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Method-level bug prediction


Giger, Emanuel; D'Ambros, Marco; Pinzger, Martin; Gall, Harald (2012). Method-level bug prediction. In: International Symposium on Empirical Software Engineering and Measurement, Lund, Sweden, 19 September 2012 - 20 September 2012, 171-180.

Abstract

Researchers proposed a wide range of approaches to build effective bug prediction models that take into account multiple aspects of the software development process. Such models achieved good prediction performance, guiding developers towards those parts of their system where a large share of bugs can be expected. However, most of those approaches predict bugs on file-level. This often leaves developers with a considerable amount of effort to examine all methods of a file until a bug is located. This particular problem is reinforced by the fact that large files are typically predicted as the most bug-prone. In this paper, we present bug prediction models at the level of individual methods rather than at file-level. This increases the granularity of the prediction and thus reduces manual inspection efforts for developers. The models are based on change metrics and source code metrics that are typically used in bug prediction. Our experiments---performed on 21 Java open-source (sub-)systems---show that our prediction models reach a precision and recall of 84% and 88%, respectively. Furthermore, the results indicate that change metrics significantly outperform source code metrics.

Abstract

Researchers proposed a wide range of approaches to build effective bug prediction models that take into account multiple aspects of the software development process. Such models achieved good prediction performance, guiding developers towards those parts of their system where a large share of bugs can be expected. However, most of those approaches predict bugs on file-level. This often leaves developers with a considerable amount of effort to examine all methods of a file until a bug is located. This particular problem is reinforced by the fact that large files are typically predicted as the most bug-prone. In this paper, we present bug prediction models at the level of individual methods rather than at file-level. This increases the granularity of the prediction and thus reduces manual inspection efforts for developers. The models are based on change metrics and source code metrics that are typically used in bug prediction. Our experiments---performed on 21 Java open-source (sub-)systems---show that our prediction models reach a precision and recall of 84% and 88%, respectively. Furthermore, the results indicate that change metrics significantly outperform source code metrics.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:20 September 2012
Deposited On:29 Jan 2013 09:39
Last Modified:07 Dec 2017 19:03
Publisher:Association for Computing Machinery
ISBN:978-1-4503-1056-7
Additional Information:© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement , Pages 171-180 (2012), http://doi.acm.org/10.1145/2372251.2372285
Publisher DOI:https://doi.org/10.1145/2372251.2372285
Other Identification Number:merlin-id:7102

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