A Subtractive Clustering Based Approach for Early Prediction of Fault Proneness in Software Modules
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A Subtractive Clustering Based Approach for Early Prediction of Fault Proneness in Software Modules

Authors: Ramandeep S. Sidhu, Sunil Khullar, Parvinder S. Sandhu, R. P. S. Bedi, Kiranbir Kaur

Abstract:

In this paper, subtractive clustering based fuzzy inference system approach is used for early detection of faults in the function oriented software systems. This approach has been tested with real time defect datasets of NASA software projects named as PC1 and CM1. Both the code based model and joined model (combination of the requirement and code based metrics) of the datasets are used for training and testing of the proposed approach. The performance of the models is recorded in terms of Accuracy, MAE and RMSE values. The performance of the proposed approach is better in case of Joined Model. As evidenced from the results obtained it can be concluded that Clustering and fuzzy logic together provide a simple yet powerful means to model the earlier detection of faults in the function oriented software systems.

Keywords: Subtractive clustering, fuzzy inference system, fault proneness.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331265

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References:


[1] Fenton N.E. and Pfleeger S.L. (1997), "Software Metrics: A Rigorous and Practical Approach". PWS publishing Company: ITP, Boston, MA, 2nd edition, pp.132-145.
[2] Jiang Y., Cukic B. and Menzies T. (2007), "Fault Prediction Using Early Lifecycle Data". ISSRE 2007, the 18th IEEE Symposium on Software Reliability Engineering, IEEE Computer Society, Sweden, pp. 237-246.
[3] Nizar Grira, Michel Crucianu, Nozha Boujemaa, "Unsupervised and Semi-supervised Clustering: a Brief Survey", A Review of Machine Learning Techniques for Processing Multimedia Content, Report of the MUSCLE European Network of Excellence (6th Framework Programme), 2005 URL: www.rocq.inria.fr/~crucianu/src/BriefSurveyClustering.pdf
[4] http://en.wikipedia.org/wiki/Cluster_analysis
[5] Toon Calders, "Data Mining Clustering", URL: wwwis.win.tue.nl/~tcalders/teaching/.../slides/DM09-07-Clustering.pdf
[6] http://www.scholarpedia.org/article/Fuzzy_C-means_cluster_analysis
[7] home.dei.polimi.it/matteucc/Clustering/tutorial_html/
[8] scianta.com/technology/machinelearning.htm
[9] Challagulla, V.U.B. , Bastani, F.B. , I-Ling Yen , Paul,( 2005) "Empirical assessment of machine learning based software defect prediction techniques", 10th IEEE International Workshop on Object- Oriented Real-Time Dependable Systems, WORDS 2005, 2-4 Feb 2005, pp. 263-270.
[10] http://mdp.ivv.nasa.gov
[11] Khaled Hammouda, "A Comparative Study of Data Clustering Techniques", SYDE 625: Tools of Intelligent Systems Design. Course Project. Unpublished Aug 2000
[12] www.mathworks.com
[13] S. Chiu, "Fuzzy Model Identification Based on Cluster Estimation," J. of Intelligent & Fuzzy Systems, Vol. 2, No. 3, 1994.