DocumentCode
637296
Title
Performance evaluation of VSM and LSI models to determine bug reports similarity
Author
Chawla, Indu ; Singh, S.K.
Author_Institution
Dept. of Comput. Sci., Jaypee Inst. of Inf. Technol., Noida, India
fYear
2013
fDate
8-10 Aug. 2013
Firstpage
375
Lastpage
380
Abstract
Bug reports of open source software systems are increasing exponentially. One reason for growing bug reports is that bug reporters do not browse the bug repository before submitting a bug report. There may be some similar bugs already reported: one, which are exactly similar or duplicate and other, which are semantically similar means they may belong to the same software component or files. The information contained in the previously reported similar bugs can be helpful in fixing and resolving the newly reported bugs. In this paper, we applied and compared performance of two information retrieval (IR) models: Vector Space Model (VSM) and Latent Semantic Indexing (LSI), in extracting existing similar bug reports. The performance of these two models have been evaluated based on the Top Ten results retrieved by them for relevant bug reports. Experiments have been conducted on 106 bug reports of three components from Google chrome, browser. Result shows that LSI performs better in most cases in comparison to VSM.
Keywords
information retrieval; pattern matching; performance evaluation; program debugging; public domain software; software management; Google chrome; IR model; LSI models; VSM; browser; bug reports similarity determination; bug repository; information retrieval; latent semantic indexing; open source software systems; performance evaluation; vector space model; Computer bugs; Indexing; Information retrieval; Large scale integration; Semantics; Software; Vectors; Bug report; Latent Semantic Indexing; Vector space model;
fLanguage
English
Publisher
ieee
Conference_Titel
Contemporary Computing (IC3), 2013 Sixth International Conference on
Conference_Location
Noida
Print_ISBN
978-1-4799-0190-6
Type
conf
DOI
10.1109/IC3.2013.6612223
Filename
6612223
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