Publication Type

Conference Proceeding Article

Publication Date

10-2011

Abstract

Many software maintenance activities need to find code units (functions, files, etc.) that implement a certain concern (features, bugs, etc.). To facilitate such activities, many approaches have been proposed to automatically link code units with concerns described in natural languages, which are termed as concern localization and often employ Information Retrieval (IR) techniques. There has not been a study that evaluates and compares the effectiveness of latest IR techniques on a large dataset. This study fills this gap by investigating ten IR techniques, some of which are new and have not been used for concern localization, on a Linux kernel dataset. The Linux kernel dataset contains more than 1,500 concerns that are linked to over 85,000 C functions. We have evaluated the effectiveness of the ten techniques on recovering the links between the concerns and the implementing functions and ranked the IR techniques based on their precisions on concern localization.

Keywords

concern localization, information retrieval, Linux kernel, mean average precision

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

18th Working Conference on Reverse Engineering (WCRE 2011): Limerick, Ireland, 17-20 October: Proceedings

First Page

92

Last Page

96

ISBN

9781457719486

Identifier

10.1109/WCRE.2011.72

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1109/WCRE.2011.72

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