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Published June 20, 2022 | Version v1
Conference paper Restricted

SOFTWARE DEFECTS PREDICTION USING MACHINE LEARNING TECHNIQUES: A CONTEMPORARY REVIEW

Creators

  • 1. Kaduna State University

Description

Detecting defects in software at the bleeding edge of a software development life cycle is vital. Identifying defects before the deployment of software aids in delivering high-quality products, and reduces development costs. Machine learning techniques are deployed in the earlier stages of software development to improve software performance quality and decrease software maintenance costs. This study focuses on reviewing some papers published in software defect prediction using Machine learning techniques from 2020 to the current time to determine the predominance of machine learning methodologies adoption in software defect prediction. Google Scholar was used to source research papers for this study, and data was gathered from the publications. The process involves reviewing the selected papers, writing a concise synopsis of the papers, connecting and involving them where appropriate, reviewing existing methodology, and finally summarizing the findings. The result shows recent activities and trends in defect prediction research. This investigation will aid researchers in understanding the most recent and cutting-edge trends in software defect prediction research using machine learning techniques.

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References

  • Assim, M., Obeidat, Q., and Hammad, M. (2020). Software Defects Prediction using Machine Learning Algorithms. In 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI) (pp. 1-6). IEEE.
  • Chen, J., Hu, K., Yu, Y., Chen, Z., Xuan, Q., Liu, Y., and Filkov, V. (2020). Software visualization and deep transfer learning for effective software defect prediction. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (pp. 578-589).
  • D'Ambros, M., Lanza, M., and Robbes, R. (2010). An extensive comparison of bug prediction approaches. In: 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), pp. 31–41.
  • Hasanpour, A., Farzi, P., Tehrani, A., and Akbari, R. (2020). Software defect prediction based on deep learning models: Performance study. arXiv preprint arXiv:2004.02589.
  • Jin, C. (2021). Software defect prediction model based on distance metric learning. Soft Computing, 25(1), 447-461.
  • Jing, X.-Y., Ying, S., Zhang, Z.-W., Wu, S.-S., and Liu, J. (2014). Dictionary learning-based software defect prediction. Proc. of the International Conference on Software Engineering.
  • Khan, B., Naseem, R., Shah, M. A., Wakil, K., Khan, A., Uddin, M. I., and Mahmoud, M. (2021). Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques. Journal of Healthcare Engineering.
  • Kitchenham, B., Budgen, D., and Brereton, P. (2015). Evidence-based Software Engineering and Systematic Reviews, CRC Press
  • Kitchenham, B.A., and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering, Technical Report EBSE-2007-01, School of Computer Science and Mathematics, Keele University
  • Lin, J., and Lu, L. (2021). Semantic Feature Learning via Dual Sequences for Defect Prediction. IEEE Access, 9, 13112-13124
  • Liu, A.G., Musial, E., and Chen, M.-H. (2011). Progressive reliability forecasting of serviceoriented software. Proceeding of the International Conference on Web Services ICWS'11.
  • McDonald, M., Musson, R., and Smith, R. (2007). The practical guide to defect prevention. Control, 260–272.
  • Menzies, T., Krishna, R., and Pryor, D. (2017). The SEACRAFT Repository of Empirical Software Engineering Data.
  • Naik, K., and Tripathy, P. (2008). Software Testing and Quality Assurance. John Wiley & Sons, Inc.
  • Qiao, L., Li, X., Umer, Q., and Guo, P. (2020). Deep learning-based software defect prediction. Neurocomputing, 385, 100-110.
  • Rahim, A., Hayat, Z., Abbas, M., Rahim, A., and Rahim, M. A. (2021). Software Defect Prediction with Naïve Bayes Classifier. In 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST) (pp. 293-297).
  • Ronchieri, E., Canaparo, M., Belgiovine, M., and Salomon, D. (2020). Software Defect Prediction on Unlabelled Dataset with Machine Learning Techniques
  • Shi, K., Lu, Y., Liu, G., Wei, Z., and Chang, J. (2021). MPT embedding: An unsupervised representation learning of code for software defect prediction. Journal of Software: Evolution and Process, 33(4), e2330.
  • Singh, P. K., Agarwal, D., and Gupta, A. (2015). A systematic review on software defect prediction. In 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 1793-1797).
  • Sohan, M. F., Kabir, M. A., Rahman, M., Bhuiyan, T., Jabiullah, M. I., & Felix, E. A. (2020). Prevalence of machine learning techniques in software defect prediction. In Cyber Security and Computer Science: Second EAI International Conference, ICONCS 2020, Dhaka, Bangladesh, February 15-16, 2020, Proceedings 2 (pp. 257-269). Springer International Publishing.
  • Yan, M., Fang, Y., Lo, D., Xia, X., & Zhang, X. (2017, November). File-level defect prediction: Unsupervised vs. supervised models. In 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) (pp. 344-353). IEEE.
  • Zhang, W. H., He, R. Y., Wu, L. J., Jian, Y., and Han, X. Y. (2021). Comparison of software defect prediction models based on machine learning. IOP Conference Series: Materials Science and Engineering (Vol. 1043, No. 3, p. 032074). IOP Publishing.