NDLI: A Fuzzy Expert System for Cost-Effective Regression Testing Strategies

Content Provider	IEEE Xplore Digital Library
Author	Schwartz, A. Hyunsook Do
Copyright Year	2013
Description	Author affiliation: North Dakota State U., Fargo, ND, USA (Schwartz, A.; Hyunsook Do)
Abstract	Different testing environments and software change characteristics can affect the choice of regression testing techniques. In our prior work, we developed adaptive regression testing (ART) strategies to investigate this problem. While the ART strategies showed promising results, we also found that the multiple criteria decision making processes required for the ART strategies are time-consuming, often inaccurate and inconsistent, and limited in their scalability. To address these issues, in this research, we develop and empirically study a fuzzy expert system (FESART) to aid decision makers in choosing the most cost-effective technique for a particular software version. The results of our study show that FESART is consistently more cost-effective than the previously proposed ART strategies. One of the biggest contributors to FESART being more cost-effective is the reduced time required to apply the strategy. This contribution has significant impact because a strategy that is less time-consuming will be easier for researchers and practitioners to adopt, and will provide even greater cost-savings for regression testing sessions.
Starting Page	1
Ending Page	10
File Size	429855
Page Count	10
File Format	PDF
ISBN	9780769549811
ISSN	10636773
DOI	10.1109/ICSM.2013.11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-22
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Expert systems Testing Subspace constraints Fuzzy logic Software Decision making Fuzzy sets empirical studies Regression testing test case prioritization adaptive regression testing strategy AHP fuzzy AHP
Content Type	Text
Resource Type	Article

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