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This work was partially supported by National Key Research and Development Plan of China (Grant No. 2018YFB1003900).
[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
Figure 1
(Color online) The workflow of DeepDir.
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