Today software has reformed the key element on every environment. Quality of software is connected with the number of faults as well as it determinate by time and cost. Software is a process and maintains continuous change to improve the functionality and effectiveness of the software quality. During the life cycle of software various problems arises like advanced planning, well documentation and proper process control. Software defects are expensive in specification of cost and quality. Software defect prediction improves quality framework predictive techniques and software metrics to provide fault-prone module description. This paper main feature is the concept of change proneness and software prediction model used to control the classes of software which are often to change. We have two aspects to be inscribed Parameters like Accuracy, Precision, Recall and Receiver operating characteristics (ROC). Machine learning algorithms are used for predicting software. This paper is proposing to relate and compare all machine learning techniques interrelated to performance parameters.

Software Quality, Support Vector Machine, Software Defect Prediction, Faults Prone, Change Proneness

[1]. K. Chandra, "Improving software quality using machine learning”, Innovation and Challenges in Cyber Security, India, pp.115-118, 2016.
[2]. B. Tang, "Toward optimal feature selection in naive Bayes for text categorization”, IEEE Transactions on Knowledge and Data Engineering, vol.5, issue.9, pp.2508-2521, 2016.
[3]. S. Choudhury, "Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection”, Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials IEEE, India, pp.89-95, 2015.
[4]. L. Zhao, "Quality Evaluation of College Foreign Language Textbook Based on SVM”, Intelligent Computation Technology and Automation IEEE, China, pp.670-673, 2015.
[5]. L. Zhao, "Quality Evaluation of College Foreign Language Textbook Based on SVM”, Intelligent Computation Technology and Automation IEEE, China, pp.670-673, 2015.
[6]. C. Izurieta, "Preemptive management of model driven technical debt for improving software quality”, Proceedings of the 11th International ACM SIGSOFT Conference on Quality of Software Architectures, USA, pp.31-36, 2015.
[7]. A.D Lipitakis, "On machine learning with imbalanced data and research quality evaluation methodologies”, Computational Science and Computational Intelligence International Conference IEEE, USA, pp.451-457, 2014.
[8]. G. Vitello, "A Novel Technique for Fingerprint Classification based on Fuzzy C-Means and Naive Bayes Classifier", Complex, Intelligent and Software Intensive Systems IEEE, UK, pp.155-166, 2014.
[9]. T.M Khoshgoftaar, "Improving software quality estimation by combining feature selection strategies with sampled ensemble learning”, Information Reuse and Integration 15th International Conference on IEEE, USA, pp.428-433, 2014.
[10]. Y. Yibo, "Scalable svm-based classification in dynamic graphs”, Data Mining IEEE International Conference on. IEEE, China, pp.650-659, 2014.
[11]. Y. Chen, "Effective Part Localization in Latent-SVM Training”, Pattern Recognition 22nd International Conference on. IEEE, Sweden, pp.4269-4274, 2014.
[12]. S. Mahajan, “Hierarchical Reinforcement Learning in Complex Learning Problems: A Survey”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.5, pp.72-78, 2014.
[13]. E. Shihab, "Practical software quality prediction" Software Maintenance and Evolution IEEE International Conference on. IEEE, Canada, pp.639-644, 2014.
[14]. M. Shepperd, "Researcher bias: The use of machine learning in software defect prediction", IEEE Transactions on Software Engineering, vol.40, issue.6, pp.603-613, 2014.
[15]. P. A Selvaraj, "Support Vector Machine for Software Defect Prediction", International Journal of Engineering & Technology Research, pp.68-76, 2013.
[16]. D. Gray, “The misuse of the NASA metrics data program data sets for automated software defect prediction" , Evaluation & Assessment in Software Engineering 15th Annual Conference on IET, UK, pp.603-613, 2011.
[17]. Q. Song, "A general software defect-proneness prediction framework" IEEE Transactions on Software Engineering, vol.37, issue.3, pp.356-370, 2011.
[18]. N. Gayatri, "Performance analysis of data mining algorithms for software quality prediction”, Advances in Recent Technologies in Communication and Computing International Conference on. IEEE, India, pp.393-395, 2009.
[19]. Y. Zhao, "Comparison of decision tree methods for finding active objects”, Advances in Space Research, vol.12, issue.14, pp.1955-1959, 2008.
[20]. I. Gondra, "Applying machine learning to software fault-proneness prediction”, Journal of Systems and Software, vol.81, issue.2, pp.186-195, 2008.
[21]. M. Hall,"A decision tree-based attribute weighting filter for naive Bayes”, Knowledge-Based Systems, vol.20, issue.2, pp.120-126, 2007.
[22]. C.J Du, "Learning techniques used in computer vision for food quality evaluation: a review”, Journal of food engineering, vol.72, issue.1, pp.39-55, 2006.
[23]. J. Huang, "Using AUC and accuracy in evaluating learning algorithms”, IEEE Transactions on knowledge and Data Engineering, vol.17, issue.3, pp.299-310, 2005.
[24]. M. R Boutell, "Learning multi-label scene classification", Pattern recognition, vol.37, issue.9, pp.1757-1771, 2004.
[25]. J. Huang, "Comparing naive Bayes, decision trees, and SVM with AUC and accuracy" Data Mining Third IEEE International Conference on IEEE, USA, pp.553-556, 2003.
[26]. D. Zhang, "Question classification using support vector machines”, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval, USA, pp.26-32 2003.
[27]. D. Zhang, "Machine learning and software engineering”, Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. 14th IEEE International Conference on. IEEE, 2002.
[28]. I. Kononenko, "Machine learning for medical diagnosis: history, state of the art and perspective”, Artificial Intelligence in medicine vol. 23, issue.1, pp.89-109, 2001.
[29]. A.K Jain, "Classification of text documents", Pattern Recognition, Fourteenth International Conference on IEEE, Australia, pp.1051-4651, 1998.
[30]. A.P Bradley, "The use of the area under the ROC curve in the evaluation of machine learning algorithms" Pattern recognition, vol.30, issue.7, pp-1145-1159, 1997.
[31]. S.W Wilson, “Classifier fitness based on accuracy", Evolutionary computation, vol.3, issue.2, pp.149-175, 1995.