Technical Q&A site answer recommendation via question boosting

Software developers have heavily used online question-and-answer platforms to seek help to solve their technical problems. However, a major problem with these technical Q8A sites is "answer hungriness,"i.e., a large number of questions remain unanswered or unresolved, and users have to wait for a long time or painstakingly go through the provided answers with various levels of quality. To alleviate this time-consuming problem, we propose a novel DEEPANS neural network-based approach to identify the most relevant answer among a set of answer candidates. Our approach follows a three-stage process: question boosting, label establishment, and answer recommendation. Given apost, we first generate a clarifying question as a way of question boosting. We automatically establish the positive, neutral+, neutral-, and negative training samples via label establishment. When it comes to answer recommendation, we sort answer candidates by the matching scores calculated by our neural network-based model. To evaluate the performance of our proposed model, we conducted a large-scale evaluation on four datasets, collected from the real-world technical Q8A sites (i.e., Ask Ubuntu, Super User, Stack Overflow Python, and Stack Overflow Java). Our experimental results show that our approach significantly outperforms several state-of-the-art baselines in automatic evaluation. We also conducted a user study with 50 solved/unanswered/unresolved questions. The user-study results demonstrate that our approach is effective in solving the answer-hungry problem by recommending the most relevant answers from historical archives.