Intelligent Test Management Optimization: A Machine Learning Framework for Enhanced Software Testing Decision-Making

Software testing remains an essential and challenging part of today's software development, especially in Continuous Integration (CI) environments, where tests must be decided on quickly. This paper introduces a new framework, ITMOS (intelligent test management optimisation system), based on machine learning and natural language processing, to automate important test management decisions. The system tackles two core problems: automatic mapping of suitable CI configurations to requirements' characteristics, and intelligent suggestions for test automation strategies. With an ensemble approach in place of semantic classifiers, word-distribution analysers, and categorical data processors, ITMOS reaches F1-scores of 0.9163 for CI configuration classification and 0.9463 for test automation recommendations. Industrial validation demonstrates significant improvements in decision speed (a 96% speedup), consistency (a 100% agreement rate compared to 76% manual agreement), and global testing efficiency. This framework is a significant advancement in intelligent software engineering tools, with applied solutions to scalable ways of managing tests in large development contexts.