  Community Question Answering platforms such as Stack Overflow help a wide
range of users solve their challenges online. As the popularity of these
communities has grown over the years, both the number of members and posts have
escalated. Also, due to the diverse backgrounds, skills, expertise, and
viewpoints of users, each question may obtain more than one answers. Therefore,
the focus has changed toward producing posts that have a set of answers more
valuable for the community as a whole, not just one accepted-answer aimed at
satisfying only the question-asker. Same as every universal community, a large
number of low-quality posts on Stack Overflow require improvement. We call
these posts deficient and define them as posts with questions that either have
no answer yet or can be improved by other ones. In this paper, we propose an
approach to automate the identification process of such posts and boost their
set of answers, utilizing the help of related experts. With the help of 60
participants, we trained a classification model to identify deficient posts by
investigating the relationship between characteristics of 3075 questions posted
on Stack Overflow and their need for better answers set. Then, we developed an
Eclipse plugin named SOPI and integrated the prediction model in the plugin to
link these deficient posts to related developers and help them improve the
answer set. We evaluated both the functionality of our plugin and the impact of
answers submitted to Stack Overflow with the help of 10 and 15 expert
industrial developers, respectively. Our results indicate that decision trees,
specifically J48, predicts a deficient question better than the other methods
with 0.945 precision and 0.903 recall. We conclude that not only our plugin
helps programmers contribute more easily to Stack Overflow, but also it
improves the quality of answers.
