Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2021

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 Q&A 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 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 Q&A 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.

Keywords

CQA, Question Boosting, Question Answering, Sequence-to-sequence, Deep Neural Network, Weakly Supervised Learning

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Software Engineering and Methodology

Volume

30

Issue

1

First Page

1

Last Page

34

ISSN

1049-331X

Identifier

10.1145/3412845

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://xin-xia.github.io/publication/tosem205.pdf

Download

Share

COinS