Multi-Abstraction Concern Localization

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 request...

Full description

Saved in:
Bibliographic Details
Main Authors: DUY, Tien-Duy B., WANG, Shaowei, LO, David
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2013
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/2019
http://dx.doi.org/10.1109/ICSM.2013.48
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-3018
record_format dspace
spelling sg-smu-ink.sis_research-30182018-07-13T03:33:47Z Multi-Abstraction Concern Localization DUY, Tien-Duy B. WANG, Shaowei LO, David 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%. 2013-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2019 info:doi/10.1109/ICSM.2013.48 http://dx.doi.org/10.1109/ICSM.2013.48 http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Text Retrieval Multi-Abstraction Concern Localization Topic Model Latent Dirichlet Allocation Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Text Retrieval
Multi-Abstraction
Concern Localization
Topic Model
Latent Dirichlet Allocation
Software Engineering
spellingShingle Text Retrieval
Multi-Abstraction
Concern Localization
Topic Model
Latent Dirichlet Allocation
Software Engineering
DUY, Tien-Duy B.
WANG, Shaowei
LO, David
Multi-Abstraction Concern Localization
description 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%.
format text
author DUY, Tien-Duy B.
WANG, Shaowei
LO, David
author_facet DUY, Tien-Duy B.
WANG, Shaowei
LO, David
author_sort DUY, Tien-Duy B.
title Multi-Abstraction Concern Localization
title_short Multi-Abstraction Concern Localization
title_full Multi-Abstraction Concern Localization
title_fullStr Multi-Abstraction Concern Localization
title_full_unstemmed Multi-Abstraction Concern Localization
title_sort multi-abstraction concern localization
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/2019
http://dx.doi.org/10.1109/ICSM.2013.48
_version_ 1770571766389800960