LSTM-based argument recommendation for non-API methods
LSTM-based argument recommendation for non-API methods作者机构：School of Computer Science and Technology Beijing Institute of Technology School of Electronics Engineering and Computer Science Peking University
出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期：2020年第63卷第9期
页 面：5-26页
核心收录：
学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081202[工学-计算机软件与理论]
基 金：supported by National Natural Science Foundation of China (Grant Nos. 61772071,61690205,61832009) National Key R&D Program (Grant Nos. 2018YFB1003904)
主 题：argument recommendation LSTM deep learning non-API
摘 要：Automatic code completion is one of the most useful features provided by advanced IDEs. Argument recommendation, as a special kind of code completion, is widely used as well. While existing approaches focus on argument recommendation for popular APIs, a large number of non-API invocations are requesting for accurate argument recommendation as well. To this end, we propose an LSTM-based approach to recommending non-API arguments instantly when method calls are typed in. With data collected from a large corpus of open-source applications, we train an LSTM neural network to recommend actual arguments based on identifiers of the invoked method, the corresponding formal parameter, and a list of syntactically correct candidate arguments. To feed these identifiers into the LSTM neural network, we convert them into fixed-length vectors by Paragraph Vector, an unsupervised neural network based learning algorithm. With the resulting LSTM neural network trained on sample applications, for a given call site we can predict which of the candidate arguments is more likely to be the correct one. We evaluate the proposed approach with tenfold validation on 85 open-source C applications. Results suggest that the proposed approach outperforms the state-of-the-art approaches in recommending non-API arguments. It improves the precision significantly from 71.46% to 83.37%.
同方期刊数据库