Risk-based test case prioritization using a fuzzy expert system

• Published in: Information and software technology Vol. 69; pp. 1 - 15
• Main Authors: Hettiarachchi, Charitha, Do, Hyunsook, Choi, Byoungju
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-01-2016; Elsevier Science Ltd
• Subjects: Automation; Effectiveness studies; Empirical study; Expert systems; Fault diagnosis; Fuzzy expert system; Fuzzy logic; Regression testing; Requirements analysis; Requirements risks-based testing; Risk assessment; Software engineering; Test case prioritization; Regression testing; Fuzzy expert system; Requirements risks-based testing; Test case prioritization; Empirical study
• ISSN: 0950-5849; 1873-6025
• DOI: 10.1016/j.infsof.2015.08.008

Context: The use of system requirements and their risks enables software testers to identify more important test cases that can reveal the faults associated with system components.
Objective:The goal of this research is to make the requirements risk estimation process more systematic and precise by reducing subjectivity using a fuzzy expert system. Further, we provide empirical results that show that our proposed approach can improve the effectiveness of test case prioritization.
Method: In this research, we used requirements modification status, complexity, security, and size of the software requirements as risk indicators and employed a fuzzy expert system to estimate the requirements risks. Further, we employed a semi-automated process to gather the required data for our approach and to make the risk estimation process less subjective.
Results:The results of our study indicated that the prioritized tests based on our new approach can detect faults early, and also the approach can be effective at finding more faults earlier in the high-risk system components compared to the control techniques.
Conclusion: We proposed an enhanced risk-based test case prioritization approach that estimates requirements risks systematically with a fuzzy expert system. With the proposed approach, testers can detect more faults earlier than with other control techniques. Further, the proposed semi-automated, systematic approach can easily be applied to industrial applications and can help improve regression testing effectiveness.