NDLI: Clustering and Metrics Thresholds Based Software Fault Prediction of Unlabeled Program Modules

Content Provider	IEEE Xplore Digital Library
Author	Catal, C. Sevim, U. Diri, B.
Copyright Year	2009
Abstract	Predicting the fault-proneness of program modules when the fault labels for modules are unavailable is a practical problem frequently encountered in the software industry. Because fault data belonging to previous software version is not available, supervised learning approaches can not be applied, leading to the need for new methods, tools, or techniques. In this study, we propose a clustering and metrics thresholds based software fault prediction approach for this challenging problem and explore it on three datasets, collected from a Turkish white-goods manufacturer developing embedded controller software. Experiments reveal that unsupervised software fault prediction can be automated and reasonable results can be produced with techniques based on metrics thresholds and clustering. The results of this study demonstrate the effectiveness of metrics thresholds and show that the standalone application of metrics thresholds (one-stage) is currently easier than the clustering and metrics thresholds based (two-stage) approach because the selection of cluster number is performed heuristically in this clustering based method.
Starting Page	199
Ending Page	204
File Size	302751
Page Count	6
File Format	PDF
ISBN	9781424437702
DOI	10.1109/ITNG.2009.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	software fault prediction Humans Predictive models unlabeled program modules Application software Information technology Embedded software unsupervised learning metrics thresholds Supervised learning Machine learning Computer industry clustering Manufacturing Software tools
Content Type	Text
Resource Type	Article

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