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Time variance and defect prediction in software projects


Ekanayake, Jayalath; Tappolet, Jonas; Gall, Harald; Bernstein, Abraham (2012). Time variance and defect prediction in software projects. Empirical Software Engineering, 17(4-5):348-389.

Abstract

It is crucial for a software manager to know whether or not one can rely on a bug prediction model. A wrong prediction of the number or the location of future bugs can lead to problems in the achievement of a project’s goals. In this paper we first verify the existence of variability in a bug prediction model’s accuracy over time both visually and statistically. Furthermore, we explore the reasons for such a highvariability over time, which includes periods of stability and variability of prediction quality, and formulate a decision procedure for evaluating prediction models before applying them. To exemplify our findings we use data from four open source projects and empirically identify various project features that influence the defect prediction quality. Specifically, we observed that a change in the number of authors editing a file and the number of defects fixed by them influence the prediction quality. Finally, we introduce an approach to estimate the accuracy of prediction models that helps a project manager decide when to rely on a prediction model. Our findings suggest that one should be aware of the periods of stability and variability of prediction quality and should use approaches such as ours to assess their models’ accuracy in advance.

Abstract

It is crucial for a software manager to know whether or not one can rely on a bug prediction model. A wrong prediction of the number or the location of future bugs can lead to problems in the achievement of a project’s goals. In this paper we first verify the existence of variability in a bug prediction model’s accuracy over time both visually and statistically. Furthermore, we explore the reasons for such a highvariability over time, which includes periods of stability and variability of prediction quality, and formulate a decision procedure for evaluating prediction models before applying them. To exemplify our findings we use data from four open source projects and empirically identify various project features that influence the defect prediction quality. Specifically, we observed that a change in the number of authors editing a file and the number of defects fixed by them influence the prediction quality. Finally, we introduce an approach to estimate the accuracy of prediction models that helps a project manager decide when to rely on a prediction model. Our findings suggest that one should be aware of the periods of stability and variability of prediction quality and should use approaches such as ours to assess their models’ accuracy in advance.

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7 citations in Web of Science®
7 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Date:2012
Deposited On:09 Feb 2012 14:04
Last Modified:07 Dec 2017 11:27
Publisher:Springer
ISSN:1382-3256
Publisher DOI:https://doi.org/10.1007/s10664-011-9180-x
Other Identification Number:merlin-id:3616

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