Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2022

Abstract

Developers spend a lot of time online, searching for code to help them implement their desired features. While code recommenders help improve developers’ productivity, there is currently no support for context-aware code recommendation for opportunistic code reuse on-the-go. Typical code recommendation systems provide recommendations against a search query, whereas a code recommender that supports opportunistic reuse can recommend related code snippets that represent features that the developer may want to implement next. In this paper, we present a novel Context-aware Feature-driven API usage-based Code Recommender (CA-FACER) tool, which is an Intellij IDEA plugin that leverages a developer’s development context to recommend related code snippets. We consider the methods having API usages in a developer’s active project as part of the development context. Our approach uses contextual data from a developer’s active project to find similar projects and recommends code from popular features of those projects. The popular features are identified as frequently occurring API usage based Method Clone Classes. From our experimental evaluation on 120 Android Java projects from GitHub, we observe a 46% improvement of precision using our proposed context-aware approach over a baseline system. Our technique recommends related code examples with an average precision (P@5) of 94% and 83% and a success rate of 90% and 95% for initial and evolved development stages respectively. A video demonstration of our tool is available at https://youtu.be/UjuM8WRc318.

Keywords

API Usage, Code Recommendation, Code Reuse, Context-aware, Method Clone Structures, Software Features

Discipline

Artificial Intelligence and Robotics | Software Engineering

Publication

ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Singapore, November 14-18

First Page

1647

Last Page

1651

ISBN

9781450394130

Identifier

10.1145/3540250.3558937

Publisher

ACM

City or Country

New York

Comments

Cited by: 4

Additional URL

https://doi.org/10.1145/3540250.3558937

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