Towards More Accurate Retrieval of Duplicate Bug Reports

Publication Type

Conference Proceeding Article

Publication Date

11-2011

Abstract

In a bug tracking system, different testers or users may submit multiple reports on the same bugs, referred to as duplicates, which may cost extra maintenance efforts in triaging and fixing bugs. In order to identify such duplicates accurately, in this paper we propose a retrieval function (REP) to measure the similarity between two bug reports. It fully utilizes the information available in a bug report including not only the similarity of textual content in summary and description fields, but also similarity of non-textual fields such as product, component, version, etc. For more accurate measurement of textual similarity, we extend BM25F – an effective similarity formula in information retrieval community, specially for duplicate report retrieval. Lastly we use a two-round stochastic gradient descent to automatically optimize REP for specific bug repositories in a supervised learning manner. We have validated our technique on three large software bug repositories from Mozilla, Eclipse and OpenOffice. The experiments show 10–27% relative improvement in recall rate@k and 17–23% relative improvement in mean average precision over our previous model. We also applied our technique to a very large dataset consisting of 209,058 reports from Eclipse, resulting in a recall rate@k of 37–71% and mean average precision of 47%.

Discipline

Software Engineering

Research Areas

Software Systems

Publication

ASE 2011: 26th IEEE/ACM International Conference on Automated Software Engineering, 6-10 November 2011, Lawrence, KS: Proceedings

ISBN

9781457716386

Identifier

10.1109/ASE.2011.6100061

Publisher

IEEE

City or Country

Lawrence, USA

Additional URL

https://doi.org/10.1109/ASE.2011.6100061

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