Code2Que: A Tool for Improving Question Titles from Mined Code Snippets in Stack Overflow

TL;DR

Code2Que is a web-based tool that leverages deep learning to generate high-quality question titles from code snippets, aiming to improve question clarity and help less experienced developers formulate better questions on Stack Overflow.

Contribution

This paper introduces a novel deep learning-based approach with attention, copy, and coverage mechanisms to automatically generate question titles from code snippets, enhancing question quality.

Findings

The model effectively generates relevant question titles from code snippets.

Code2Que improves question quality and helps less experienced users.

The retrieval component finds similar questions based on code embeddings.

Abstract

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. At the same time, we embed the given code snippet into a vector and retrieve the related questions with similar problematic code snippets.