{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:23:00Z","timestamp":1743056580331,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031545207"},{"type":"electronic","value":"9783031545214"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-54521-4_19","type":"book-chapter","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T06:08:12Z","timestamp":1708582092000},"page":"343-362","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CUTE: A Collaborative Fusion Representation-Based Fine-Tuning and\u00a0Retrieval Framework for\u00a0Code Search"],"prefix":"10.1007","author":[{"given":"Qihong","family":"Song","sequence":"first","affiliation":[]},{"given":"Jianxun","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Haize","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"issue":"9","key":"19_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3480027","volume":"54","author":"C Liu","year":"2021","unstructured":"Liu, C., Xia, X., Lo, D., Gao, C., Yang, X., Grundy, J.: Opportunities and challenges in code search tools. ACM Comput. Surv. (CSUR) 54(9), 1\u201340 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"19_CR2","unstructured":"Linstead, E., Rigor, P., Bajracharya, S., Lopes, C., Baldi, P.: Mining internet-scale software repositories. In: Advances in Neural Information Processing Systems, vol. 20 (2007)"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Lu, M., Sun, X., Wang, S., Lo, D., Duan, Y.: Query expansion via wordnet for effective code search. In: 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER), pp. 545\u2013549. IEEE (2015)","DOI":"10.1109\/SANER.2015.7081874"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Lv, F., Zhang, H., Lou, J.G., Wang, S., Zhang, D., Zhao, J.: Codehow: effective code search based on api understanding and extended boolean model (e). In: 2015 30th IEEE\/ACM International Conference on Automated Software Engineering (ASE), pp. 260\u2013270. IEEE (2015)","DOI":"10.1109\/ASE.2015.42"},{"issue":"5","key":"19_CR5","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1145\/175290.175300","volume":"37","author":"TJ Biggerstaff","year":"1994","unstructured":"Biggerstaff, T.J., Mitbander, B.G., Webster, D.E.: Program understanding and the concept assignment problem. Commun. ACM 37(5), 72\u201382 (1994)","journal-title":"Commun. ACM"},{"key":"19_CR6","unstructured":"Bellet, A., Habrard, A., Sebban, M.: A survey on metric learning for feature vectors and structured data. arXiv preprint arXiv:1306.6709 (2013)"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Gu, X., Zhang, H., Kim, S.: Deep code search. In: Proceedings of the 40th International Conference on Software Engineering, pp. 933\u2013944 (2018)","DOI":"10.1145\/3180155.3180167"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Cambronero, J., Li, H., Kim, S., Sen, K., Chandra, S.: When deep learning met code search. In: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 964\u2013974 (2019)","DOI":"10.1145\/3338906.3340458"},{"key":"19_CR9","doi-asserted-by":"publisher","first-page":"106542","DOI":"10.1016\/j.infsof.2021.106542","volume":"134","author":"S Fang","year":"2021","unstructured":"Fang, S., Tan, Y.S., Zhang, T., Liu, Y.: Self-attention networks for code search. Inf. Softw. Technol. 134, 106542 (2021)","journal-title":"Inf. Softw. Technol."},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., Yin, J.: Unixcoder: unified cross-modal pre-training for code representation. arXiv preprint arXiv:2203.03850 (2022)","DOI":"10.18653\/v1\/2022.acl-long.499"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Feng, Z., et al.: 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":"19_CR12","unstructured":"Guo, D., et al.: Graphcodebert: pre-training code representations with data flow. arXiv preprint arXiv:2009.08366 (2020)"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Liu, S., Wu, B., Xie, X., Meng, G., Liu, Y.: Contrabert: enhancing code pre-trained models via contrastive learning. arXiv preprint arXiv:2301.09072 (2023)","DOI":"10.1109\/ICSE48619.2023.00207"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Niu, C., Li, C., Luo, B., Ng, V.: Deep learning meets software engineering: a survey on pre-trained models of source code. arXiv preprint arXiv:2205.11739 (2022)","DOI":"10.24963\/ijcai.2022\/775"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Ge, W.: Deep metric learning with hierarchical triplet loss. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 269\u2013285 (2018)","DOI":"10.1007\/978-3-030-01231-1_17"},{"key":"19_CR16","unstructured":"Robinson, J., Chuang, C.Y., Sra, S., Jegelka, S.: Contrastive learning with hard negative samples. arXiv preprint arXiv:2010.04592 (2020)"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Harwood, B., Kumar BG, V., Carneiro, G., Reid, I., Drummond, T.: Smart mining for deep metric learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2821\u20132829 (2017)","DOI":"10.1109\/ICCV.2017.307"},{"issue":"5","key":"19_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447571","volume":"15","author":"X Ling","year":"2021","unstructured":"Ling, X., et al.: Deep graph matching and searching for semantic code retrieval. ACM Trans. Knowl. Disc. Data (TKDD) 15(5), 1\u201321 (2021)","journal-title":"ACM Trans. Knowl. Disc. Data (TKDD)"},{"key":"19_CR19","unstructured":"Wang, X., et al.: Syncobert: syntax-guided multi-modal contrastive pre-training for code representation. arXiv preprint arXiv:2108.04556 (2021)"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Suh, Y., Han, B., Kim, W., Lee, K.M.: Stochastic class-based hard example mining for deep metric learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7251\u20137259 (2019)","DOI":"10.1109\/CVPR.2019.00742"},{"issue":"12","key":"19_CR21","doi-asserted-by":"publisher","first-page":"5947","DOI":"10.1109\/TNNLS.2018.2817340","volume":"29","author":"Z Yu","year":"2018","unstructured":"Yu, Z., Yu, J., Xiang, C., Fan, J., Tao, D.: Beyond bilinear: Generalized multimodal factorized high-order pooling for visual question answering. IEEE Trans. Neural Netw. Learn. Syst. 29(12), 5947\u20135959 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Li, L., Dong, R., Chen, L.: Context-aware co-attention neural network for service recommendations. In: 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), pp. 201\u2013208. IEEE (2019)","DOI":"10.1109\/ICDEW.2019.00-11"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Li, B., Sun, Z., Li, Q., Wu, Y., Hu, A.: Group-wise deep object co-segmentation with co-attention recurrent neural network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8519\u20138528 (2019)","DOI":"10.1109\/ICCV.2019.00861"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"11","key":"19_CR25","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"19_CR26","unstructured":"Wang, H., Zhang, J., Xia, Y., Bian, J., Zhang, C., Liu, T.Y.: Cosea: convolutional code search with layer-wise attention. arXiv preprint arXiv:2010.09520 (2020)"},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Gu, J., Chen, Z., Monperrus, M.: Multimodal representation for neural code search. In: 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp. 483\u2013494. IEEE (2021)","DOI":"10.1109\/ICSME52107.2021.00049"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, X., Zhang, H., Sun, H., Wang, K., Liu, X.: A novel neural source code representation based on abstract syntax tree. In: 2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE), pp. 783\u2013794. IEEE (2019)","DOI":"10.1109\/ICSE.2019.00086"},{"key":"19_CR29","doi-asserted-by":"crossref","unstructured":"Ling, C., Lin, Z., Zou, Y., Xie, B.: Adaptive deep code search. In: Proceedings of the 28th International Conference on Program Comprehension, pp. 48\u201359 (2020)","DOI":"10.1145\/3387904.3389278"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Chai, Y., Zhang, H., Shen, B., Gu, X.: Cross-domain deep code search with meta learning. In: Proceedings of the 44th International Conference on Software Engineering, pp. 487\u2013498 (2022)","DOI":"10.1145\/3510003.3510125"},{"key":"19_CR31","unstructured":"Husain, H., Wu, H.H., Gazit, T., Allamanis, M., Brockschmidt, M.: Codesearchnet challenge: evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436 (2019)"},{"key":"19_CR32","unstructured":"Tipirneni, S., Zhu, M., Reddy, C.K.: Structcoder: structure-aware transformer for code generation. arXiv preprint arXiv:2206.05239 (2022)"},{"key":"19_CR33","doi-asserted-by":"publisher","first-page":"109635","DOI":"10.1109\/ACCESS.2019.2933042","volume":"7","author":"H Ma","year":"2019","unstructured":"Ma, H., Li, Y., Ji, X., Han, J., Li, Z.: Mscoa: multi-step co-attention model for multi-label classification. IEEE Access 7, 109635\u2013109645 (2019)","journal-title":"IEEE Access"},{"key":"19_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, P., Zhu, H., Xiong, T., Yang, Y.: Co-attention network and low-rank bilinear pooling for aspect based sentiment analysis. In: ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6725\u20136729. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8682248"},{"key":"19_CR35","doi-asserted-by":"crossref","unstructured":"Shuai, J., Xu, L., Liu, C., Yan, M., Xia, X., Lei, Y.: Improving code search with co-attentive representation learning. In: Proceedings of the 28th International Conference on Program Comprehension, pp. 196\u2013207 (2020)","DOI":"10.1145\/3387904.3389269"},{"key":"19_CR36","unstructured":"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)"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Collaborative Computing: Networking, Applications and Worksharing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-54521-4_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T06:01:53Z","timestamp":1731391313000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-54521-4_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031545207","9783031545214"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-54521-4_19","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CollaborateCom","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Collaborative Computing: Networking, Applications and Worksharing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Corfu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"colcom2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Cony +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"176","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}