Software Fault Prediction Based on Improved Fuzzy Clustering
Predicting parts of the software programs that are more defects prone could ease up the software testing process and helps effectively to reduce the cost and time of developments. Although many machine-learning and statistical techniques have been proposed widely for defining fault prone modules in software fault prediction, but this area have yet to be explored with high accuracy and less error. Unfortunately, several earlier methods including artificial neural networks and its variants that have been used, marred by limitations such as inability to adequately handle uncertainties in software measurement data which leads to low accuracy, instability and inconsistency in prediction. In this paper, first the effect of irrelevant and inconsistent modules on fault prediction is decreased by designing a new framework, in which the entire project’s modules are clustered. The generated output is then passed to the next model in the hybrid setting, which is a probabilistic neural network (PNN) for training and prediction. We used four NASA data sets to evaluate our results. Performance evaluation in terms of false positive rate, false negative rate, and overall error are calculated and showed 30% to 60% improvement in false negative rate compared to other well-performed training methods such as naïve Bayes and random forest.
KeywordsFuzzy Clustering Fault Prediction Probabilistic Neural Network
Unable to display preview. Download preview PDF.
- 4.Promise Software Engineering Repository, http://promisedata.org (August 20, 2012)
- 5.Catal, C., Sevim, U., Diri, B.: Software fault prediction of unlabeled program modules. In: Proceedings of the World Congress on Engineering, vol. 1, pp. 1–3 (2009)Google Scholar
- 8.Mahaweerawat, A., Sophatsathit, P., Lursinsap, C.: Adaptive self-organizing map clustering for software fault prediction. In: Fourth International Joint Conference on Computer Science and Software Engineering, KhonKaen, Thailand, pp. 35–41 (2007)Google Scholar
- 9.Catal, C., Sevim, U., Diri, B.: Clustering and metrics thresholds based software fault prediction of unlabeled program modules. In: Sixth International Conference on Information Technology: New Generations, ITNG 2009, pp. 199–204 (April 2009)Google Scholar
- 13.Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers (1981)Google Scholar
- 14.Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters (1973)Google Scholar
- 15.Abaei, G., Selamat, A.: A survey on software fault detection based on different prediction approaches. Vietnam Journal of Computer Science, 1–17 (2013)Google Scholar