A Hybrid Neuro Fuzzy Approach for Bug Prediction using Software Metrics
Citation
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
Abstract
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.
References
[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.
Keywords
feature selection, ANFIS, LDA,
parameters, approaches.