Software defect prediction techniques using metrics based on neural network classifier
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Software industries strive for software quality improvement by consistent bug prediction, bug removal and prediction of fault-prone module. This area has attracted researchers due to its significant involvement in software industries. Various techniques have been presented for software defect prediction. Recent researches have recommended data-mining using machine learning as an important paradigm for software bug prediction. state-of-art software defect prediction task suffer from various issues such as classification accuracy. However, software defect datasets are imbalanced in nature and known fault prone due to its huge dimension. To address this issue, here we present a combined approach for software defect prediction and prediction of software bugs. Proposed approach delivers a concept of feature reduction and artificial intelligence where feature reduction is carried out by well-known principle component analysis (PCA) scheme which is further improved by incorporating maximum-likelihood estimation for error reduction in PCA data reconstruction. Finally, neural network based classification technique is applied which shows prediction results. A framework is formulated and implemented on NASA software dataset where four datasets i.e., KC1, PC3, PC4 and JM1 are considered for performance analysis using MATLAB simulation tool. An extensive experimental study is performed where confusion, precision, recall, classification accuracy etc. parameters are computed and compared with existing software defect prediction techniques. Experimental study shows that proposed approach can provide better performance for software defect prediction.
KeywordsDefect prediction models Machine learning techniques Software defect prediction Software metrics
I would like to express my gratitude to Dr. Lilly Florence Prof. & HOD, MCA programme, Adhiyamaan College of Engineering, Hosur for providing me the guidance and support to make this work possible.
- 5.Sandhu, P.S., Brar, A.S., Goel, R., Kaur, J., Anand, S.: A model for early prediction of faults in software systems. In: 2nd International Conference on Computer and Automation Engineering, Singapore, pp. 281–285 (2010)Google Scholar
- 8.Aljamaan, H.I., Elish, M.O.: An empirical study of bagging and boosting ensembles for identifying faulty classes in object-oriented software. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM ’09), pp. 187–194, IEEE, Nashville (2009)Google Scholar
- 10.Shan, C., Chen, B., Hu, C., Xue, J., Li, N.: Software defect prediction model based on LLE and SVM. In: Proceedings of the Communications Security Conference (CSC ’14), pp. 1–5 (2014)Google Scholar
- 14.Xu, Z., Xuan, J., Liu, J., Cui, X.: MICHAC: defect prediction via feature selection based on maximal information coefficient with hierarchical agglomerative clustering. In: 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), Suita, pp. 370–381 (2016)Google Scholar
- 18.Shan C., Chen B., Hu C., Xue J., Li N.: Software defect prediction model based on LLE and SVM. In: Proceedings of the Communications Security Conference (CSC ’14), pp. 1–5 (2014)Google Scholar
- 21.Bautista, A.M., Feliu, T.S.: Defect prediction in software repositories with artificial neural networks. In: Mejia, J., Munoz, M., Rocha, Á., Calvo-Manzano, J. (eds.) Trends and Applications in Software Engineering. Advances in Intelligent Systems and Computing, vol. 405. Springer, Cham (2016)Google Scholar
- 22.Khoshgoftaar, T.M., Gao, K.: Feature selection with imbalanced data for software defect prediction. In: 2009 International Conference on Machine Learning and Applications, Miami Beach, pp. 235–240 (2009)Google Scholar
- 26.Software Defect Dataset: Promise repository, http://promise.site.uottawa.ca/SERepository/datasets-page.html