Change Impact Analysis: A Tool for Effective Regression Testing

Change impact analysis is an imperative activity for the maintenance of software. It determines the set of modules that are changed and modules that are affected by the change(s). It helps in regression testing because only those modules that are either changed or affected by the suggested change(s) are retested. Change impact analysis is a complex activity as it is very difficult to predict the impact of a change in software. Different researchers have proposed different change impact analysis approaches that help in prioritization of test cases for regression testing. In this paper, an approach based on Total Importance of Module (TIM) has been proposed that determines the importance of a module on the basis of (i) user requirements, and (ii) system requirements. The results of the proposed algorithm showed that the importance of a module is an essential attribute in forming a prioritized test suite for regression testing

Improved Annealing-Genetic Algorithm for Test Case Prioritization

Regression testing, which can improve the quality of software systems, is a useful but time consuming method. Many techniques have been introduced to reduce the time cost of regression testing. Among these techniques, test case prioritization is an effective technique which can reduce the time cost by processing relatively more important test cases at an earlier stage. Previous works have demonstrated that some greedy algorithms are effective for regression test case prioritization. Those algorithms, however, have lower stability and scalability. For this reason, this paper proposes a new regression test case prioritization approach based on the improved Annealing-Genetic algorithm which incorporates Simulated Annealing algorithm and Genetic algorithm to explore a bigger potential solution space for the global optimum. Three Java programs and five C programs were employed to evaluate the performance of the new approach with five former approaches such as Greedy, Additional Greedy, GA, etc. The experimental results showed that the proposed approach has relatively better performance as well as higher stability and scalability than those former approaches