Efficient Fault Detection by Test Case Prioritization via Test Case Selection

Abstract

One of the significant features of software quality is software reliability. In the testing phase, faults are identified and corrected by integrating them into software development, thus obtaining better reliability. Here, by utilizing the Elliptical Distributions-centric Emperor Penguins Colony Algorithm (ED-EPCA)-based Test Case Prioritization (TCP), an effectual Fault Detection (FD) technique is proposed using Fishers Yates Shuffled Shepherd Optimization Algorithm (FY-SSOA)-based Test Case Selection (TCS). Initially, for the incoming source code, the Test Case (TC) is created. Then, the significant factors needed for TCS and prioritization are identified. Next, by utilizing the Log Scaling-centered Generalized Discriminant Analysis (LS-GDA) model, the estimated factors are abated further to enhance the TCS along with prioritization for the Fault Detection Process (FDP). Then, using the FY-SSOA, the optimized TCs are selected. Subsequently, with the help of ED-EPCA, the TCs being selected are ranked as well as prioritized. Finally, to validate the proposed system’s effectiveness, the model’s performance is evaluated in the working platform of Java and analogized with the traditional methodologies. The results indicate that the test case prioritization-based fault detection method is robust with a 99.23% fault detection rate and a small amount of memory usage, which is only 8245475 kb by generating a large number of test cases.