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SCIENCE CHINA Information Sciences, Volume 64 , Issue 9 : 199102(2021) https://doi.org/10.1007/s11432-019-1520-6

DeepDir: a deep learning approach for API directive detection

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  • ReceivedApr 30, 2019
  • AcceptedJul 20, 2019
  • PublishedNov 24, 2020

Abstract

There is no abstract available for this article.


Acknowledgment

This work was partially supported by National Key Research and Development Plan of China (Grant No. 2018YFB1003900).


References

[1] Maalej W, Robillard M P. Patterns of Knowledge in API Reference Documentation. IIEEE Trans Software Eng, 2013, 39: 1264-1282 CrossRef Google Scholar

[2] Jiang H, Zhang J X, Ren Z L, et al. An unsupervised approach for discovering relevant tutorial fragments for APIs. In: Proceedings of the 39th International Conference on Software Engineering (ICSE 17), 2017. 38--48. Google Scholar

[3] Huang Q, Xia X, Xing Z C, et al. API method recommendation without worrying about the task-API knowledge gap. In: Proceedings of International Conference on Automated Software Engineering (ASE 18), 2018. 293--304. Google Scholar

[4] Robillard M P, Chhetri Y B. Recommending reference API documentation. Empir Software Eng, 2015, 20: 1558-1586 CrossRef Google Scholar

[5] Monperrus M, Eichberg M, Tekes E. What should developers be aware of? An empirical study on the directives of API documentation. Empir Software Eng, 2012, 17: 703-737 CrossRef Google Scholar

[6] Hu X, Li G, Xia X, et al. Deep code comment generation. In: Proceedings of IEEE International Conference on Program Comprehension (ICPC 18), 2018. 200--210. Google Scholar

[7] Chen X, Jiang H, Chen Z. Automatic test report augmentation to assist crowdsourced testing. Front Comput Sci, 2019, 13: 943-959 CrossRef Google Scholar

[8] Li X C, Jiang H, Kamei Y, et al. Bridging semantic gaps between natural languages and APIs with word embedding. IEEE Trans Softw Eng, 2018. doi: 10.1109/TSE.2018.2876006. Google Scholar