Multi-Abstraction Concern Localization

Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2013

Abstract

Concern localization refers to the process of locating code units that match a particular textual description. It takes as input textual documents such as bug reports and feature requests and outputs a list of candidate code units that need to be changed to address the bug reports or feature requests. Many information retrieval (IR) based concern localization techniques have been proposed in the literature. These techniques typically represent code units and textual descriptions as a bag of tokens at one level of abstraction, e.g., each token is a word, or each token is a topic. In this work, we propose multi-abstraction concern localization. A code unit and a textual description is represented at multiple abstraction levels. Similarity of a textual description and a code unit, is now made by considering all these abstraction levels. We have evaluated our solution on AspectJ bug reports and feature requests from the iBugs benchmark dataset. The experiment shows that our proposed approach outperforms a baseline approach, in terms of Mean Average Precision, by up to 19.36%.

Keywords

Text Retrieval, Multi-Abstraction, Concern Localization, Topic Model, Latent Dirichlet Allocation

Discipline

Software Engineering

Research Areas

Software Systems

Publication

29th IEEE International Conference on Software Maintenance (ICSM), 22-28 September 2013

First Page

364

Last Page

367

ISSN

1063-6773

Identifier

10.1109/ICSM.2013.48

Publisher

IEEE

City or Country

Eindhoven

Additional URL

http://dx.doi.org/10.1109/ICSM.2013.48

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