FeaRS: Recommending Complete Android Method Implementations

Fengcai Wen, Valentina Ferrari, Emad Aghajani, Csaba Nagy, Michele Lanza, G. Bavota
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引用次数: 0

Abstract

Several techniques have been proposed in the literature to support code completion, showing excellent results in predicting the next few tokens a developer is likely to type given the current context. Only recently, approaches pushing the boundaries of code completion (e.g., by presenting entire code statements) have been proposed. In this line of research, we present FeaRS, a recommender system that, given the current code a developer is writing in the IDE, recommends the next complete method to be implemented. FeaRS has been deployed to learn “implementation patterns” (i.e., groups of methods usually implemented within the same task) by continuously mining open-source Android projects. Such knowledge is leveraged to provide method recommendations when the code written by the developer in the IDE matches an “implementation pattern”. Preliminary results of FeaRS’ accuracy show its potential as well as some open challenges to overcome.
恐惧:推荐完整的Android方法实现
文献中已经提出了几种技术来支持代码完成,在预测开发人员在给定当前上下文下可能键入的下几个令牌方面显示了出色的结果。直到最近,才有人提出了突破代码完成边界的方法(例如,通过呈现整个代码语句)。在这方面的研究中,我们提出了FeaRS,这是一个推荐系统,根据开发人员在IDE中编写的当前代码,推荐下一个要实现的完整方法。FeaRS通过不断挖掘开源Android项目来学习“实现模式”(即通常在同一任务中实现的方法组)。当开发人员在IDE中编写的代码与“实现模式”匹配时,利用这些知识提供方法建议。FeaRS准确性的初步结果显示了它的潜力以及一些有待克服的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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