Hybrid Approach for Software Defect Prediction Using Machine Learning with Optimization Technique
Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1315961Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 991
 Grbac, TihanaGalinac, Per Runeson, and DarkoHuljenić. "A quantitative analysis of the unit verification perspective on fault distributions in complex software systems: an operational replication." Software quality journal 24, no. 4 (2016): 967-995.
 P. Bishnu and V. Bhattacherjee, “Software fault prediction using quad tree based k-means clustering algorithm,” IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 6, pp. 1146–1150, 2012.
 Malhotra, Ruchika. "A systematic review of machine learning techniques for software fault prediction." Applied Soft Computing 27 (2015): 504-518.
 Tantithamthavorn, Chakkrit, Shane McIntosh, Ahmed E. Hassan, and Kenichi Matsumoto. "An empirical comparison of model validation techniques for defect prediction models." IEEE Transactions on Software Engineering 43, no. 1 (2017): 1-18.
 Nam, Jaechang, Wei Fu, Sunghun Kim, Tim Menzies, and Lin Tan. "Heterogeneous defect prediction." IEEE Transactions on Software Engineering (2017).
 Maua, Goran, and TihanaGalinacGrbac. "Co-evolutionary multi-population genetic programming for classification in software defect prediction." Applied Soft Computing 55, no. C (2017): 331-351.
 Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J. and Folleco, A., 2014. An empirical study of the classification performance of learners on imbalanced and noisy software quality data. Information Sciences, 259, pp.571-595.
 L. Guo, Y. Ma, B. Cukic, and H. Singh, “Robust Prediction of Fault-Proneness by Random Forests,” Proc. 15th Int’l Symp.Software Reliability Eng., 2004
 Rathore, S.S. and Kumar, S., 2017. A decision tree logic based recommendation system to select software fault prediction techniques. Computing, 99(3), pp.255-285.
 Arar, Ö.F. and Ayan, K., 2015. Software defect prediction using cost-sensitive neural network. Applied Soft Computing, 33, pp.263-277.
 Idri, A., AzzahraAmazal, F. and Abran, A., 2015. Analogy-based software development effort estimation: A systematic mapping and review. Information and Software Technology, 58, pp.206-230.
 E. A. Felix and S. P. Lee, "Integrated Approach to Software Defect Prediction," in IEEE Access, vol. 5, pp. 21524-21547, 2017.
 M. Cheng, G. Wu, M. Yuan and H. Wan, "Semi-supervised Software Defect Prediction Using Task-Driven Dictionary Learning," in Chinese Journal of Electronics, vol. 25, no. 6, pp. 1089-1096, 11 2016.
 T. Lee, J. Nam, D. Han, S. Kim and H. Peter In, "Developer Micro Interaction Metrics for Software Defect Prediction," in IEEE Transactions on Software Engineering, vol. 42, no. 11, pp. 1015-1035, Nov. 1 2016.
 Software Defect Dataset, Promise Repository, http://promise.site.uottawa.ca/SERepository/datasets-page.html.accessedaround14/11/2017.