DocumentCode :
650710
Title :
Improving Feature Location by Enhancing Source Code with Stereotypes
Author :
Alhindawi, Nouh ; Dragan, Natalia ; Collard, Michael L. ; Maletic, Jonathan I.
Author_Institution :
Dept. of Comput. Sci., Kent State Univ., Kent, OH, USA
fYear :
2013
fDate :
22-28 Sept. 2013
Firstpage :
300
Lastpage :
309
Abstract :
A novel approach to improve feature location by enhancing the corpus (i.e., source code) with static information is presented. An information retrieval method, namely Latent Semantic Indexing (LSI), is used for feature location. Adding stereotype information to each method/function enhances the corpus. Stereotypes are terms that describe the abstract role of a method, for example get, set, and predicate are well-known method stereotypes. Each method in the system is automatically stereotyped via a static-analysis approach. Experimental comparisons of using LSI for feature location with, and without, stereotype information are conducted on a set of open-source systems. The results show that the added information improves the recall and precision in the context of feature location. Moreover, the use of stereotype information decreases the total effort that a developer would need to expend to locate relevant methods of the feature.
Keywords :
indexing; information retrieval; program diagnostics; public domain software; source coding; LSI; feature location; information retrieval; latent semantic indexing; open-source systems; source code; static-analysis approach; Context; Feature extraction; Large scale integration; Open source software; Semantics; Standards; Taxonomy; feature location; information retrieval; method stereotypes; program comprehension; software maintenance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software Maintenance (ICSM), 2013 29th IEEE International Conference on
Conference_Location :
Eindhoven
ISSN :
1063-6773
Type :
conf
DOI :
10.1109/ICSM.2013.41
Filename :
6676901
Link To Document :
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