Fuzzy Inference System for Test Case Prioritization in Software Testing

Aron Karatayev, Anna Ogorodova, Pakizar Shamoi
4/25/2024
cs.SEcs.AI

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Abstract

In the realm of software development, testing is crucial for ensuring software quality and adherence to requirements. However, it can be time-consuming and resource-intensive, especially when dealing with large and complex software systems. Test case prioritization (TCP) is a vital strategy to enhance testing efficiency by identifying the most critical test cases for early execution. This paper introduces a novel fuzzy logic-based approach to automate TCP, using fuzzy linguistic variables and expert-derived fuzzy rules to establish a link between test case characteristics and their prioritization. Our methodology utilizes two fuzzy variables - failure rate and execution time - alongside two crisp parameters: Prerequisite Test Case and Recently Updated Flag. Our findings demonstrate the proposed system capacity to rank test cases effectively through experimental validation on a real-world software system. The results affirm the practical applicability of our approach in optimizing the TCP and reducing the resource intensity of software testing.

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Cite this paper

@article{karatayev2024fuzzy,
  title  = {Fuzzy Inference System for Test Case Prioritization in Software Testing},
  author = {Aron Karatayev and Anna Ogorodova and Pakizar Shamoi},
  year   = {2024},
  eprint = {2404.16395},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.16395v1}
}

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