Abstract
This paper aims to solve a software engineering problem by applying several approaches to exploit tree representations of programs. Firstly, we propose a new sibling-subtree convolutional neural network (SibStCNN), and combination models of tree-based neural networks and k-Nearest Neighbors (kNN) for source code classification. Secondly, we present a pruning tree technique to reduce data dimension and strengthen classifiers. The experiments show that the proposed models outperform other methods, and the pruning tree leads to not only a substantial reduction in execution time but also an increase in accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Our models are publicly available at https://github.com/nguyenlab/TBCNN_kNN_SVM.git.
References
Binkley, D., Feild, H., Lawrie, D., Pighin, M.: Software fault prediction using language processing. In: Testing: Academic and Industrial Conference Practice and Research Techniques-MUTATION, 2007. TAICPART-MUTATION 2007, pp. 99–110. IEEE (2007)
Huo, X., Li, M., Zhou, Z.-H.: Learning unified features from natural and programming languages for locating buggy source code
Joachims, T.: Making large scale SVM learning practical. Technical report, Universität Dortmund (1999)
Kaur, J., Singh, S., Kahlon, K.S., Bassi, P.: Neural network-a novel technique for software effort estimation. Int. J. Comput. Theor. Eng. 2(1), 17 (2010)
Menzies, T., Greenwald, J., Frank, A.: Data mining static code attributes to learn defect predictors. IEEE Trans. Softw. Eng. 33(1), 2–13 (2007)
Mo, R., Cai, Y., Kazman, R., Xiao, L., Feng, Q.: Decoupling level: a new metric for architectural maintenance complexity. In: Proceedings of the 38th International Conference on Software Engineering, pp. 499–510. ACM (2016)
Mou, L., Li, G., Zhang, L., Wang, T., Jin, Z.: Convolutional neural networks over tree structures for programming language processing. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (2016)
Phan, V.A., Chau, N.P., Nguyen, M.L.: Exploiting tree structures for classifying programs by functionalities. In: 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE), pp. 85–90. IEEE (2016)
Socher, R., Huang, E.H., Pennin, J., Manning, C.D., Ng, A.Y.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Advances in Neural Information Processing Systems, pp. 801–809 (2011)
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1422–1432 (2015)
Ugurel, S., Krovetz, R., Giles, C.L.: What’s the code?: automatic classification of source code archives. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 632–638. ACM (2002)
Wang, S., Yao, X.: Using class imbalance learning for software defect prediction. IEEE Trans. Reliab. 62(2), 434–443 (2013)
Acknowledgements
This work was supported partly by JSPS KAKENHI Grant number 15K16048 and the first author would like to thank the scholarship from Ministry of Training and Education (MOET), Vietnam under the project 911.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Phan, A.V., Nguyen, M.L., Bui, L.T. (2017). SibStCNN and TBCNN + kNN-TED: New Models over Tree Structures for Source Code Classification. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-68935-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68934-0
Online ISBN: 978-3-319-68935-7
eBook Packages: Computer ScienceComputer Science (R0)