Holistic combination of structural and textual code information for context based API recommendation

• Main Authors: CHEN, Chi, PENG, Xin, XING, Zhengchang, SUN, Jun, WANG, Xin, ZHAO, Yifan, ZHAO, Wenyun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Semantics; Deep Learning; Data Models; Context Modeling; Computational Modeling; Task Analysis; Token Networks; API; Recommendation; Data Flow; Control Flow; Text; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6714; https://ink.library.smu.edu.sg/context/sis_research/article/7717/viewcontent/09409670.pdf
• Institution: Singapore Management University

Context based API recommendation is an important way to help developers find the needed APIs effectively and efficiently. For effective API recommendation, we need not only a joint view of both structural and textual code information, but also a holistic view of correlated API usage in control and data flow graph as a whole. Unfortunately, existing API recommendation methods exploit structural or textual code information separately. In this work, we propose a novel API recommendation approach called APIRec-CST (API Recommendation by Combining Structural and Textual code information). APIRec-CST is a deep learning model that combines the API usage with the text information in the source code based on an API Context Graph Network and a Code Token Network that simultaneously learn structural and textual features for API recommendation. We apply APIRec-CST to train a model for JDK library based on 1,914 open-source Java projects and evaluate the accuracy and MRR (Mean Reciprocal Rank) of API recommendation with another 6 open-source projects. The results show that our approach achieves respectively a top-1, top-5, top-10 accuracy and MRR of 60.3%, 81.5%, 87.7% and 69.4%, and significantly outperforms an existing graph-based statistical approach and a tree-based deep learning approach for API recommendation. A further analysis shows that textual code information makes sense and improves the accuracy and MRR. The sensitivity analysis shows that the top-k accuracy and MRR of APIRec-CST are insensitive to the number of APIs to be recommended in a hole. We also conduct a user study in which two groups of students are asked to finish 6 programming tasks with or without our APIRec-CST plugin. The results show that APIRec-CST can help the students to finish the tasks faster and more accurately and the feedback on the usability is overwhelmingly positive.