Title
Improving feature location using structural similarity and iterative graph mapping
Abstract
Locating program element(s) relevant to a particular feature is an important step in efficient maintenance of a software system. The existing feature location techniques analyse each feature independently and perform a one-time analysis after being provided an initial input. As a result, these techniques are sensitive to the quality of the input. In this paper, we propose to address the above issues in feature location using an iterative context-aware approach. The underlying intuition is that features are not independent of each other, and the structure of source code resembles the structure of features. The distinguishing characteristics of the proposed approach are: (1) it takes into account the structural similarity between a feature and a program element to determine feature-element relevance and (2) it employs an iterative process to propagate the relevance of the established mappings between a feature and a program element to the neighbouring features and program elements. We evaluate our approach using two different systems, DirectBank, a small-scale industry financial system, and Linux kernel, a large-scale open-source operating system. Our evaluation suggests that the proposed approach is more robust and can significantly increase the recall of feature location with only a minor decrease of precision.
Year
DOI
Venue
2013
10.1016/j.jss.2012.10.270
Journal of Systems and Software
Keywords
Field
DocType
structural similarity,different system,particular feature,iterative graph mapping,program element,improving feature location,feature location,iterative context-aware approach,locating program element,financial system,existing feature location technique,neighbouring feature,information retrieval
Data mining,Pattern recognition,Iterative and incremental development,Feature (computer vision),Computer science,Source code,Software system,Feature model,Structural similarity,Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,Linux kernel
Journal
Volume
Issue
ISSN
86
3
0164-1212
Citations 
PageRank 
References 
10
0.51
22
Authors
5
Name
Order
Citations
PageRank
Xin Peng159967.59
Zhenchang Xing2138787.95
Xi Tan37314.27
Yijun Yu41558113.40
Wenyun Zhao552654.45