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Stack Overflow has been heavily used by software developers as a popular wayto seek programming-related information from peers via the internet. The StackOverflow community recommends users to provide the related code snippet whenthey are creating a question to help others better understand it and offertheir help. Previous studies have shown that} a significant number of thesequestions are of low-quality and not attractive to other potential experts inStack Overflow. These poorly asked questions are less likely to receive usefulanswers and hinder the overall knowledge generation and sharing process.Considering one of the reasons for introducing low-quality questions in SO isthat many developers may not be able to clarify and summarize the key problemsbehind their presented code snippets due to their lack of knowledge andterminology related to the problem, and/or their poor writing skills, in thisstudy we propose an approach to assist developers in writing high-qualityquestions by automatically generating question titles for a code snippet usinga deep sequence-to-sequence learning approach. Our approach is fullydata-driven and uses an attention mechanism to perform better contentselection, a copy mechanism to handle the rare-words problem and a coveragemechanism to eliminate word repetition problem. We evaluate our approach onStack Overflow datasets over a variety of programming languages (e.g., Python,Java, Javascript, C# and SQL) and our experimental results show that ourapproach significantly outperforms several state-of-the-art baselines in bothautomatic and human evaluation. We have released our code and datasets tofacilitate other researchers to verify their ideas and inspire the follow-upwork.

Generating Question Titles for Stack Overflow from Mined Code Snippets