Research Article
Severity Assessment of Software Defect Reports using Text Classification
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 83 - Issue 11 |
| Published: December 2013 |
| Authors: Ruchika Malhotra, Nidhi Kapoor, Rishabh Jain, Sahaj Biyani |
10.5120/14492-2622
|
Ruchika Malhotra, Nidhi Kapoor, Rishabh Jain, Sahaj Biyani . Severity Assessment of Software Defect Reports using Text Classification. International Journal of Computer Applications. 83, 11 (December 2013), 13-16. DOI=10.5120/14492-2622
@article{ 10.5120/14492-2622,
author = { Ruchika Malhotra,Nidhi Kapoor,Rishabh Jain,Sahaj Biyani },
title = { Severity Assessment of Software Defect Reports using Text Classification },
journal = { International Journal of Computer Applications },
year = { 2013 },
volume = { 83 },
number = { 11 },
pages = { 13-16 },
doi = { 10.5120/14492-2622 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2013
%A Ruchika Malhotra
%A Nidhi Kapoor
%A Rishabh Jain
%A Sahaj Biyani
%T Severity Assessment of Software Defect Reports using Text Classification%T
%J International Journal of Computer Applications
%V 83
%N 11
%P 13-16
%R 10.5120/14492-2622
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Defect severity assessment is essential in order to allocate testing resources and effectively plan testing activities. In this paper, we use text classification techniques to predict and assess the severity of defects. The results are based on defect description of issue requirements obtained from NASA project. We have used Support Vector Machine technique to predict defect severity from issue reports.
References
- Martin Pavlov ILIEV, "A method for automated prediction of defect severity using ontologies" in Master's thesis, LIACS, Leiden University, Logica Netherlands, 2012
- Tim Menzies and Andrian Marcus, "Automated severity assessment of software defect reports", 2007
- Konstantin Mertsalov and Michael McCreary, "Document classification with support vector machines", January 2009
- Principles of data mining by Max Barmer, Springer
- Yogesh Singh, Arvinder Kaur, Ruchika Malhotra ,"Software Fault Proneness Prediction Using Support Vector Machines"
- Y. H Li and A. K Jain, "Classification of text documents",1998
- Thorsten Joachims, "Text categorization wtith Support Vector Machines: learning with relevant features"
- Shrikanth Shankar and George Karypis, "A feature weight adjustment algorithm for document categorization"
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