A Hybrid Neuro Fuzzy Approach for Bug Prediction using Software Metrics

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2016 by IJETT Journal
Volume-38 Number-2
Year of Publication : 2016
Authors : Aditi Thakur, Dr. Ajay Goel
DOI :  10.14445/22315381/IJETT-V38P217


Aditi Thakur, Dr. Ajay Goel"A Hybrid Neuro Fuzzy Approach for Bug Prediction using Software Metrics", International Journal of Engineering Trends and Technology (IJETT), V38(2),85-92 August 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Software quality is an important factor since software systems are playing a key role in today’s world. There are several perspectives within the field on software quality measurement. This measurement is frequently used so many defects which can cause crashes, failures, or security breaches encountered in the software. Testing the software for such defect is essential to enhance the quality. However, due to the increase in intricacy of software manual testing was becoming extremely time consuming task and some automatic supporting tools have been developed. One such supporting tool is defect prediction models. Some defect prediction models can be found in the literature and most of them share a common procedure to develop the models. In general, the models’ development procedure indirectly assumes that underlying data distribution of software systems is relatively stable over time. But, this assumption is not necessarily true and consequently, the reliability of those models is doubtful at some points in time.


[1] A. E. Hassan, “Predicting faults using the complexity of code changes,” in Proceedings of ICSE 2009, 2009, pp.78–88, 2009.
[2] Emanuel Giger, Martin Pinzger, Harald C. Gall, “Comparing Fine-Grained Source Code Changes And Code Churn For Bug Prediction”, 8th IEEE Working Conference on Mining Software Repositories, ISBN: 978-1-4503-0574-7, May 2011.
[3] Yuan Tian, David Lo, and Chengnian Sun, “Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction”, 19th IEEE Working Conference on Reverse Engineering, ISSN: 10951350, 2012.
[4] Shivkumar Shivaji, E. James Whitehead, Ram Akella, Sunghun Kim, “Reducing Features to Improve Code Change-Based Bug Prediction”, IEEE Transaction on Software Engineering, VOL. 39, NO. 4, APRIL 2013.
[5] Phiradet Bangcharoensap, Akinori Ihara, Yasutaka Kamei, Kenichi Matsumoto, “Locating Source Code to be Fixed based on Initial Bug Reports”, 4th IEEE International Workshop on Empirical Software Engineering, pp. 978-0-7695-4866-1, 2012.
[6] Marco D?Ambros and Michele Lanza, “Software Bugs and Evolution: A Visual Approach to Uncover Their Relationship”, Proceedings of the 10th European Conference on Software Maintenance and Reengineering, 2006, pp. 238, ISSN: 1534-5351, March 2006.
[7] Alberto Bacchelli, Marco D?Ambros and Michele Lanza, “Are Popular Classes More Defect Prone? Springer, Vol 6013, pp 59-73, 2010.
[8] K. E. Emam, W. Melo, and J. C. Machado, “The prediction of faulty classes using object-oriented design metrics,” Journal of Systems and Software, vol. 56, no. 1, pp. 63–75, 2001.
[9] A. E. Hassan and R. C. Holt, “The top ten list: Dynamic fault prediction,” in Proceedings of ICSM 2005, 2005, pp. 263–272.
[10] A. Marcus, D. Poshyvanyk, and R. Ferenc, “Using the conceptual cohesion of classes for fault prediction in objectoriented systems,” IEEE Trans. Software Eng., vol. 34, no. 2, pp. 287–300, 2008.

feature selection, ANFIS, LDA, parameters, approaches.