Automated Whitebox Test Case Generation for Statement Coverage Using U-NSGA-III

Abstract

Software testing is a significant and complex phase of software development as it ensures the complete functionality of the software. To evaluate the behaviour of the software the testing process acquires generation of test cases as an input for the system under test. Generation of test cases remains a challenging task usually as it is done based on human intelligence and it has become much complicated and time-consuming process. Therefore, it can be enhanced by automating the test case generation mechanism to identify and eliminate bugs. Unified Modelling Language (UML) is a de facto standard that has been used in academia and industry currently. Test case automation using UML diagrams is more effective and efficient as it is done early in the software development life cycle. Therefore, to overcome the problem of time as well as budget constraint, it is required to optimize the entire test suite. Many researchers have proposed conventional based approaches for dealing with this problem and they have achieved optimized test cases by selecting, minimizing, or reducing the test cases. However, in this research UML sequence diagram and class diagram have been proposed for white box testing. To generate test cases these two diagrams have been proved as compatible from existing literature. Currently, existing approaches dealing with test case optimization have achieved 85% and 90% statement coverage for the System Under Test 1 (SUT1) and System Under Test 2 (SUT2) using genetic algorithm. However, in this research Unified Non-Sorting Genetic Algorithm (U-NSGA-III) has been proposed for automated test case generation and optimization. The proposed approach has achieved 95% statement coverage for SUT1 and 90.47% for SUT2. In this dissertation, two benchmark case studies have been used and controlled experimentation have been performed for optimization of test cases. The comparison of our approach has been done with GA-UNSGAIII and PSO-UNSGAIII. From our results, it has been concluded that proposed U-NSGA-III has performed better than other approaches.