HintMiner is an automated tool that assists users in quickly obtaining possible hints after posting a question, thereby increasing the chance of receiving satisfactory answers on QA platforms such as Stack Overflow. The system first crawls a large collection of web Q&A posts and then uses MiningNet to extract potential hints from them. MiningNet is an encoder‑decoder model built on the Transformer architecture and incorporates a copy mechanism, allowing the generated hints to directly reuse key fragments from the original posts. To address the scarcity of labeled data, the authors devise a self‑supervised learning objective: randomly mask several sentences in a post as “hints”, and the model must reconstruct the masked sentences given the remaining context. This objective enables effective training on massive unlabeled Q&A data.
The approach was evaluated on 60,000 Stack Overflow questions, achieving an average BLEU score of 36.17% and an average ROUGE‑2 score of 36.29%, substantially outperforming baseline methods. Both the tool and the experimental data are publicly released for reproducibility and further research.
Review: By leveraging self‑supervised learning, the method makes full use of publicly available Q&A data and demonstrates the practical potential of language models in assisting real‑world programming tasks.