A Study on Software Metrics based Software Defect Prediction using Data Mining and Machine Learning Techniques

Software quality is a field of study and practice that describes the desirable attributes of software products. The performance must be perfect without any defects.Software quality metrics are a subset of software metrics that focus on the quality aspects of the product, process, and project.The software defectprediction model helps in early detection of defects and contributes to their efficient removal and producing a quality software system based on several metrics. The main objective of paper is to help developers identify defects based on existing software metrics using data mining techniques and thereby improve the software quality.In this paper, variousclassification techniquesare revisitedwhich are employed for software defect prediction using software metrics in the literature.

[1]  Tulips Angel Thankachan A Survey on Classification and Rule Extraction Techniques for Datamining , 2013 .

[2]  Shihai Wang,et al.  An Empirical Study for Software Fault-Proneness Prediction with Ensemble Learning Models on Imbalanced Data Sets , 2014, J. Softw..

[3]  Binu Rajan,et al.  Software Defect Prediction System –Decision Tree Algorithm With Two Level Data Preprocessing , 2014 .

[4]  Olcay Taner Yildiz,et al.  Software defect prediction using Bayesian networks , 2012, Empirical Software Engineering.

[5]  Ayse Basar Bener,et al.  A mapping study on bayesian networks for software quality prediction , 2014, RAISE 2014.

[6]  Rizwana Kalsoom,et al.  Application and Verification of Algorithm Learning Based Neural Network , 2014, ArXiv.

[7]  Ali Selamat,et al.  A survey on software fault detection based on different prediction approaches , 2014, Vietnam Journal of Computer Science.

[8]  Karim O. Elish,et al.  Predicting defect-prone software modules using support vector machines , 2008, J. Syst. Softw..

[9]  Chih-Ping Chu,et al.  Integrating in-process software defect prediction with association mining to discover defect pattern , 2009, Inf. Softw. Technol..

[10]  Zhi-Hua Zhou,et al.  Sample-based software defect prediction with active and semi-supervised learning , 2012, Automated Software Engineering.

[11]  Bruce Christianson,et al.  Using the Support Vector Machine as a Classification Method for Software Defect Prediction with Static Code Metrics , 2009, EANN.

[12]  Bojan Cukic,et al.  Robust prediction of fault-proneness by random forests , 2004, 15th International Symposium on Software Reliability Engineering.

[13]  Jan Vanthienen,et al.  Software Defect Prediction Based on Association Rule Classification , 2010 .

[14]  Ioannis Stamelos,et al.  Regression via Classification applied on software defect estimation , 2008, Expert Syst. Appl..

[15]  王青,et al.  Using Bayesian regression and EM algorithm with missing handling for software effort prediction , 2015 .

[16]  Mohammad Alshayeb,et al.  Software defect prediction using ensemble learning on selected features , 2015, Inf. Softw. Technol..

[17]  Mario Barcelo-Valenzuela,et al.  Knowledge Sources and Automatic Classification: A Literature Review , 2014 .

[18]  M. SURENDRA NAIDU,et al.  CLASSIFICATION OF DEFECTS IN SOFTWARE USING DECISION TREE ALGORITHM , 2013 .

[19]  Xiao-Yuan Jing,et al.  Software defect prediction based on collaborative representation classification , 2014, ICSE Companion.

[20]  Tim Menzies,et al.  Class level fault prediction using software clustering , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[21]  Anuradha Chug,et al.  Software Defect Prediction Using Supervised Learning Algorithm and Unsupervised Learning Algorithm , 2013 .

[22]  Xiao Liu,et al.  An empirical study on software defect prediction with a simplified metric set , 2014, Inf. Softw. Technol..

[23]  Divya Tomar,et al.  A Feature Selection Based Model for Software Defect Prediction , 2014 .

[24]  Lech Madeyski,et al.  Which process metrics can significantly improve defect prediction models? An empirical study , 2014, Software Quality Journal.

[25]  Aurora Trinidad Ramirez Pozo,et al.  Exploring Genetic Programming and Boosting Techniques to Model Software Reliability , 2007, IEEE Transactions on Reliability.

[26]  Gábor Horváth,et al.  Least Squares Support Vector Machines for Data Mining , 2005 .

[27]  Izzat Alsmadi,et al.  Enhance Rule Based Detection for Software Fault Prone Modules , 2012 .

[28]  Ye Yang,et al.  Using Bayesian regression and EM algorithm with missing handling for software effort prediction , 2015, Inf. Softw. Technol..

[30]  Arvinder Kaur,et al.  Empirical Evaluation of Machine Learning Algorithms for Fault Prediction , 2014 .

[31]  Bart Baesens,et al.  Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings , 2008, IEEE Transactions on Software Engineering.

[32]  Bing Ge,et al.  Research on software defect prediction based on data mining , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[33]  N. Satyanarayana,et al.  Survey of Classification Techniques in Data Mining , 2014 .

[34]  Vedula Venkateswara Rao,et al.  Improved Classification Based Association Rule Mining , 2013 .

[35]  Manika Verma,et al.  A Comparative study of Techniques in Data Mining , 2014 .

[36]  K. Sankar,et al.  Prediction of Code Fault Using Naive Bayes and SVM Classifiers , 2014 .

[37]  Ali Selamat,et al.  An empirical study based on semi-supervised hybrid self-organizing map for software fault prediction , 2015, Knowl. Based Syst..

[38]  Cagatay Catal,et al.  A Comparison of Semi-Supervised Classification Approaches for Software Defect Prediction , 2014, J. Intell. Syst..

[39]  Taghi M. Khoshgoftaar,et al.  Software Defect Prediction for High-Dimensional and Class-Imbalanced Data , 2011, SEKE.

[40]  Hareton K. N. Leung,et al.  Bayesian Prediction of Fault-Proneness of Agile-Developed Object-Oriented System , 2013, ICEIS.

[41]  Marian Jureczko,et al.  Significance of Different Software Metrics in Defect Prediction , 2011 .

[42]  D. V. Ashoka,et al.  Application of Data Mining Techniques for Defect Detection and Classification , 2014, FICTA.

[43]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[44]  Rashedur M. Rahman,et al.  Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis , 2013 .

[45]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[46]  Improving The Fault Prediction In Oo Systems Using ANN With Firefly Algorithm , 2013 .

[47]  Ruchika Malhotra,et al.  A systematic review of machine learning techniques for software fault prediction , 2015, Appl. Soft Comput..

[48]  M. Manish,et al.  A Clustered Approach to Analyze the Software Quality Using Software Defects , 2012, 2012 Second International Conference on Advanced Computing & Communication Technologies.

[49]  Yogesh Singh,et al.  Suitability of KNN Regression in the Development of Interaction based Software Fault Prediction Models , 2014 .

[50]  Banu Diri,et al.  Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm , 2011, Expert Syst. Appl..

[51]  Zsuzsanna Marian,et al.  Software defect prediction using relational association rule mining , 2014, Inf. Sci..

[52]  Ioannis Stamelos,et al.  Software Defect Prediction Using Regression via Classification , 2006, IEEE International Conference on Computer Systems and Applications, 2006..