一句话：提出 Code2Que 工具，用深度 seq2seq 从代码片段生成 Stack Overflow 问题标题，提升低质量问题标题质量。
Stack Overflow 上大量问题质量偏低，低经验开发者难以为代码片段写出清晰、贴切的问题标题。Code2Que 是一个基于 Web 的工具，分离线学习与在线推荐两阶段：离线从高质量〈代码片段, 问题〉对中训练带注意力、复制与覆盖机制的深度 sequence-to-sequence 模型；在线时给定代码片段生成候选标题，辅助用户编辑。评估从 Stack Overflow 的 Python 和 Java 数据集中抽取 50 个低质量〈代码片段, 问题〉对，并开展用户研究，从 Clearness、Fitness 和 Willingness to Respond 三个指标比较生成标题与人工标题。实验表明 Code2Que 能在多数量低质量问题上改进标题质量。
Stack Overflow is one of the most popular technical Q&A sites used by software developers. Seeking help from Stack Overflow has become an essential part of software developers’ daily work for solving programming-related questions. Although the Stack Overflow community has provided quality assurance guidelines to help users write better questions, we observed that a significant number of questions submitted to Stack Overflow are of low quality. In this paper, we introduce a new web-based tool, Code2Que, which can help developers in writing higher quality questions for a given code snippet. Code2Que consists of two main stages: offline learning and online recommendation. In the offline learning phase, we first collect a set of good quality ⟨code snippet, question⟩ pairs as training samples. We then train our model on these training samples via a deep sequence-to-sequence approach, enhanced with an attention mechanism, a copy mechanism and a coverage mechanism. In the online recommendation phase, for a given code snippet, we use the offline trained model to generate question titles to assist less experienced developers in writing questions more effectively. To evaluate Code2Que, we first sampled 50 low quality ⟨code snippet, question⟩ pairs from the Python and Java datasets on Stack Overflow. Then we conducted a user study to evaluate the question titles generated by our approach as compared to human-written ones using three metrics: Clearness, Fitness and Willingness to Respond. Our experimental results show that for a large number of low-quality questions in Stack Overflow, Code2Que can improve the question titles in terms of Clearness, Fitness and Willingness measures.
DOI 原文 ·
@inproceedings{paperbot6396,
title = {Code2Que: a tool for improving question titles from mined code snippets in stack overflow},
author = {Zhipeng Gao and Xin Xia and David Lo and John Grundy and Yuan-Fang Li},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3473114}
}