1 |
SEKER A, DIRI B, ARSLAN H, et al. Open source software development challenges: a systematic literature review on GitHub[J]. International Journal of Open Source Software and Processes, 2020, 11(4): 1-26. 10.4018/ijossp.2020100101
|
2 |
GOUSIOS G, SPINELLIS D. GHTorrent: GitHub's data from a firehose[C]// Proceedings of the 9th IEEE Working Conference on Mining Software Repositories. Piscataway: IEEE, 2012: 12-21. 10.1109/msr.2012.6224294
|
3 |
陈丹,王星,何鹏,等. 开源社区中已有开发者的合作行为分析[J]. 计算机科学, 2016, 43(6A):476-479, 501. 10.11896/j.issn.1002-137X.2016.6A.112
|
|
CHEN D, WANG X, HE P, et al. Towards understanding existing developers’ collaborative behavior in OSS communities[J]. Computer Science, 2016, 43(6A):476-479, 501. 10.11896/j.issn.1002-137X.2016.6A.112
|
4 |
RAHMAN M M, ROY C K, REDL J, et al. CORRECT: code reviewer recommendation at GitHub for Vendasta technologies[C]// Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering. New York: ACM, 2016: 792-797. 10.1145/2970276.2970283
|
5 |
MONTANDON J E, SILVA L L, VALENTE M T. Identifying experts in software libraries and frameworks among GitHub users[C]// Proceedings of the IEEE/ACM 16th International Conference on Mining Software Repositories. Piscataway: IEEE, 2019: 276-287. 10.1109/msr.2019.00054
|
6 |
MANES S S, BAYSAL O. How often and what StackOverflow posts do developers reference in their GitHub projects?[C]// Proceedings of the IEEE/ACM 16th International Conference on Mining Software Repositories. Piscataway: IEEE, 2019:235-239. 10.1109/msr.2019.00047
|
7 |
BALTES S, DUMANI L, TREUDE C, et al. SOTorrent: reconstructing and analyzing the evolution of stack overflow posts[C]// Proceedings of the ACM/IEEE 15th International Conference on Mining Software Repositories. New York: ACM, 2018: 319-330. 10.1145/3196398.3196430
|
8 |
BALTES S, TREUDE C, DIEHL S. SOTorrent: studying the origin, evolution, and usage of stack overflow code snippets[C]// Proceedings of the IEEE/ACM 16th International Conference on Mining Software Repositories. Piscataway: IEEE, 2019: 191-194. 10.1109/msr.2019.00038
|
9 |
ZEROUALI A, MENS T, ROBLES G, et al. On the diversity of software package popularity metrics: an empirical study of NPM[C]// Proceedings of the IEEE 26th International Conference on Software Analysis, Evolution and Reengineering. Piscataway: IEEE, 2019: 589-593. 10.1109/saner.2019.8667997
|
10 |
张云帆,周宇,黄志球. 基于语义相似度的API使用模式推荐[J]. 计算机科学, 2020, 47(3): 34-40. 10.11896/jsjkx.190300053
|
|
ZHANG Y F, ZHOU Y, HUANG Z Q. Semantic similarity based API usage pattern recommendation[J]. Computer Science, 2020, 47(3): 34-40. 10.11896/jsjkx.190300053
|
11 |
LIN Z Q, ZOU Y Z, ZHAO J F, et al. Improving software text retrieval using conceptual knowledge in source code[C]// Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering. Piscataway: IEEE, 2017: 123-134. 10.1109/ase.2017.8115625
|
12 |
HUANG Q, XIA X, XING Z C, et al. API method recommendation without worrying about the task‑API knowledge gap[C]// Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering. Piscataway: IEEE, 2018: 293-304. 10.1145/3238147.3238191
|
13 |
NIE L M, HE J, REN Z L, et al. Query expansion based on crowd knowledge for code search[J]. IEEE Transactions on Services Computing, 2016, 9(5): 771-783. 10.1109/tsc.2016.2560165
|
14 |
XIE W K, PENG X, LIU M W, et al. API method recommendation via explicit matching of functionality verb phrases[C]// Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York: ACM, 2020: 1015-1026. 10.1145/3368089.3409731
|
15 |
SHATNAWI A, SHATNAWI H, SAIED M A, et al. Identifying software components from object‑oriented APIs based on dynamic analysis[C]// Proceedings of the 2018 ACM/IEEE 26th International Conference on Program Comprehension. New York: ACM, 2018: 189-199. 10.1145/3196321.3196349
|
16 |
SHATNAWI A, SERIAI A, SAHRAOUI H, et al. Mining software components from object‑oriented APIs[EB/OL]. (2016-06-02) [2021-05-06]. . 10.1007/978-3-319-14130-5_23
|
17 |
MATOS A S, FERREIRA FILHO J B, ROCHA L S. Splitting APIs: an exploratory study of software unbundling[C]// Proceedings of the 2019 IEEE/ACM 16th International Conference on Mining Software Repositories. Piscataway: IEEE, 2019: 360-370. 10.1109/msr.2019.00062
|
18 |
杨程,范强,王涛,等. 基于多维特征的开源项目个性化推荐方法[J]. 软件学报, 2017, 28(6): 1357-1372. 10.13328/j.cnki.jos.005230
|
|
YANG C, FAN Q, WANG T, et al. Multi‑feature based personal recommendation approach for open source project[J]. Journal of Software, 2017, 28(6): 1357-1372. 10.13328/j.cnki.jos.005230
|
19 |
SHEN Q, XIE B, ZOU Y Z, et al. NLI2Code: reusing libraries with natural language interface[C]// Proceedings of the 2019 International Conference on Software and Systems Reuse, LNCS 11602. Cham: Springer, 2019: 168-184.
|
20 |
ZIMMERMAN K, RUPAKHETI C R. An automated framework for recommending program elements to novices[C]// Proceedings of the 30th IEEE/ACM International Conference on Automated Software Engineering. Piscataway: IEEE, 2015: 283-288. 10.1109/ase.2015.54
|
21 |
KIM J, LEE S, HWANG S W, et al. Enriching documents with examples: a corpus mining approach[J]. ACM Transactions on Information Systems, 2013, 31(1): No.1. 10.1145/2414782.2414783
|
22 |
ZHONG H, XIE T, ZHANG L, et al. MAPO: mining and recommending API usage patterns[C]// Proceedings of the 2009 European Conference on Object‑Oriented Programming, LNCS 5653. Berlin: Springer, 2009: 318-343.
|
23 |
WANG J, DANG Y N, ZHANG H Y, et al. Mining succinct and high‑coverage API usage patterns from source code[C]// Proceedings of the 10th Working Conference on Mining Software Repositories. Piscataway: IEEE, 2013: 319-328. 10.1109/msr.2013.6624045
|
24 |
NGUYEN T T, NGUYEN H A, PHAM N H, et al. Graph‑based mining of multiple object usage patterns[C]// Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering. New York: ACM, 2009: 383-392. 10.1145/1595696.1595767
|
25 |
NGUYEN T T, PHAM H V, VU P M, et al. Learning API usages from bytecode: a statistical approach[C]// Proceedings of the IEEE/ACM 38th International Conference on Software Engineering. New York: ACM, 2016: 416-427. 10.1145/2884781.2884873
|
26 |
GU X D, ZHANG H Y, KIM S. CodeKernel: a graph kernel based approach to the selection of API usage examples[C]// Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering. Piscataway: IEEE, 2019: 590-601. 10.1109/ase.2019.00061
|
27 |
CHEN C, PENG X, XING Z C, et al. Holistic combination of structural and textual code information for context based API recommendation[J]. IEEE Transactions on Software Engineering, 2022, 48(8): 2987-3009. 10.1109/tse.2021.3074309
|
28 |
BORGWARDT K M, KRIEGEL H P. Shortest‑path kernels on graphs[C]// Proceedings of the 5th IEEE International Conference on Data Mining. Piscataway: IEEE, 2005: 74-81.
|