Holistic Combination of Structural and Textual Code Information for Context based API Recommendation

• URL: http://arxiv.org/abs/2010.07514v1
• Date: Thu, 15 Oct 2020 04:40:42 GMT
• Title: Holistic Combination of Structural and Textual Code Information for Context based API Recommendation
• Authors: Chi Chen, Xin Peng, Zhenchang Xing, Jun Sun, Xin Wang, Yifan Zhao, and Wenyun Zhao
• Abstract summary: 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 source code based on an API Graph Network and a Code Token Network. We show that our approach achieves a 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.
• Score: 28.74546332681778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. 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.

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