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 ExactMatch and a 4.8% to 14.4% boost in terms of GLEU.

Software defect prediction can make software quality assurance (SQA) process more efficient, economic and targeted.Previous studies mainly focused on classifying software modules as defect-prone or not.However, prediction the number of defects for a new software module is rarely investigated.Moreover, these studies built models independently for each project, which may ignore the relatedness among multiple projects.To effectively utilize the relatedness, we propose a novel approach MPR (multiproject regression) for SDNP (software defect number prediction).To verify the effectiveness of MPR, we perform experimental studies on 30 real-world projects and compare our approach with 6 state-of-the-art baselines (i.e., LR, NNR, SVR, DTR, BRR and DBR).AAE (Average absolute error) and ARE (average relative error) performance measures are used to evaluate the performance of MPR.The results show MPR can achieve better performance in most cases, which indicates the competitiveness of MPR in the context of SDNP.