Optimizing the software test case through physics-informed particle-based method

Software testing relies heavily on test case reduction, which tries to lessen the number of test cases despite preserving enough coverage. This study presents a novel method named Physics-Informed Particle-based Optimization (PIPO) for improving the reduction of software test cases through the use of particle-based optimization, which is guided by physics. Particle dynamics serves as the inspiration for the suggested optimization approach, which treats test cases as particles traveling across a multidimensional space. These particles move according to physics-based principles as well as conventional optimization techniques, leading to a hybrid approach that improves solution quality and convergence efficiency. Employing experimental assessments on a range of software test cases, we illustrate the efficacy of our approach and show notable benefits in terms of decreased test suite sizes by 25% relative to baseline techniques without sacrificing test coverage. The test cases are reduced from 167 (original) to 121 (reduced) by the suggested approach. Moreover, PIPO’s competitive performance is demonstrated by a comparative analysis with the most advanced test case minimization algorithms, obtaining the reduced test set with suitable and acceptable values such as Best - 195.4 ± 5.62, Avg - 184.6 ± 3.27, and Std Dev - 2.06 ± 0.68. The results highlight the efficiency benefits attained by using physics-inspired methods, showing not just greater reduction rates but also faster convergence. Thus, incorporating PIPO into software testing can offer a promising avenue for achieving more efficient and effective test case minimization strategies.