Concern localization using information retrieval: An empirical study on Linux kernel

Shaowei Wang*, David Lo, Zhenchang Xing, Lingxiao Jiang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

49 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 18th Working Conference on Reverse Engineering, WCRE 2011
Pages92-96
Number of pages5
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event18th Working Conference on Reverse Engineering, WCRE 2011 - Limerick, Ireland
Duration: 17 Oct 201120 Oct 2011

Publication series

NameProceedings - Working Conference on Reverse Engineering, WCRE
ISSN (Print)1095-1350

Conference

Conference18th Working Conference on Reverse Engineering, WCRE 2011
Country/TerritoryIreland
CityLimerick
Period17/10/1120/10/11

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