A Novel Framework For Optimal Test Case Generation and Prioritization Using Ent-LSOA And IMTRNN Techniques

Abstract

Test Case Generation (TCG) generates various types of tests, including functional tests, performance tests, security tests, and reliability tests to ensure software quality, while Test Case Prioritization (TCP) prioritizes the generated tests. However, the previous studies had challenges, including resource constraints, detecting crucial requirements, and automating the Test Case (TC) process efficiently. Additionally, the process is costlier and takes a maximum time duration that affects the effective performance. Therefore, an effective framework is proposed to overcome such issues by optimizing TCG and TCP processes effectively. The proposed work starts with the generation of a Unified Modeling Language (UML) diagram from historical project source code, which is then converted into a Comma-Separated Value (CSV) format. Then, the feature extraction is performed on this CSV file, followed by optimal TCG using the Entropy-based Locust Swarm Optimization Algorithm (Ent-LSOA). Additionally, factors are extracted and reduced from the historical project source code using Pearson Correlation Coefficient-Generalized Discriminant Analysis (PCC-GDA). Finally, the optimal TCs and selected factors are prioritized with the highest accuracy and recall of 96.89% and 96.92%, respectively using an Interpolated Multiple Time scale Recurrent Neural Network (IMTRNN). Thus, the proposed work outperformed the existing techniques by providing an efficient solution for TCG and TCP in software testing.