View : 13 Download: 0

Risk-based test case prioritization using a fuzzy expert system

Title
Risk-based test case prioritization using a fuzzy expert system
Authors
Hettiarachchi, CharithaDo, HyunsookChoi, Byoungju
Ewha Authors
최병주
SCOPUS Author ID
최병주scopus
Issue Date
2016
Journal Title
INFORMATION AND SOFTWARE TECHNOLOGY
ISSN
0950-5849JCR Link1873-6025JCR Link
Citation
vol. 69, pp. 1 - 15
Keywords
Regression testingRequirements risks-based testingTest case prioritizationFuzzy expert systemEmpirical study
Publisher
ELSEVIER SCIENCE BV
Indexed
SCIE; SCOPUS WOS scopus
Abstract
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. (C) 2015 Elsevier B.V. All rights reserved.
DOI
10.1016/j.infsof.2015.08.008
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE