一句话：提出 FACER 推荐系统，结合信息检索、静态分析与数据挖掘，向开发者推荐与任务功能相关的函数。
开发者需要高效找到可复用的代码，以支持机会式编程并提升开发效率。本文提出 FACER（Feature-driven API usage-based Code Examples Recommender）推荐系统，结合信息检索、静态代码分析和数据挖掘技术，基于 API 使用与函数克隆结构，为开发者推荐与其开发任务功能相关的函数。作者在 GitHub 上选取 122 个不同类别的项目进行实验，评估推荐函数对相关特征的准确性。结果显示，FACER 的推荐精确率在自动评估下为 54%，在人工评估下为 75%。
Developers need to be able to find reusable code for desired software features in a way that supports opportunistic programming for increased developer productivity. Our objective is to develop a recommendation system that provides a developer with function recommendations having functionality relevant to her development task. We employ a combination of information retrieval, static code analysis and data mining techniques to build the proposed recommendation system called FACER (Feature-driven API usage-based Code Examples Recommender). We performed an experimental evaluation on 122 projects from GitHub from selected categories to determine the accuracy of the retrieved code for related features. FACER recommended functions with a precision of 54% and 75% when evaluated using automated and manual methods respectively.
DOI 原文 ·
@inproceedings{paperbot5118,
title = {Recommending related functions from API usage-based function clone structures},
author = {Shamsa Abid},
booktitle = {FSE},
year = {2019},
doi = {10.1145/3338906.3342486}
}