{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T03:57:12Z","timestamp":1782878232132,"version":"3.54.5"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T00:00:00Z","timestamp":1655164800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T00:00:00Z","timestamp":1655164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012166","name":"national key r&d program of china","doi-asserted-by":"crossref","award":["2018YFB1402800"],"award-info":[{"award-number":["2018YFB1402800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61772560"],"award-info":[{"award-number":["61772560"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012476","name":"fundamental research funds for central universities of the central south university","doi-asserted-by":"publisher","award":["2021zzts0725"],"award-info":[{"award-number":["2021zzts0725"]}],"id":[{"id":"10.13039\/501100012476","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Autom Softw Eng"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s10515-022-00341-1","type":"journal-article","created":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T11:03:06Z","timestamp":1655204586000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Code comment generation based on graph neural network enhanced transformer model for code understanding in open-source software ecosystems"],"prefix":"10.1007","volume":"29","author":[{"given":"Li","family":"Kuang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4975-034X","authenticated-orcid":false,"given":"Xiaoxian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,14]]},"reference":[{"key":"341_CR1","doi-asserted-by":"crossref","unstructured":"Ahmad, W.U., Chakraborty, S., Ray, B., Chang, K.W.: A transformer-based approach for source code summarization. arXiv preprint arXiv:200500653 (2020)","DOI":"10.18653\/v1\/2020.acl-main.449"},{"key":"341_CR2","unstructured":"Allamanis, M., Brockschmidt, M., Khademi, M.: Learning to represent programs with graphs. arXiv preprint arXiv:1711.00740 (2017)"},{"key":"341_CR3","unstructured":"Allamanis, M., Peng, H., Sutton, C.: A convolutional attention network for extreme summarization of source code. In: International conference on machine learning, PMLR, pp. 2091\u20132100 (2016)"},{"issue":"4","key":"341_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3212695","volume":"51","author":"M Allamanis","year":"2018","unstructured":"Allamanis, M., Barr, E.T., Devanbu, P., Sutton, C.: A survey of machine learning for big code and naturalness. ACM Comput. Surv. (CSUR) 51(4), 1\u201337 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"341_CR5","unstructured":"Alon, U., Brody, S., Levy, O., Yahav, E.: code2seq: Generating sequences from structured representations of code. arXiv preprint arXiv:1808.01400 (2018)"},{"key":"341_CR6","doi-asserted-by":"crossref","unstructured":"Alon, U., Zilberstein, M., Levy, O., Yahav, E.: code2vec: Learning distributed representations of code. Proc. ACM Program. Lang. 3(POPL), 1\u201329 (2019)","DOI":"10.1145\/3290353"},{"issue":"4","key":"341_CR7","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1145\/3296979.3192412","volume":"53","author":"U Alon","year":"2018","unstructured":"Alon, U., Zilberstein, M., Levy, O., Yahav, E.: A general path-based representation for predicting program properties. ACM SIGPLAN Notices 53(4), 404\u2013419 (2018)","journal-title":"ACM SIGPLAN Notices"},{"key":"341_CR8","unstructured":"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\u201372 (2005)"},{"key":"341_CR9","unstructured":"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:170702275 (2017)"},{"key":"341_CR10","doi-asserted-by":"crossref","unstructured":"Chen, F., Kim, M., Choo, J.: Novel natural language summarization of program code via leveraging multiple input representations. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 2510\u20132520 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.214"},{"key":"341_CR11","unstructured":"Chen, X., Liu, C., Song, D.: Tree-to-tree neural networks for program translation. arXiv preprint arXiv:180203691 (2018)"},{"key":"341_CR12","doi-asserted-by":"crossref","unstructured":"Chirkova, N., Troshin, S.: Empirical study of transformers for source code. arXiv preprint arXiv:201007987 (2020)","DOI":"10.1145\/3468264.3468611"},{"key":"341_CR13","doi-asserted-by":"crossref","unstructured":"Cho, K., Van\u00a0Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:14061078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"341_CR14","doi-asserted-by":"crossref","unstructured":"Eriguchi, A., Hashimoto, K., Tsuruoka, Y.: Tree-to-sequence attentional neural machine translation. arXiv preprint arXiv:160306075 (2016)","DOI":"10.18653\/v1\/P16-1078"},{"key":"341_CR15","doi-asserted-by":"crossref","unstructured":"Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., et\u00a0al.: Codebert: a pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"341_CR16","unstructured":"Fernandes, P., Allamanis, M., Brockschmidt, M.: Structured neural summarization. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6\u20139, 2019, OpenReview.net (2019). https:\/\/openreview.net\/forum?id=H1ersoRqtm"},{"key":"341_CR17","doi-asserted-by":"crossref","unstructured":"Gao, S., Gao, C., He, Y., Zeng, J., Nie, L.Y., Xia, X.: Code structure guided transformer for source code summarization. arXiv preprint arXiv:210409340 (2021)","DOI":"10.1145\/3522674"},{"key":"341_CR18","unstructured":"Guo, D., Ren, S., Lu, S., Feng, Z., Tang, D., Liu, S., Zhou, L., Duan, N., Svyatkovskiy, A., Fu, S., et\u00a0al.: Graphcodebert: pre-training code representations with data flow. arXiv preprint arXiv:2009.08366 (2020)"},{"key":"341_CR19","unstructured":"Hellendoorn, V.J., Sutton, C., Singh, R., Maniatis, P., Bieber, D.: Global relational models of source code. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26\u201330, 2020, OpenReview.net (2020). https:\/\/openreview.net\/forum?id=B1lnbRNtwr"},{"key":"341_CR20","unstructured":"Hellendoorn, V.J., Sutton, C., Singh, R., Maniatis, P., Bieber, D.: Global relational models of source code. In: International Conference on Learning Representations (2019)"},{"key":"341_CR21","doi-asserted-by":"crossref","unstructured":"Hu, X., Li, G., Xia, X,, Lo, D., Jin, Z.: Deep code comment generation. In: 2018 IEEE\/ACM 26th International Conference on Program Comprehension (ICPC), pp. 200\u2013210. IEEE (2018a)","DOI":"10.1145\/3196321.3196334"},{"key":"341_CR22","doi-asserted-by":"crossref","unstructured":"Hu, X., Li, G., Xia, X., Lo, D., Lu, S., Jin, Z.: Summarizing source code with transferred api knowledge (2018b)","DOI":"10.24963\/ijcai.2018\/314"},{"issue":"3","key":"341_CR23","doi-asserted-by":"publisher","first-page":"2179","DOI":"10.1007\/s10664-019-09730-9","volume":"25","author":"X Hu","year":"2020","unstructured":"Hu, X., Li, G., Xia, X., Lo, D., Jin, Z.: Deep code comment generation with hybrid lexical and syntactical information. Emp. Softw. Eng. 25(3), 2179\u20132217 (2020)","journal-title":"Emp. Softw. Eng."},{"key":"341_CR24","doi-asserted-by":"crossref","unstructured":"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 (Volume 1: Long Papers), pp. 2073\u20132083 (2016)","DOI":"10.18653\/v1\/P16-1195"},{"key":"341_CR25","doi-asserted-by":"crossref","unstructured":"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\u2013195 (2020)","DOI":"10.1145\/3387904.3389268"},{"key":"341_CR26","doi-asserted-by":"crossref","unstructured":"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\u2013806. IEEE (2019)","DOI":"10.1109\/ICSE.2019.00087"},{"key":"341_CR27","doi-asserted-by":"crossref","unstructured":"Lin, C., Ouyang, Z., Zhuang, J., Chen, J., Li, H., Wu, R.: Improving code summarization with block-wise abstract syntax tree splitting. arXiv preprint arXiv:210307845 (2021)","DOI":"10.1109\/ICPC52881.2021.00026"},{"key":"341_CR28","unstructured":"Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74\u201381 (2004)"},{"key":"341_CR29","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:150804025 (2015)","DOI":"10.18653\/v1\/D15-1166"},{"issue":"1","key":"341_CR30","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/TSE.2011.41","volume":"38","author":"E Murphy-Hill","year":"2011","unstructured":"Murphy-Hill, E., Parnin, C., Black, A.P.: How we refactor, and how we know it. IEEE Trans. Softw. Eng. 38(1), 5\u201318 (2011)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"341_CR31","doi-asserted-by":"crossref","unstructured":"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\u2013318 (2002)","DOI":"10.3115\/1073083.1073135"},{"key":"341_CR32","first-page":"589","volume":"8","author":"LF Ribeiro","year":"2020","unstructured":"Ribeiro, L.F., Zhang, Y., Gardent, C., Gurevych, I.: Modeling global and local node contexts for text generation from knowledge graphs. Trans. Assoc. Comput. Ling. 8, 589\u2013604 (2020)","journal-title":"Trans. Assoc. Comput. Ling."},{"key":"341_CR33","doi-asserted-by":"crossref","unstructured":"See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. arXiv preprint arXiv:1704.04368 (2017)","DOI":"10.18653\/v1\/P17-1099"},{"key":"341_CR34","doi-asserted-by":"crossref","unstructured":"Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. arXiv preprint arXiv:180302155 (2018)","DOI":"10.18653\/v1\/N18-2074"},{"key":"341_CR35","doi-asserted-by":"crossref","unstructured":"Shido, Y., Kobayashi, Y., Yamamoto, A., Miyamoto, A., Matsumura, T.: Automatic source code summarization with extended tree-lstm. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8851751"},{"key":"341_CR36","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. arXiv preprint arXiv:170603762 (2017)"},{"key":"341_CR37","doi-asserted-by":"crossref","unstructured":"Wan, Y., Zhao, Z., Yang, M., Xu, G., Ying, H., Wu, J., Yu, P.S.: Improving automatic source code summarization via deep reinforcement learning. In: Proceedings of the 33rd ACM\/IEEE International Conference on Automated Software Engineering, pp. 397\u2013407 (2018)","DOI":"10.1145\/3238147.3238206"},{"key":"341_CR38","unstructured":"Wang, W., Zhang, K., Li, G., Jin, Z.: Learning to represent programs with heterogeneous graphs. arXiv preprint arXiv:201204188 (2020)"},{"key":"341_CR39","unstructured":"Wei, B., Li, G., Xia, X., Fu, Z., Jin, Z.: Code generation as a dual task of code summarization. arXiv preprint arXiv:191005923 (2019)"},{"issue":"10","key":"341_CR40","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1109\/TSE.2017.2734091","volume":"44","author":"X Xia","year":"2017","unstructured":"Xia, X., Bao, L., Lo, D., Xing, Z., Hassan, A.E., Li, S.: Measuring program comprehension: a large-scale field study with professionals. IEEE Trans. Softw. Eng. 44(10), 951\u2013976 (2017)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"341_CR41","doi-asserted-by":"crossref","unstructured":"Xu, S., Zhang, S., Wang, W., Cao, X., Guo, C., Xu, J.: Method name suggestion with hierarchical attention networks. In: Proceedings of the 2019 ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation, pp. 10\u201321 (2019)","DOI":"10.1145\/3294032.3294079"},{"key":"341_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, X., Zhang, H., Sun, H., Liu, X.: Retrieval-based neural source code summarization. In: 2020 IEEE\/ACM 42nd International Conference on Software Engineering (ICSE), pp. 1385\u20131397. IEEE (2020)","DOI":"10.1145\/3377811.3380383"},{"key":"341_CR43","unstructured":"Z\u00fcgner, D., Kirschstein, T., Catasta, M., Leskovec, J., G\u00fcnnemann, S.: Language-agnostic representation learning of source code from structure and context. arXiv preprint arXiv:210311318 (2021)"}],"container-title":["Automated Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-022-00341-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10515-022-00341-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-022-00341-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T11:19:22Z","timestamp":1666955962000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10515-022-00341-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,14]]},"references-count":43,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["341"],"URL":"https:\/\/doi.org\/10.1007\/s10515-022-00341-1","relation":{},"ISSN":["0928-8910","1573-7535"],"issn-type":[{"value":"0928-8910","type":"print"},{"value":"1573-7535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,14]]},"assertion":[{"value":"15 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"43"}}