/ML applications in software testing and empirical validation of intelligent decision-making systems. Results: Our analysis reveals that AI-powered test management systems achieve 85-95% accuracy in automated testing decisions, with 40-60% reduction in test execution time and 20- 50% cost savings. Machine learning classification techniques dominate current implementations, followed by genetic algorithms and natural language processing. The ITMOS framework demonstrates industrial viability with F1-scores of 0.9163 for CI configuration and 0.9463 for test automation decisions. Conclusions: Intelligent test management systems show significant promise for transforming software testing practices through automated decision support, predictive analytics, and adaptive optimization. However, challenges remain in integration complexity, tool reliability, and organizational adoption
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