Test Case Prioritization Using Ant Colony Optimization to Improve Fault Detection and Time
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Abstract
Regression testing plays a critical role in ensuring the reliability and quality of software following continuous integration and development. However, executing all test cases during regression testing can be time-consuming and resource-intensive. Test Case Prioritization (TCP) addresses this challenge by determining an optimal execution order of test cases that maximizes early fault detection while minimizing execution time. Optimization algorithms contribute significantly to enhancing the effectiveness of TCP while utilizing limited resources. This study proposes an Ant Colony Optimization (ACO) algorithm to address the TCP problem, leveraging its strength in navigating complex search spaces inspired by the foraging behavior of real ant colonies. It involves four phases: dataset selection, dataset conversion, algorithm implementation, and performance evaluation. ACO was implemented and evaluated on two datasets (Case Study One and Case Study Two) of differing sizes and complexity. The results demonstrate its potential to improve testing efficiency and effectiveness with limited resources using the Average Percentage Fault Detected (APFD) and execution time. Case Study One, which involved a larger dataset, achieved a higher APFD (0.6911), but required more iterations and execution time (0.3733 s). In contrast, Case Study Two, with fewer test cases and faults, demonstrated a faster convergence and execution time (0.2596 s), with a slightly lower APFD (0.6700). These findings demonstrate a trade-off between early fault detection and execution efficiency, indicating that dataset characteristics such as size and fault density influence the performance of the algorithm.
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