Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow
This addresses the tedious task of query reformulation for developers, especially novices, on Stack Overflow, though it is incremental as it builds on existing deep learning methods for query reformulation.
The paper tackles the problem of inefficient search on Stack Overflow due to gaps between user intent and queries, and between queries and content, by proposing an automated query reformulation approach using deep learning. The result is a Transformer model that outperforms five state-of-the-art baselines, achieving boosts of 5.6% to 33.5% in ExactMatch and 4.8% to 14.4% in GLEU.
As a popular Q&A site for programming, Stack Overflow is a treasure for developers. However, the amount of questions and answers on Stack Overflow make it difficult for developers to efficiently locate the information they are looking for. There are two gaps leading to poor search results: the gap between the user's intention and the textual query, and the semantic gap between the query and the post content. Therefore, developers have to constantly reformulate their queries by correcting misspelled words, adding limitations to certain programming languages or platforms, etc. As query reformulation is tedious for developers, especially for novices, we propose an automated software-specific query reformulation approach based on deep learning. With query logs provided by Stack Overflow, we construct a large-scale query reformulation corpus, including the original queries and corresponding reformulated ones. Our approach trains a Transformer model that can automatically generate candidate reformulated queries when given the user's original query. The evaluation results show that our approach outperforms five state-of-the-art baselines, and achieves a 5.6% to 33.5% boost in terms of $\mathit{ExactMatch}$ and a 4.8% to 14.4% boost in terms of $\mathit{GLEU}$.