Improved Path Testing Using Multi-Verse Optimization Algorithm and the Integration of Test Path Distance

Abstract

Emerging technologies in artificial intelligence (AI) and advanced optimization methodologies have opened up a new frontier in the field of software engineering. Among these methodologies, optimization algorithms such as the multi-verse optimizer (MVO) provide a compelling and structured technique for identifying software vulnerabilities, thereby enhancing software robustness and reliability. This research investigates the feasibility and efficacy of applying an augmented version of this technique, known as the test path distance multiverse optimization (TPDMVO) algorithm, for comprehensive path coverage testing, which is an indispensable aspect of software validation. The algorithm’s versatility and robustness are examined through its application to a wide range of case studies with varying degrees of complexity. These case studies include rudimentary functions like maximum and middle value extraction, as well as more sophisticated data structures such as binary search trees and AVL trees. A notable innovation in this research is the introduction of a customized fitness function, carefully designed to guide TPDMVO towards traversing all possible execution paths in a program, thereby ensuring comprehensive coverage. To further enhance the comprehensiveness of the test, a metric called ‘test path distance’ (TPD) is utilized. This metric is designed to guide TPDMVO towards paths that have not been explored before. The findings confirm the superior performance of the TPDMVO algorithm, which achieves complete path coverage in all test scenarios. This demonstrates its robustness and adaptability in handling different program complexities.