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Evaluation of Fault Proneness of Modules in Open Source Software Systems Using k-NN Clustering


Affiliations
1 Department of Computer Science and IT, Rayat Institute of Engineering and IT, Railmajra, Ropar, India
2 Rayat Institute of Engineering and IT, Railmajra, Ropar, India
3 Punjab Technical University, Jalandhar, India
 

Fault-proneness of a software module is the probability that the module contains faults. A correlation exists between the fault-proneness of the software and the measurable attributes of the code (i.e. the static metrics) and of the testing (i.e. the dynamic metrics). Early detection of fault-prone software components enables verification experts to concentrate their time and resources on the problem areas of the software system under development. This paper introduces the evaluation of the fault proneness of modules in open source software system using k-NN clustering algorithm with Object-Oriented metrics and ck metrics. The contribution of this paper is that it has used Metric values of JEdit open source software for generation of the rules for the classification of software modules in the categories of Faulty and non faulty modules and thereafter empirically validation is performed. The results shows that algorithm approach can be used for finding the fault proneness in object oriented software components.

Keywords

k-NN Clustering Algorithms, Software Fault, Classification, Object Oriented Metrics, CK Metrics.
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  • Evaluation of Fault Proneness of Modules in Open Source Software Systems Using k-NN Clustering

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Authors

Harish Kundra
Department of Computer Science and IT, Rayat Institute of Engineering and IT, Railmajra, Ropar, India
Anamika Sharma
Rayat Institute of Engineering and IT, Railmajra, Ropar, India
R. P. S. Bedi
Punjab Technical University, Jalandhar, India

Abstract


Fault-proneness of a software module is the probability that the module contains faults. A correlation exists between the fault-proneness of the software and the measurable attributes of the code (i.e. the static metrics) and of the testing (i.e. the dynamic metrics). Early detection of fault-prone software components enables verification experts to concentrate their time and resources on the problem areas of the software system under development. This paper introduces the evaluation of the fault proneness of modules in open source software system using k-NN clustering algorithm with Object-Oriented metrics and ck metrics. The contribution of this paper is that it has used Metric values of JEdit open source software for generation of the rules for the classification of software modules in the categories of Faulty and non faulty modules and thereafter empirically validation is performed. The results shows that algorithm approach can be used for finding the fault proneness in object oriented software components.

Keywords


k-NN Clustering Algorithms, Software Fault, Classification, Object Oriented Metrics, CK Metrics.