A Simple, Efficient, Context‐sensitive Approach for Code Completion. Issue 7 (10th May 2016)

• Record Type: Journal Article
• Authors: Asaduzzaman, Muhammad; Roy, Chanchal K.; Schneider, Kevin A.; Hou, Daqing
• Other Names: Moonen Leon guestEditor.; Pollock Lori guestEditor.
• Is Part Of: Journal of software. Volume 28:Issue 7(2016)
• Page Start: 512
• Page End: 541
• Publication Date: 2016-05-10
• Subjects: API methods -- code completion -- field completion -- context sensitive -- simhash -- language model; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1791
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Code completion helps developers use application programming interfaces (APIs) and frees them from remembering every detail. In this paper, we first describe a novel technique called Context‐sensitive Code Completion (CSCC) for improving the performance of API method call completion. CSCC is context sensitive in that it uses new sources of information as the context of a target method call. CSCC indexes method calls in code examples by their context. To recommend completion proposals, CSCC ranks candidate methods by the similarities between their contexts and the context of the target call. Evaluation using a set of subject systems and five popular state‐of‐the‐art techniques suggests that CSCC performs better than existing type or example‐based code completion systems. We conduct experiments to find how different contextual elements of the target call benefit CSCC. Next, we investigate the adaptability of the technique to support another form of code completion, i.e., field completion. Evaluation with eight different subject systems suggests that CSCC can easily support field completion with high accuracy. Finally, we compare CSCC with four popular statistical language models that support code completion. Results of statistical tests from our study suggest that CSCC not only outperforms those techniques that are based on token level language models, but also in most cases performs better or equally well with GraLan, the state‐of‐the‐art graph‐based language model. Copyright © 2016 John Wiley & Sons, Ltd.