  A fast and effective approach to obtain information regarding software
development problems is to search them to find similar solved problems or post
questions on community question answering (CQA) websites. Solving coding
problems in a short time is important, so these CQAs have a considerable impact
on the software development process. However, if developers do not get their
expected answers, the websites will not be useful, and software development
time will increase. Stack Overflow is the most popular CQA concerning
programming problems. According to its rules, the only sign that shows a
question poser has achieved the desired answer is the user's acceptance. In
this paper, we investigate unresolved questions, without accepted answers, on
Stack Overflow. The number of unresolved questions is increasing. As of August
2019, 47% of Stack Overflow questions were unresolved. In this study, we
analyze the effectiveness of various features, including some novel features,
to resolve a question. We do not use the features that contain information not
present at the time of asking a question, such as answers. To evaluate our
features, we deploy several predictive models trained on the features of 18
million questions to predict whether a question will get an accepted answer or
not. The results of this study show a significant relationship between our
proposed features and getting accepted answers. Finally, we introduce an online
tool that predicts whether a question will get an accepted answer or not.
Currently, Stack Overflow's users do not receive any feedback on their
questions before asking them, so they could carelessly ask unclear, unreadable,
or inappropriately tagged questions. By using this tool, they can modify their
questions and tags to check the different results of the tool and deliberately
improve their questions to get accepted answers.
