Critical Path-Aware Deep Learning Architecture for Efficient Test Case Prioritization and Minimization
Keywords:
Test case prioritization; Test case minimization; Optimization; Software testing; Critical path analysis; Deep learningAbstract
Test case prioritization and minimization are essential practices to augment the efficiency of the testing process in software testing. However, conventional methods often struggle with large-scale software systems due to their inability to effectively handle the critical path, leading to suboptimal prioritization and minimal test coverage This research paper proposes an Enhanced Electric-Eel with Critical Path-Aware Foraging Optimization (EECPFO) algorithm tailored for test case minimization and prioritization in software testing. The algorithm is designed to address the challenges of minimizing redundancy, prioritizing critical paths, and maintaining diversity in test suites. To achieve this, modifications including a Dynamic Fitness Function, Redundancy-aware Foraging, Critical Path Sensitivity, and Diversity Maintenance are integrated into the EEFO framework. The proposed algorithm is evaluated for its effectiveness using three open-source Java programs (JTopas, Ant, and JMeter) from the Software Infrastructure Repository (SIR), employing well-known metrics such as Average Percentage of Fault Detection (APFD) and Average Percentage of Fault Detection with Cost (APFDc). Experimental results demonstrate significant improvements in test case prioritization and minimization compared to benchmark algorithms, showcasing enhanced fault detection rates, coverage, and cost reduction percentages. The findings, highlight the potential of the proposed EECPFO algorithm as a valuable tool for optimizing software testing processes, leading to more efficient and effective quality assurance practices.
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