A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

Feidu Akmel, Ermiyas Birihanu, Bahir Siraj

Abstract


Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

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References


Sandeep D and R. S, "Case Studies of Most Common and Severe Types of Software System Failure " International Journal of Advanced Research in Computer Science and Software Engineering vol. 2, pp. 341-347 August 2012

Venkata U and R. A, "Empirical Assessment of Machine Learning based Software Defect Prediction Techniques " Proceedings of the 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems 2005.

Robert N, "Why Software Fails " 2005

Rajkumar G and K.Alagarsamy, "The Most Common Factors For The Failure Of Software Development Project," vol. 11, pp. 74-77, January 2013.

L. J, "Major Causes of Software Project Failures " CROSSTALK The Journal of Defense Software Engineering pp. 9-12, July 1998.

T. Clancy, "The Standish Group Report CHAOS," Project Smart pp. 1-16, 2014.

Vikas S and J. R, "Cataloguing Most Severe Causes that lead Software Projects to Fail," International Journal on Recent and Innovation Trends in Computing and Communication vol. 2, pp. 1143– 1147, May 2014.

Mikyeong P and E. H, "Software Fault Prediction Model using Clustering Algorithms Determining the Number of Clusters Automatically," International Journal of Software Engineering and Its Applications, vol. 8, pp. 199-204, 2014.

Harry A, "machine learning capabilities, limitation and implications " Technology Futures Researcher at Nesta 2015.

T. M. Mitchell, "Machine Learning " 1997.

George T, Ioannis K, Ioannis P, and Ioannis V, "Modern Applications of Machine Learning " Proceedings of the 1st Annual SEERC Doctoral Student Conference vol. 1, 2006.

Sarwesh S and S. K, "A Review of Ensemble Technique for Improving Majority Voting for Classifier," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 1, pp. 177-180, January 2013.

Christian M, Gail K, and Marta A, "Title," unpublished|.

D. Zhang, "Title," unpublished|.

T. M. Mitchell, The Discipline of Machine Learning: Carnegie Mellon University, 2006.

Xia C, Michael R, Kam-Fai W, and M. W, "ComPARE: A Generic Quality Assessment Environment for Component-Based Software Systems," Center of Innovation and Technology the Chinese University of Hong Kong, pp. 1-25.

Ekbal R, Srikanta P, and V. B, "A Survey in the Area of Machine Learning and Its Application for Software Quality Prediction," ACM SIGSOFT Software Engineering,, vol. 37, September 2012.

L. C. Briand, "Novel Applications of Machine Learning in Software Testing," pp. 3-10, 2008.

Yogesh S, Pradeep K, and O. S, "A Review of Studies On Machine Learning Techniques," International Journal of Computer Science and Security vol. 1, pp. 70-84.

Nguyen V, "The Application of Machine Learning Methods in Software Verification and Validation " MSc, Master of Science in Engineering, The University of Texas at Austin, Texas 2010.

Ekbal R, Srikanta P, and V. B, "Software Quality Estimation using Machine Learning:Case-based Reasoning Technique " International Journal of Computer Applications, vol. 58, pp. 43-48, November 2012.

E. Erturk and E. A. Sezer, "A comparison of some soft computing methods for software fault prediction," Expert Systems with Applications, 2014.

M. SURENDRA and D. N. GEETHANJALI, "Classification of Defects In Software Using Decision Tree Algorithm," vol. 5, pp. 1332-1340, June 2013.

Saiqa A, Luiz F, and F. A, "Benchmarking Machine Learning Techniques for Software Defect Detection," International Journal of Software Engineering & Applications, vol. 6, pp. 11-23, May 2015.

Martin S, David B, and T. H, "Researcher Bias: The Use of Machine Learning in Software Defect Prediction " IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, vol. 40, pp. 603-616, JUNE 2014.

X. Tan, X. Peng, S. Pan, and W. Zhao, "Assessing Software Quality by Program Clustering and Defect Prediction," pp. 244-248, 2011.

Jaspree K and P. S, "A k-means Based Approach for Prediction of Level of Severity of Faults in Software System," Proceedings of International conference on Intelligent Computational Systems, 2011.

Pooja P and D. A. Phalke, "Software Defect Prediction for Quality Improvement Using Hybrid Approach," International Journal of Application or Innovation in Engineering & Management, vol. 4, June 2015.

A. Kaur and R. Malhotra, "Application of Random Forest in Predicting Fault-Prone Classes," pp. 37-43, 2008.

Y. I. Peng, G. Kou, G. Wang, W. Wu, and Y. Shi, "Ensemble of Software Defect Predictors: An Ahp-Based Evaluation Method," International Journal of Information Technology & Decision Making, vol. 10, pp. 187-206, 2011.




DOI: https://doi.org/10.23956/ijermt.v6i6.286

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