An Evidential approach on Feature Subset Selection in Software Defect Prediction
|M. Jaikumar1 , V. Kathiresan2|
1 Department of Computer Applications, Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore, India .
2 Department of Computer Applications, Dr.SNS Rajalakshmi College of Arts and Science, Coimbatore, India.
|Correspondence should be addressed to: firstname.lastname@example.org.|
Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 41-49, Dec-2017
Online published on Dec 31, 2017
Copyright © M. Jaikumar , V. Kathiresan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
|View this paper at Google Scholar | DPI Digital Library|
|XML View||PDF Download|
IEEE Style Citation: M. Jaikumar , V. Kathiresan, “An Evidential approach on Feature Subset Selection in Software Defect Prediction”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.41-49, 2017.
MLA Style Citation: M. Jaikumar , V. Kathiresan "An Evidential approach on Feature Subset Selection in Software Defect Prediction." International Journal of Computer Sciences and Engineering 5.12 (2017): 41-49.
APA Style Citation: M. Jaikumar , V. Kathiresan, (2017). An Evidential approach on Feature Subset Selection in Software Defect Prediction. International Journal of Computer Sciences and Engineering, 5(12), 41-49.
|199||120 downloads||62 downloads|
|In software quality research, software defect is a key topic. The characteristic of software attributes influences the performance and effectiveness of the defect prediction model. However this issue is not well explored to the best of our knowledge. So this paper focus on the problem of attribute selection in the context of software defect prediction, we propose a Dempster-Shafer Theory technique with modified combination rule known as Dubois And Prade’s Disjunctive Consensus Rule is adapted for selecting best set of attributes to improve the accuracy of the software defect prediction. Dempster-Shafer Theory (DST) offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The proposed method is evaluated using the data sets from NASA metric data repository.|
|Key-Words / Index Term :|
|Software Defect, prediction, dempster shafer theory, probability, evidence, reliability|
. Y. Chen, P. Du,Xi , X.-H. Shen, “Research on Software Defect Prediction Based on Data Mining”, Computer and Automation Engineering(ICCAE), 2nd International Conference, (2010), vol. 1, pp. 563-567.
. H.Najadat and I.Alsmadi, “Enhance Rule Based Detection for Software Fault Prone Modules”, International Journal of Software Engineering and Its Applications, vol. 6, no. 1, (2012).¬
. A.Okutan, O. T.Yildiz, “Software defect prediction using Bayesian networks”, Empirical Software Engineering, (2014), vol. 19, no. 1, pp. 154-181.
. T. Nu Phyu, “Survey of Classification Techniques in DataMining”, International MultiConference of Engineers and Computer Scientists, (2009); Hong Kong.
. K. Sankar, S. Kannan and P.Jennifer, “Prediction of Code Fault Using Naive Bayes and SVM Classifiers Middle-East Journal of Scientific Research”, vol. 20, no. 1, (2014), pp.108-113.
. Y. Ma,C. Bojan,“Singh:Robust prediction of fault-proneness by random forests ,Software Reliability Engineering”, ISSRE 2004. 15th International Symposium,(2004),pp. 417-428.
. A. Chug1 and S. Dhall1, “Software Defect Prediction Using Supervised Learning Algorithm and Unsupervised Learning Algorithm”, The Next Generation Information Technology Summit (4th International Conference),(2013),pp.1-6.
. S. Agarwal and D.Tomar, “A Feature Selection Based Model for Software Defect Prediction”, International Journal of Advanced Science and Technology, vol.65,(2014), pp. 39-58.
. C.-P.Chang a,*, C.-P.Chu a, Y.-F.Yehb, “Integrating in-process software defect prediction with association mining to discover defect pattern”, Information and Software Technology ,vol. 51, no. 2, (2009), pp. 375-384.
. M. L., H. Zhang, R. Wu, Z.-H. Zhou, “Sample-based software defect prediction with active and semi-supervised learning”, Automated Software Engineering , (2012), vol. 19, no. 2, pp. 201-230
. P.Dhiman, M.C. Manish,“A Clustered Approach to Analyze the Software Quality Using Software Defects, Advanced Computing & Communication Technologies (ACCT)”, 2012 Second International Conference,(2012).
. X. Yuan, H.W. Zhang, S. Ying,F. Wang, “Software defect prediction based on collaborative representation classification”, Proceedings in ICSE Companion 2014, 36th International Conference on Software Engineering, pp. 632-633.
. K. Gao, T..M.Khoshgoftarr, “Software Defect Prediction for high- dimensional and class-imbalanced data”, 23rd International Conference on Software Engineering & Knowledge Engineering (SEKE`2011), Eden Roc Renaissance, (2011)Miami Beach, USA.
. The Global Conference for Wikimedia,(2014); London.
. M. Surendra Naidu, “Classification of Defects in Software Using Decision Tree Algorithm”, International Journal of Engineering Science and Technology (IJEST), (2013).
. Black, Paul E. (2 February 2005). "greedy algorithm". Dictionary of Algorithms and Data Structures. U.S. National Institute of Standards and Technology (NIST). Retrieved 17 August 2012.
. Dempster, A. P. (1967). “Upper and Lower Probabilities Induced by a MultivaluedMapping.” The Annals of Statistics 28: 325-339.
. Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton, NJ, PrincetonUniversity Press
. Klir, G. J. and M. J. Wierman (1998). Uncertainty-Based Information: Elements ofGeneralized Information Theory. Heidelberg, Physica-Verlag.
. Zadeh, L. A. (1986). A Simple View of the Dempster-Shafer Theory of Evidence and itsImplication for the Rule of Combination. The AI Magazine. 7: 85-90.
. Dubois, D. and H. Prade (1986). "A Set-Theoretic View on Belief Functions: LogicalOperations and Approximations by Fuzzy Sets." International Journal of GeneralSystems 12: 193-226.
. Dubois, D. and H. Prade (1992). "On the combination of evidence in variousmathematical frameworks." Reliability Data Collection and Analysis. J. Flammand T. Luisi. Brussels, ECSC, EEC, EAFC: 213-241.