Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2016

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 are relevant to 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 a multi-abstraction concern localization technique named MULAB. MULAB represents a code unit and a textual description at multiple abstraction levels. Similarity of a textual description and a code unit is now made by considering all these abstraction levels. We combine a vector space model and multiple topic models to compute the similarity and apply a genetic algorithm to infer semi-optimal topic model configurations. We have evaluated our solution on 136 concerns from 8 open source Java software systems. The experimental results show that MULAB outperforms the state-of-art baseline PR, which is proposed by Scanniello et al. in terms of effectiveness and rank.

Keywords

Concern localization, Multi-abstraction, Text retrieval, Topic modeling

Discipline

Databases and Information Systems | Software Engineering

Publication

2016 IEEE International Conference on Software Maintenance and Evolution: ICSME 2016: Proceedings, 2-10 October 2016, Raleigh, North Carolina

First Page

110

Last Page

121

ISBN

9781509038060

Identifier

10.1109/ICSME.2016.51

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

http://doi.ieeecomputersociety.org/10.1109/ICSME.2016.51

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