一句话：构建大规模人工标注的用户评论-代码配对数据集，并建立监督式 Review2Code 基准以对比监督与无监督方法。
移动应用的用户评论（UR）能为应用的维护与演化提供有价值反馈，因此如何更高效地利用用户评论来缩短发布周期成为软件工程社区关注的问题，其中「基于用户评论的代码定位」（Review2Code）被提出并深入研究。然而，由于缺乏大规模的真实配对（ground truth）数据集，现有方法全部基于无监督学习，监督式方法难以施展。为点亮由大规模标注数据驱动的监督学习路径，本文首先构建了一个大规模人工标注的真实配对数据集，并详细描述了标注流程与统计分析；随后在该数据集之上搭建了一个基准，包含两个当前最优的无监督 Review2Code 方法与四个监督式 Review2Code 方法，用于系统比较两类方法的性能。作者希望该工作能为监督式 Review2Code 方案的深入探索提供基础。
As User Reviews (URs) of mobile Apps are proven to provide valuable feedback for maintaining and evolving applications, how to make full use of URs more efficiently in the release cycle of mobile Apps has become a widely concerned and researched topic in the Software Engineering (SE) community. In order to speed up the completion of coding work related to URs to shorten the release cycle as much as possible, the task of User Review-based code localization is proposed and studied in depth. However, due to the lack of large-scale ground truth dataset (i.e., truly related pairs), existing methods are all unsupervised learning-based. In order to light up supervised learning approaches, which are driven by large labeled datasets, for Review2Code, and to compare their performances with unsupervised learning-based methods, we first introduce a large-scale human-labeled ground truth dataset, including the annotation process and statistical analysis. Then, a benchmark consisting of two SOTA unsupervised learning-based and four supervised learning-based Review2Code methods is constructed based on this dataset. We believe that this paper can provide a basis for in-depth exploration of the supervised learning-based Review2Code solutions.
DOI 原文 ·
@inproceedings{paperbot7102,
title = {Lighting up supervised learning in user review-based code localization: dataset and benchmark},
author = {Xinwen Hu and Yu Guo and Jianjie Lu and Zheling Zhu and Chuanyi Li and Jidong Ge and Liguo Huang and Bin Luo},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3549141}
}