NDLI: A novel fuzzy classification to enhance software regression testing

Content Provider	IEEE Xplore Digital Library
Author	Zhiwei Xu Yi Liu Kehan Gao
Copyright Year	2013
Description	Author affiliation: Eastern Connecticut State Univ., Willimantic, CT, USA (Kehan Gao) \|\| Univ. of Michigan, Dearborn, MI, USA (Zhiwei Xu) \|\| Georgia Coll. State Univ., Milledgeville, GA, USA (Yi Liu)
Abstract	An effective system regression testing for consecutive releases of very large software systems, such as modern telecommunications systems, depends considerably on the selection of test cases for execution. Classification models can classify, early in the test planning phase, those test cases that are likely to detect faults in the upcoming regression test. Due to the high uncertainties in regression test, classification models based on fuzzy logic are very useful. Recently, methods have been proposed for automatically generating fuzzy if-then rules by applying complicated rule generation procedures to numerical data. In this research, we introduce and demonstrate a new rule-based fuzzy classification (RBFC) modeling approach as a method for identifying high effective test cases. The modeling approach, based on test case metrics and the proposed rule generation technique, is applied to extracting fuzzy rules from numerical data. In addition, it also provides a convenient way to modify rules according to the costs of different misclassification errors. We illustrate our modeling technique with a case study of large-scale industrial software systems and the results showed that test effectiveness and efficiency was significantly improved.
Starting Page	53
Ending Page	58
File Size	177987
Page Count	6
File Format	PDF
ISBN	9781467358958
DOI	10.1109/CIDM.2013.6597217
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Educational institutions Software Numerical models Equations Testing Software engineering
Content Type	Text
Resource Type	Article

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