Test Case Prioritization for Software Product Line: A Systematic Mapping Study

Combinatorial explosion remains a common issue in testing. Due to the vast number of product variants, the number of test cases required for comprehensive coverage has significantly increased. One of the techniques to efficiently tackle this problem is prioritizing the test suites using a regression testing method. However, there is a lack of comprehensive reviews focusing on test case prioritization in SPLs. To address this research gap, this paper proposed a systematic mapping study to observe the extent of test case prioritization usage in Software Product Line Testing. The study aims to classify various aspects of SPL-TCP (Software Product Line – Test Case Prioritization), including methods, criteria, measurements, constraints, empirical studies, and domains. Over the last ten years, a thorough investigation uncovered twenty-four primary studies, consisting of 12 journal articles and 12 conference papers, all related to Test Case Prioritization for SPLs. This systematic mapping study presents a comprehensive classification of the different approaches to test case prioritization for Software Product Lines. This classification can be valuable in identifying the most suitable strategies to address specific challenges and serves as a guide for future research works. In conclusion, this mapping study systematically classifies different approaches to test case prioritization in Software Product Lines. The results of this study can serve as a valuable resource for addressing challenges in SPL testing and provide insights for future research

Implementation Approach of Unit and Integration Testing Method Based on Recent Advancements in Functional Software Testing

Finding bugs and flaws, detecting invalid or inaccurate functionality, and analyzing and certifying the entire softwareproduct all require software testing. We looked at unit testing and integration testing in this project since they are two fundamental stages of software testing and are significantly associated. For both unit and integration testing, a sufficient number of testing methodologies and approaches have been assessed and contrasted, with each implementation system, algorithm, and technique being thoroughly scrutinized. Some of them are effective in finding as many hidden defects as possible while also reducing testing complexity, time, and expense. In this context, we chose sOrTES, a stochastic scheduling support tool that would be utilized for manual integration test cases. The chosen strategy is the most appropriate since empirical evidence reveals that it can prevent around 40% of testing failures while also increasing requirement coverage by 9.6%