Low-rank representation for semi-supervised software defect prediction

Low-rank representation for semi-supervised software defect prediction

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Software defect prediction based on machine learning is an active research topic in the field of software engineering. The historical defect data in software repositories may contain noises because automatic defect collection is based on modified logs and defect reports. When the previous defect labels of modules are limited, predicting the defect-prone modules becomes a challenging problem. In this study, the authors propose a graph-based semi-supervised defect prediction approach to solve the problems of insufficient labelled data and noisy data. Graph-based semi-supervised learning methods used the labelled and unlabelled data simultaneously and consider them as the nodes of the graph at the training phase. Therefore, they solve the problem of insufficient labelled samples. To improve the stability of noisy defect data, a powerful clustering method, low-rank representation (LRR), and neighbourhood distance are used to construct the relationship graph of samples. Therefore, they propose a new semi-supervised defect prediction approach, named low-rank representation-based semi-supervised software defect prediction (LRRSSDP). The widely used datasets from NASA projects and noisy datasets are employed as test data to evaluate the performance. Experimental results show that (i) LRRSSDP outperforms several representative state-of-the-art semi-supervised defect prediction methods; and (ii) LRRSSDP can maintain robustness in noisy environments.


    1. 1)
      • 1. Catal, C., Diri, B.: ‘A systematic review of software fault prediction studies’, Expert Syst. Appl., 2009, 36, (4), pp. 73467354.
    2. 2)
      • 2. Hall, T., Beecham, S., Bowes, D., et al: ‘A systematic literature review on fault prediction performance in software engineering’, IEEE Trans. Softw. Eng., 2012, 38, (6), pp. 12761304.
    3. 3)
      • 3. Shi, Y., Li, M., Arndt, S., et al: ‘Metric-based software reliability prediction approach and its application’, Empir. Softw. Eng., 2017, 22, (4), pp. 15791633.
    4. 4)
      • 4. Jing, X.Y., Wu, F., Dong, X., et al: ‘An improved SDA based defect prediction framework for both within-project and cross-project class-imbalance problems’, IEEE Trans. Softw. Eng., 2017, 43, (4), pp. 321339.
    5. 5)
      • 5. Kim, S., Zhang, H., Wu, R., et al: ‘Dealing with noise in defect prediction’. Proc. Int. Conf. Software Engineering, Zurich, Switzerland, 2011, pp. 481490.
    6. 6)
      • 6. Zhu, X.: ‘Semi-supervised learning with graphs’. PhD thesis, Carnegie Mellon University, 2005.
    7. 7)
      • 7. Culp, M., Michailidis, G.: ‘Graph-based semisupervised learning’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, (1), pp. 174179.
    8. 8)
      • 8. Li, S., Fu, Y.: ‘Low-rank coding with b-matching constraint for semi-supervised classification’. Proc. Int. Joint Conf. Artificial Intelligence, Beijing, China, 2013, pp. 14721478.
    9. 9)
      • 9. Fei, L., Xu, Y., Fang, X., et al: ‘Low rank representation with adaptive distance penalty for semi-supervised subspace classification’, Pattern Recognit., 2017, 67, pp. 252262.
    10. 10)
      • 10. Seliya, N., Khoshgoftaar, T.M.: ‘Software quality estimation with limited fault data: a semi-supervised learning perspective’, Softw. Qual. J., 2007, 15, (3), pp. 327344.
    11. 11)
      • 11. Seliya, N., Khoshgoftaar, T.M.: ‘Software quality analysis of unlabeled program modules with semisupervised clustering’, IEEE Trans. Syst., Man, Cybern. A, Syst. Humans, 2007, 37, (2), pp. 201211.
    12. 12)
      • 12. Catal, C., Diri, B.: ‘Unlabelled extra data do not always mean extra performance for semi-supervised fault prediction’, Expert Syst., 2009, 26, (5), pp. 458471.
    13. 13)
      • 13. Jiang, Y., Li, M., Zhou, Z.-H.: ‘Software defect detection with ROCUS’, J. Comput. Sci. Technol., 2011, 26, (2), pp. 328342.
    14. 14)
      • 14. Li, M., Zhang, H., Wu, R., et al: ‘Sample-based software defect prediction with active and semi-supervised learning’, Autom. Softw. Eng., 2012, 19, (2), pp. 201230.
    15. 15)
      • 15. Thung, F., Le, X.B.D., Lo, D.: ‘Active semi-supervised defect categorization’. Proc. Int. Conf. Program Comprehension, Florence, Italy, 2015, pp. 6070.
    16. 16)
      • 16. Catal, C.: ‘A comparison of semi-supervised classification approaches for software defect prediction’, J. Intell. Syst., 2014, 23, (1), pp. 7582.
    17. 17)
      • 17. Ma, Y., Pan, W., Zhu, S., et al: ‘An improved semi-supervised learning method for software defect prediction’, J. Intell. Fuzzy Syst., 2014, 27, (5), pp. 24732480.
    18. 18)
      • 18. Abaei, G., Selamat, A., Fujita, H.: ‘An empirical study based on semi-supervised hybrid self-organizing map for software fault prediction’, Knowl.-Based Syst., 2015, 74, pp. 2839.
    19. 19)
      • 19. Wang, T.J., Zhang, Z.W., Jing, X.Y., et al: ‘Non-negative sparse-based SemiBoost for software defect prediction’, Softw. Test. Verif. Reliab., 2016, 26, (7), pp. 498515.
    20. 20)
      • 20. Zhang, Z.W., Jing, X.Y., Wang, T.J.: ‘Label propagation based semi-supervised learning for software defect prediction’, Autom. Softw. Eng., 2017, 24, (1), pp. 4769.
    21. 21)
      • 21. Khoshgoftaar, T.M., Seliya, N.: ‘The necessity of assuring quality in software measurement data’. Proc. Int. Conf. Software Metrics, Chicago, IL, USA, 2004, pp. 119130.
    22. 22)
      • 22. Tang, W., Khoshgoftaar, T.M.: ‘Noise identification with the k-means algorithm’. Proc. Int. Conf. Tools with Artificial Intelligence, FL, USA, 2004, pp. 373378.
    23. 23)
      • 23. Bird, C., Bachmann, A., Aune, E., et al: ‘Fair and balanced?: bias in bug-fix datasets’. Proc. of the 7th Joint Meeting of the European Software Engineering Conf. and the ACM SIGSOFT Symp. on the Foundations of Software Engineering, Amsterdam, Netherlands, 2009, pp. 121130.
    24. 24)
      • 24. Bachmann, A., Bird, C., Rahman, F., et al: ‘The missing links: bugs and bug-fix commits’. Proc. of the Eighteenth ACM SIGSOFT Int. Symp. on Foundations of Software Engineering, Santa Fe, NM, USA, 2010, pp. 97106.
    25. 25)
      • 25. Kim, S., Zimmermann, T., Whitehead, E.J.Jr, et al: ‘Predicting faults from cached history’. Proc. Int. Conf. Software Engineering, Minneapolis, MI, USA, 2007, pp. 489498.
    26. 26)
      • 26. Nguyen, T.H., Adams, B., Hassan, A.E.: ‘A case study of bias in bug-fix datasets’. 17th Working Conf. on Reverse Engineering, Beverly, CA, USA, 2010, pp. 259268.
    27. 27)
      • 27. Rahman, F., Posnett, D., Herraiz, I., et al: ‘Sample size vs. Bias in defect prediction’. Proc. of the 9th Joint Meeting on Foundations of Software Engineering, Saint Petersburg, Russia, 2013, pp. 147157.
    28. 28)
      • 28. Herzig, K., Just, S., Zeller, A.: ‘It's not a bug, it's a feature: how misclassification impacts bug prediction’. Proc. Int. Conf. Software Engineering, San Francisco, CA, USA, 2013, pp. 392401.
    29. 29)
      • 29. Tantithamthavorn, C., McIntosh, S., Hassan, A.E., et al: ‘The impact of mislabeling on the performance and interpretation of defect prediction models’. Proc. Int. Conf. Software Engineering, San Francisco, CA, USA, 2013, pp. 812823.
    30. 30)
      • 30. Zhuang, L., Gao, S., Tang, J., et al: ‘Constructing a nonnegative low-rank and sparse graph with data-adaptive features’, IEEE Trans. Image Process., 2015, 24, (11), pp. 37173728.
    31. 31)
      • 31. Feng, J., Lin, Z., Xu, H., et al: ‘Robust subspace segmentation with block-diagonal prior’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 38183825.
    32. 32)
      • 32. Liu, G., Yan, S.: ‘Latent low-rank representation for subspace segmentation and feature extraction’. Proc. Int. Conf. Computer Vision, Barcelona, Spain, 2011, pp. 16151622.
    33. 33)
      • 33. Zhang, H., Lin, Z., Zhang, C., et al: ‘Robust latent low rank representation for subspace clustering’, Neurocomputing, 2014, 145, pp. 369373.
    34. 34)
      • 34. Candes, E.J., Li, X., Ma, Y., et al: ‘Robust principal component analysis’, J. ACM (JACM), 2011, 58, (3), pp. 1147.
    35. 35)
      • 35. Siming, W., Zhouchen, L.: ‘Analysis and improvement of low rank representation for subspace segmentation’, arXiv preprint arXiv:1107.1561, 2011.
    36. 36)
      • 36. Fang, X., Xu, Y., Li, X., et al: ‘Learning a nonnegative sparse graph for linear regression’, IEEE Trans. Image Process., 2015, 24, (9), pp. 27602771.
    37. 37)
      • 37. Fang, X., Xu, Y., Li, X., et al: ‘Robust semi-supervised subspace clustering via non-negative low-rank representation’, IEEE Trans. Cybernetics, 2016, 46, (8), pp. 18281838.
    38. 38)
      • 38. Jing, X.Y., Qi, F., Wu, F., et al: ‘Missing data imputation based on low-rank recovery and semi-supervised regression for software effort estimation’. Proc. Int. Conf. Software Engineering, Austin, TX, USA, 2016, pp. 607618.
    39. 39)
      • 39. Zhou, D., Bousquet, O., Lal, T.N., et al: ‘Learning with local and global consistency’, Adv. Neural Inf. Process. Syst., Vancouver, Canada, 2004, pp. 321328.
    40. 40)
      • 40. Shepperd, M., Song, Q., Sun, Z., et al: ‘Data quality: some comments on the NASA software defect datasets’, IEEE Trans. Softw. Eng., 2013, 39, (9), pp. 12081215.
    41. 41)
      • 41. Lu, H., Cukic, B., Culp, M.: ‘An iterative semi-supervised approach to software fault prediction’. Proc. Int. Conf. Predictive Models in Software Engineering, Banff, Canada, 2011, Article No. 15.

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