Abstract
Source code summarization is the process of generating a concise and generalized natural language summary from a given source code, which can facilitate software developers to comprehend and use the code better. Currently, most research on source code summarization generation focuses on either converting the source code into abstract syntax tree (AST) sequences or directly converting it into code segments and then feeding these representations into deep learning models. However, these single representation approaches ignore the semantic features of source code and destroy the structure of the abstract syntax tree, which affects the quality of the generated source code summarization. In this paper, we propose a novel source code summarization approach that fuses multiple code features into self-consistency output (FCSO). Our approach is based on a graph neural network encoder and a CodeBERT encoder with a self-attention mechanism. It extracts the sentence feature attention vector and the AST feature attention vector of the source code for feature fusion. Then, it inputs them into the Transformer decoder. Furthermore, to generate more accurate source code summaries, we adopt a new decoding strategy called self-consistency. It samples different inference paths, uses a penalty mechanism to calculate their similarity scores, and ultimately selects the most consistent answer. Our experimental results demonstrate that our proposed approach outperforms standard baseline approaches. On the Python dataset, the BLEU score, METEOR score, and ROUGE_L score increase by 11.13\(\%\), 9.12\(\%\), and 7.88\(\%\), respectively. These results show that our approach provides a promising direction for future research on source code summarization.
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References
Ko, A.J., Myers, B.A., Aung, H.H.: Six learning barriers in end-user programming systems. In: 2004 IEEE Symposium on Visual Languages-Human Centric Computing, pp. 199–206. IEEE, September 2004
Eddy, B.P., Robinson, J.A., Kraft, N.A., Carver, J.C.: Evaluating source code summarization techniques: replication and expansion. In: 2013 21st International Conference on Program Comprehension (ICPC), pp. 13–22. IEEE, May 2013
Sridhara, G., Hill, E., Muppaneni, D., Pollock, L., Vijay-Shanker, K.: Towards automatically generating summary comments for Java methods. In: Proceedings of the IEEE/ACM International Conference on Automated Software Engineering, pp. 43–52, September 2010
Moreno, L., Aponte, J., Sridhara, G., Marcus, A., Pollock, L., Vijay-Shanker, K.: Automatic generation of natural language summaries for Java classes. In: 2013 21st International Conference on Program Comprehension (ICPC), pp. 23–32. IEEE, May 2013
Wong, E., Yang, J., Tan, L.: AutoComment: mining question and answer sites for automatic comment generation. In: 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 562–567. IEEE, November 2013
Iyer, S., Konstas, I., Cheung, A., Zettlemoyer, L.: Summarizing source code using a neural attention model. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 2073–2083, August 2016
Hu, X., Li, G., Xia, X., Lo, D., Lu, S., Jin, Z.: Summarizing source code with transferred API knowledge (2018)
Hu, X., Li, G., Xia, X., Lo, D., Jin, Z.: Deep code comment generation. In: Proceedings of the 26th Conference on Program Comprehension, pp. 200–210, May 2018
Wang, W., Zhang, Y., Zeng, Z., Xu, G.: TranS3: a transformer-based framework for unifying code summarization and code search. arXiv preprint arXiv:2003.03238 (2020)
Freitag, M., Al-Onaizan, Y.: Beam search strategies for neural machine translation. arXiv preprint arXiv:1702.01806 (2017)
Feng, Z., et al.: CodeBERT: a pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155 (2020)
Huo, X., Li, M., Zhou, Z.H.: Control flow graph embedding based on multi-instance decomposition for bug localization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 4223–4230, April 2020
LeClair, A., Haque, S., Wu, L., McMillan, C.: Improved code summarization via a graph neural network. In: Proceedings of the 28th International Conference on Program Comprehension, pp. 184–195, July 2020
Shi, E., et al.: Cast: enhancing code summarization with hierarchical splitting and reconstruction of abstract syntax trees. arXiv preprint arXiv:2108.12987 (2021)
LeClair, A., Jiang, S., McMillan, C.: A neural model for generating natural language summaries of program subroutines. In: 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), pp. 795–806. IEEE, May 2019
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Ahmad, W.U., Chakraborty, S., Ray, B., Chang, K.W.: A transformer-based approach for source code summarization. arXiv preprint arXiv:2005.00653 (2020)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710, August 2014
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Zhou, D.: Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171 (2022)
Cheng, J., Fostiropoulos, I., Boehm, B.: GN-Transformer: fusing sequence and graph representation for improved code summarization. arXiv preprint arXiv:2111.08874 (2021)
Wang, Y., Dong, Y., Lu, X., Zhou, A.: GypSum: learning hybrid representations for code summarization. In: Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension, pp. 12–23, May 2022
Gao, Y., Lyu, C.: M2TS: multi-scale multi-modal approach based on transformer for source code summarization. In: Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension, pp. 24–35, May 2022
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Sun, Z., Zhu, Q., Xiong, Y., Sun, Y., Mou, L., Zhang, L.: TreeGen: a tree-based transformer architecture for code generation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 05, pp. 8984–8991, April 2020
Barone, A.V.M., Sennrich, R.: A parallel corpus of Python functions and documentation strings for automated code documentation and code generation. arXiv preprint arXiv:1707.02275 (2017)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318, July 2002
Banerjee, S., Lavie, A.: METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, pp. 65–72, June 2005
Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81, July 2004
Alon, U., Brody, S., Levy, O., Yahav, E.: code2seq: generating sequences from structured representations of code. arXiv preprint arXiv:1808.01400 (2018)
Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075 (2015)
Acknowledgements
This work is supported by Jiangxi Provincial Natural Science Foundation under Grant No. 20224BAB212015, the Foundation of Jiangxi Educational Committee Under Grant No. GJJ210338, the National Natural Science Foundation of China (NSFC) under Grant No. 62363015, 61962026 and the National Natural Science Key Foundation of China Grant No. 61832014.
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Zhang, D. et al. (2024). FCSO: Source Code Summarization by Fusing Multiple Code Features and Ensuring Self-consistency Output. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14488. Springer, Singapore. https://doi.org/10.1007/978-981-97-0801-7_7
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