乐胖代购免代理版

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J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2021,9]]},"abstract":" Application Programming Interfaces (APIs) play an important role in modern software development. Developers interact with APIs on a daily basis and thus need to learn and memorize those APIs suitable for implementing the required functions. This can be a burden even for experienced developers since there exists a mass of available APIs. API recommendation techniques focus on assisting developers in selecting suitable APIs. However, existing API recommendation techniques have not taken the developers personal characteristics into account. As a result, they cannot provide developers with personalized API recommendation services. Meanwhile, they lack the support for self-defined APIs in the recommendation. To this end, we aim to propose a personalized API recommendation method that considers developers\u2019 differences. Our API recommendation method is based on statistical language. We propose a model structure that combines the N-gram model and the long short-term memory (LSTM) neural network and train predictive models using API invoking sequences extracted from GitHub code repositories. A general language model trained on all sorts of code data is first acquired, based on which two personalized language models that recommend personalized library APIs and self-defined APIs are trained using the code data of the developer who needs personalized services. We evaluate our personalized API recommendation method on real-world developers, and the experimental results show that our approach achieves better accuracy in recommending both library APIs and self-defined APIs compared with the state-of-the-art. The experimental results also confirm the effectiveness of our hybrid model structure and the choice of the LSTM\u2019s size. <\/jats:p>","DOI":"10.1142\/s021819402150042x","type":"journal-article","created":{"date-parts":[[2021,10,3]],"date-time":"2021-10-03T11:39:21Z","timestamp":1633261161000},"page":"1299-1327","source":"Crossref","is-referenced-by-count":2,"title":["Personalized API Recommendations"],"prefix":"10.1142","volume":"31","author":[{"given":"Wenhua","family":"Yang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, P. R. China"},{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu 210093, P. R. China"},{"name":"Collaborative Innovation Center of Novel Software, Technology and Industrialization, Nanjing, Jiangsu 210093, P. R. China"}]},{"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, P. R. China"},{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu 210093, P. R. China"}]},{"given":"Zhiqiu","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2021,10,3]]},"reference":[{"key":"S021819402150042XBIB001","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1145\/1065010.1065018","volume-title":"Proc. 2005 ACM SIGPLAN Conf. 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