Technical Q8A Site Answer Recommendation via Question Boosting

Abstract

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 D EEP A NS 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 a post, 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.