DocumentCode
2348923
Title
Supporting Feature-Level Software Maintenance
Author
Revelle, Meghan
Author_Institution
Comput. Sci. Dept., Coll. of William & Mary, Williamsburg, VA, USA
fYear
2009
fDate
13-16 Oct. 2009
Firstpage
287
Lastpage
290
Abstract
The proposed research defines data fusion approaches to support software maintenance tasks at the feature level. Static, dynamic, and textual sources of information are combined to locate the implementation of features in source code. Structural and textual source code information is used to define feature coupling metrics to aid feature-level impact analysis. This paper provides details on the proposed approaches and evaluation strategies as well as some preliminary results.
Keywords
software maintenance; software metrics; dynamic source; evaluation strategies; feature coupling metrics; feature-level impact analysis; feature-level software maintenance; source code; static source; textual source; Area measurement; Computer bugs; Computer science; Educational institutions; Information analysis; Information resources; Performance analysis; Reverse engineering; Scattering; Software maintenance; data fusion; feature coupling; feature location;
fLanguage
English
Publisher
ieee
Conference_Titel
Reverse Engineering, 2009. WCRE '09. 16th Working Conference on
Conference_Location
Lille
ISSN
1095-1350
Print_ISBN
978-0-7695-3867-9
Type
conf
DOI
10.1109/WCRE.2009.43
Filename
5328789
Link To Document