An Innovative Approachto Perform Software Defect Prediction

Authors

  • Prakash Behera Dept. of CSE, Claret College, Jalahalli, Bengaluru and Research Scholar, JJTU, Rajasthan, India
  • Chimaya Dash Dept. of CSE, Claret College, Jalahalli, Bengaluru and Research Scholar, JJTU, Rajasthan, India
  • R Chandramma Dept. of Computer Science and Engineering, VKIT, Bengaluru and Research Scholar - EWIT, VTU Belgaum, India 4Department of Computer Science and Engineering, EWIT, Bengaluru, India
  • Prakash Behera Dept. of Computer Science and Engineering, VKIT, Bengaluru and Research Scholar - EWIT, VTU Belgaum, India
  • Aditya Pai H Dept. of Computer Science and Engineering, K.S. Institute of Technology, Bengaluru and Research Scholar, VTU Belgaum, Bengaluru, India
  • Piyush Kumar Pareek Department of Computer Science and Engineering, EWIT, Bengaluru, India

Keywords:

Software defect predictio, Machine learning, Decision tree, Fuzzy theory

Abstract

identifying defective substances from existing software frameworks is an issue of extraordinary significance for expanding both software quality and the proficiency of software testing related exercises. We present in this paper a novel methodology for anticipating software defects utilizing fuzzy decision trees. Through the fuzzy methodology we plan to all the more likely adapt to clamor and loose data. A fuzzy decision tree will be prepared to recognize whether a software module is defective or not. Two open source software frameworks are utilized for tentatively assessing our methodology. The acquired outcomes feature that the fuzzy decision tree approach beats the non-fuzzy one on practically all contextual investigations utilized for assessment. Contrasted with the methodologies utilized in the writing, the fuzzy decision tree classifier is appeared to be more effective than the greater part of the other machine learning-based classifiers.

References

[1] R. hua Chang, X. Mu, and L. Zhang, “Software defect prediction using non-negative matrix factorization.” JSW, vol. 6, no. 11, pp. 2114–2120, 2011.

[2] J. Aranda and G. Venolia, “The secret life of bugs: Going past the errors and omissions in software repositories,” in Proceedings of the 31st International Conference on Software Engineering, ser. ICSE ’09. Washington, DC, USA: IEEE Computer Society, pp. 298–308, 2009.

[3] S. Kim, H. Zhang, R. Wu, and L. Gong, “Dealing with noise in defect prediction,” in Proceedings of the 33rd International Conference on Software Engineering, ser. ICSE ’11. New York, NY, USA: ACM, 2011, pp. 481–490, 2011.

[4] Z. Marian, G. Czibula, I.-G. Czibula, and S. Sotoc, “Software defect detection using self-organizing maps,” Studia Universitatis Babes-Bolyai, Informatica, vol. LX, no. 2, pp. 55–69, 2015.

[5] M. Umanol, H. Okamoto, I. Hatono, H. Tamura, F. Kawachi, S. Umedzu, and J. Kinoshita, “Fuzzy decision trees by fuzzy id3 algorithm and its application to diagnosis systems,” in Proceedings of the Third IEEE Conference on Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence.,,, pp. 2113–2118 vol.3, 1994.

[6] T. M. Mitchell, Machine learning. McGraw-Hill, Inc. New York, USA, 1997.

[7] C. Z. Janikow, “Fuzzy decision trees: issues and methods,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 28, no. 1, pp. 1–14, 1998.

[8] T. Hall, S. Beecham, D. Bowes, D. Gray, and S. Counsell, “A systematic literature review on fault prediction performance in software engineering,” IEEE Transactions on Software Engineering, vol. 38, no. 6, pp. 1276–1304, 2011.

[9] A. Okutan and O. T. Yildiz, “Software defect prediction using Bayesian networks,” Empirical Software Engineering, vol. 19, no. 1, pp. 154–181, 2014.

[10] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The weka data mining software: An update,” SIGKDD Explorations, vol. 11, no. 1,2009.

[11] R. Malhotra, “A defect prediction model for open source software,” in Proceedings of the World Congress on Engineering, vol. II, July 2012.

[12] M. Chen and Y. Ma, “An empirical study on predicting defect numbers,” in Proceedings of the 27th International Conference on Software Engineering and Knowledge Engineering, pp. 397–402, 2015.

[13] G. Canfora, A. D. Lucia, M. D. Penta, R. Oliveto, A. Panichella, and S. Panichella, “Multi-objective cross-project defect prediction,” in Proceedings of the 6th International Conference on Software Testing, Verification and Validation, pp. 252–261, 2013.

[14] G. Scanniello, C. Gravino, A. Marcus, and T. Menzies, “Class level fault prediction using software clustering,” in Proceedings of the 28th IEEE/ACM International Conference on Automated Software Testing, pp. 640–645, 2013.

[15] D. Radjenovi´c, M. Heriˇcko, R. Torkar, and A. ˇ Zivkoviˇc, “Software fault prediction metrics: A systematic literature review,” Information and Software Technology, vol. 55, no. 8, pp. 1397–1418, 2013.

[16] T. G. S.Fil´o, M. A. S. Bigonha, and K. A. M. Ferreira, “A catalogue of thresholds for object-oriented software metrics,” in First International Conference on Advances and Trends in Software Engineering, 2015.

[17] “Tera-promise repository,” http://openscience.us/repo/.

[18] W. Liu, S. Chawla, D. A. Cieslak, and N. V. Chawla, “N.: A robust decision tree algorithms for imbalanced data sets,” in In: Proceedings of the Tenth SIAM International Conference on Data Mining, pp.766–777, 2010.

[19] G. Wahba, Y. Lin, and H. Zhang, “GACV for support vector machines, or, another way to look at margin-like quantities,” Advances in Large Margin classifiers, pp. 297–309, 2000.

[20] D. M. W. Powers, “Evaluation: From precision, recall and f-measure to roc., informedness, markedness & correlation,” Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63, 2011.

[21] T. Fawcett, “An introduction to ROC analysis,” Pattern Recogn. Lett., vol. 27, no. 8, pp. 861–874, 2006.

[22] G. D. Boetticher, “Advances in Machine Learning Applications in Software Engineering”. IGI Global, ch. Improving the Credibility of Machine Learner Models in Software Engineering, 2007.

[23] N. V.Chawla, “Data Mining and Knowledge Discovery Handbook”, Springer US, ch. Data Mining for imbalanced datasets: an overview, 2010/

[24] “Orange data mining,” http://orange.biolab.si/.

[25] N. V. Chawla, K.W. Bowyer, L. O. Hall, andW. P. Kegelmeyer, “Smote: Synthetic minority over-sampling technique,” J. Artif. Int. Res., vol. 16, no. 1, pp. 321–357, 2002.

[26] G. Serban, A. Cˆampan, and I. G. Czibula, “A programming interface for finding relational association rules,” International Journal of Computers, Communications & Control, vol. I, no. S., pp. 439–444, June 2006.

Downloads

Published

2025-11-26

How to Cite

[1]
P. Behera, C. Dash, R. Chandramma, P. B., A. P. H, and P. K. Pareek, “An Innovative Approachto Perform Software Defect Prediction”, Int. J. Comp. Sci. Eng., vol. 7, no. 15, pp. 296–303, Nov. 2025.

Issue

Vol. 7 No. 15 (2019): IJCSE Special Issue May Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.