NDLI: Effort-Aware Defect Prediction Models

Content Provider	IEEE Xplore Digital Library
Author	Mende, T. Koschke, R.
Copyright Year	2010
Abstract	Defect Prediction Models aim at identifying error-prone modules of a software system to guide quality assurance activities such as tests or code reviews. Such models have been actively researched for more than a decade, with more than 100 published research papers. However, most of the models proposed so far have assumed that the cost of applying quality assurance activities is the same for each module. In a recent paper, we have shown that this fact can be exploited by a trivial classifier ordering files just by their size: such a classifier performs surprisingly good, at least when effort is ignored during the evaluation. When effort is considered, many classifiers perform not significantly better than a random selection of modules. In this paper, we compare two different strategies to include treatment effort into the prediction process, and evaluate the predictive power of such models. Both models perform significantly better when the evaluation measure takes the effort into account.
Starting Page	107
Ending Page	116
File Size	615886
Page Count	10
File Format	PDF
ISBN	9781612843698
ISSN	15345351
e-ISBN	9780769543215
DOI	10.1109/CSMR.2010.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-15
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Testing Prediction algorithms Object oriented modeling Quality assurance Complexity theory Data models Cost-Benefits Defect Prediction Models Evaluation
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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