Automated Question Title Reformulation by Mining Modification Logs From Stack Overflow

In Stack Overflow, developers may not clarify and summarize the critical problems in the question titles due to a lack of domain knowledge or poor writing skills. Previous studies mainly focused on automatically generating the question titles by analyzing the posts’ problem descriptions and code snippets. In this study, we aim to improve title quality from the perspective of question title reformulation and propose a novel approach QETRA motivated by the findings of our formative study. Specifically, by mining modification logs from Stack Overflow, we first extract title reformulation pairs containing the original title and the reformulated title. Then we resort to multi-task learning by formalizing title reformulation for each programming language as separate but related tasks. Later we adopt a pre-trained model T5 to automatically learn the title reformulation patterns. Automated evaluation and human study both show the competitiveness of QETRA after compared with six state-of-the-art baselines. Moreover, our ablation study results also confirm that our studied question title reformulation task is more practical than the direct question title generation task for generating high-quality titles. Finally, we develop a browser plugin based on QETRA to facilitate the developers to perform title reformulation. Our study provides a new perspective for studying the quality of post titles and can further generate high-quality titles.