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To obtain code snippets for reuse, programmers prefer to search for related documents, e.g., blogs or Q&A, instead of code itself.
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arXiv: 1703.01443
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2017
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F. Zhang, H. Niu, I. Keivanloo, and Y. Zou, “Expanding Queries for Code Search Using Semantically Related API Class-names,” IEEE Transactions on Software Engineering
2018
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ISSN: 1534-5351
Z. Sun, Y. Liu, Z. Cheng, C. Yang, and P. Che, “Req2Lib: A Semantic Neural Model for Software Library Recommendation,” in 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER) · 2020
Closest in time.
J. Shuai, L. Xu, C. Liu, M. Yan, X. Xia, and Y. Lei, “Improving Code Search with Co-Attentive Representation Learning,” Accepted by 28th International Conference on Program Comprehension (ICPC)
2020
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W. Ye, R. Xie, J. Zhang, T. Hu, X. Wang, and S. Zhang, “Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning,” p. 11, 2020
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2020
Closest in time.
S. Yan, H. Yu, Y. Chen, B. Shen, and L. Jiang, “Are the Code Snippets What We Are Searching for? A Benchmark and an Empirical Study on Code Search with Natural-Language Queries,” in 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)
2020
Closest in time.