Semantic similarity-based program retrieval: a multi-relational graph perspective
Qianwen GOU, Yunwei DONG, YuJiao WU, Qiao KE
Semantic similarity-based program retrieval: a multi-relational graph perspective
[1] |
Sadowski C, Stolee K T, Elbaum S. How developers search for code: a case study. In: Proceedings of the 10th Joint Meeting on Foundations of Software Engineering. 2015, 191–201
|
[2] |
Ling X, Wu L, Wang S, Pan G, Ma T, Xu F, Liu A X, Wu C, Ji S . Deep graph matching and searching for semantic code retrieval. ACM Transactions on Knowledge Discovery from Data, 2021, 15( 5): 88
|
[3] |
Yamaguchi F, Golde N, Arp D, Rieck K. Modeling and discovering vulnerabilities with code property graphs. In: Proceedings of 2014 IEEE Symposium on Security and Privacy. 2014, 590–604
|
[4] |
Banarescu L, Bonial C, Cai S, Georgescu M, Griffitt K, Hermjakob U, Knight K, Koehn P, Palmer M, Schneider N. Abstract meaning representation for sembanking. In: Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse. 2013, 178–186
|
[5] |
Feng Z, Guo D, Tang D, Duan N, Feng X, Gong M, Shou L, Qin B, Liu T, Jiang D, Zhou M. CodeBERT: a pre-trained model for programming and natural languages. In: Proceedings of Findings of the Association for Computational Linguistics: EMNLP 2020. 2020, 1536–1547
|
[6] |
Cambronero J, Li H, Kim S, Sen K, Chandra S. When deep learning met code search. In: Proceedings of the 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 2019, 964−974
|
[7] |
Gu X, Zhang H, Kim S. Deep code search. In: Proceedings of the 40th IEEE/ACM International Conference on Software Engineering. 2018, 933−944
|
[8] |
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. 2020, 196−207
|
[9] |
Xu L, Yang H, Liu C, Shuai J, Yan M, Lei Y, Xu Z. Two-stage attention-based model for code search with textual and structural features. In: Proceedings of 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). 2021, 342−353
|
/
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