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Enhancing Software Testing Efficiency through AI-guided Test Case Prioritization: A Systematic Literature Review

Fariya Sultana Prity

Abstract


In today's software development landscape, the need for efficient testing methodologies has become paramount to ensure the delivery of high-quality software products. With the advent of artificial intelligence (AI) techniques, test case prioritization has emerged as a promising approach to optimize testing efforts. This systematic literature review delves into the realm of enhancing software testing efficiency through AI-guided test case prioritization. The review synthesizes findings from a range of studies that apply diverse AI techniques in various software domains, emphasizing their outcomes in terms of evaluation metrics such as code coverage, fault detection rates, execution time, mutation scores, and defect identification accuracy. The presented research contributes to a comprehensive understanding of the ways AI-driven prioritization can revolutionize software testing practices.


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References


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