Optimizing Test Case Prioritization Using Early Artificial Intelligence Approaches

Test case prioritization is a critical activity in software testing that aims to improve fault detection rates and reduce testing time and cost. This paper explores the use of early artificial intelligence techniques to optimize test case prioritization in software development environments. Traditional prioritization methods often rely on manual analysis, code coverage, or risk-based approaches, which may not effectively adapt to complex and rapidly changing software systems. In this study, early AI approaches such as rule-based systems, decision trees, clustering, and simple machine learning algorithms are applied to analyze historical defect data, test execution results, and code change metrics to determine the priority of test cases. The proposed approach aims to execute high-risk and fault-prone test cases earlier in the testing process, thereby improving testing efficiency and software reliability. The results indicate that AI-based prioritization methods can significantly enhance fault detection rates, optimize resource utilization, and support intelligent decision-making in software testing processes. This research demonstrates that even early-stage AI techniques can play an important role in improving test case prioritization and overall software quality assurance.