Header

UZH-Logo

Maintenance Infos

Comparing fine-grained source code changes and code churn for bug prediction


Giger, Emanuel; Pinzger, Martin; Gall, Harald (2011). Comparing fine-grained source code changes and code churn for bug prediction. In: 8th working conference on Mining software repositories, Honolulu, HI USA, 21 May 2011 - 22 May 2011, 83-92.

Abstract

A significant amount of research effort has been dedicated to learning prediction models that allow project managers to efficiently allocate resources to those parts of a software system that most likely are bug-prone and therefore critical. Prominent measures for building bug prediction models are product measures, e.g., complexity or process measures, such as code churn. Code churn in terms of lines modified (LM) and past changes turned out to be significant indicators of bugs. However, these measures are rather imprecise and do not reflect all the detailed changes of particular source code entities during maintenance activities. In this paper, we explore the advantage of using fine-grained source code changes (SCC) for bug prediction. SCC captures the exact code changes and their semantics down to statement level. We present a series of experiments using different machine learning algorithms with a dataset from the Eclipse platform to empirically evaluate the performance of SCC and LM. The results show that SCC outperforms LM for learning bug prediction models.

Abstract

A significant amount of research effort has been dedicated to learning prediction models that allow project managers to efficiently allocate resources to those parts of a software system that most likely are bug-prone and therefore critical. Prominent measures for building bug prediction models are product measures, e.g., complexity or process measures, such as code churn. Code churn in terms of lines modified (LM) and past changes turned out to be significant indicators of bugs. However, these measures are rather imprecise and do not reflect all the detailed changes of particular source code entities during maintenance activities. In this paper, we explore the advantage of using fine-grained source code changes (SCC) for bug prediction. SCC captures the exact code changes and their semantics down to statement level. We present a series of experiments using different machine learning algorithms with a dataset from the Eclipse platform to empirically evaluate the performance of SCC and LM. The results show that SCC outperforms LM for learning bug prediction models.

Statistics

Citations

Altmetrics

Downloads

1 download since deposited on 10 Feb 2012
0 downloads since 12 months
Detailed statistics

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:22 May 2011
Deposited On:10 Feb 2012 12:15
Last Modified:14 Aug 2017 09:28
Publisher:Association for Computing Machinery
Series Name:Proceedings of the 8th Working Conference on Mining Software Repositories
ISBN:978-1-4503-0574-7
Publisher DOI:https://doi.org/10.1145/1985441.1985456
Related URLs:http://2011.icse-conferences.org/
Other Identification Number:merlin-id:3851

Download