Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2010

Abstract

Bug repositories are usually maintained in software projects. Testers or users submit bug reports to identify various issues with systems. Sometimes two or more bug reports correspond to the same defect. To address the problem with duplicate bug reports, a person called a triager needs to manually label these bug reports as duplicates, and link them to their "master" reports for subsequent maintenance work. However, in practice there are considerable duplicate bug reports sent daily; requesting triagers to manually label these bugs could be highly time consuming. To address this issue, recently, several techniques have be proposed using various similarity based metrics to detect candidate duplicate bug reports for manual verification. Automating triaging has been proved challenging as two reports of the same bug could be written in various ways. There is still much room for improvement in terms of accuracy of duplicate detection process. In this paper, we leverage recent advances on using discriminative models for information retrieval to detect duplicate bug reports more accurately. We have validated our approach on three large software bug repositories from Firefox, Eclipse, and OpenOffice. We show that our technique could result in 17--31%, 22--26%, and 35--43% relative improvement over state-of-the-art techniques in OpenOffice, Firefox, and Eclipse datasets respectively using commonly available natural language information only.

Keywords

Distribution, Maintenance, Enhancement, Management, Reliability

Discipline

Software Engineering

Research Areas

Cybersecurity

Publication

Proceedings of the 32nd Acm/Ieee International Conference on Software Engineering, Cape Town, South Africa, 2010, May 1 - 8

Volume

1

First Page

45

Last Page

54

ISBN

9781605587196

Identifier

10.1145/1806799.1806811

Publisher

Association for Computing Machinery

City or Country

New York

Additional URL

http://worldcat.org/isbn/9781605587196

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