Applying Genetic Algorithm for Prioritization of Test Case Scenarios Derived from UML Diagrams

TL;DR

This paper presents a genetic algorithm-based method to prioritize test case scenarios derived from UML diagrams, aiming to optimize testing efficiency by focusing on critical path clusters.

Contribution

It introduces a novel approach combining genetic algorithms with UML-derived test scenarios to improve test prioritization efficiency.

Findings

Effective prioritization of test cases achieved

Reduced testing time through critical path focus

Enhanced testing process efficiency

Abstract

Software testing involves identifying the test cases whichdiscover errors in the program. However, exhaustive testing ofsoftware is very time consuming. In this paper, a technique isproposed to prioritize test case scenarios by identifying the critical path clusters using genetic algorithm. The test case scenarios are derived from the UML activity diagram and state chart diagram. The testing efficiency is optimized by applying the genetic algorithm on the test data. The information flow metric is adopted in this work for calculating the information flow complexity associated with each node of the activity diagram and state chart diagram.