Characterization and Prediction of Questions without Accepted Answers on Stack Overflow

TL;DR

This paper analyzes factors influencing whether questions on Stack Overflow receive accepted answers, proposing predictive models and an online tool to help users improve their questions for better acceptance rates.

Contribution

It introduces novel features and predictive models trained on 18 million questions to forecast question acceptance, along with an online tool for question improvement.

Findings

Significant correlation between proposed features and accepted answers

Predictive models achieve high accuracy in forecasting question acceptance

Online tool helps users improve questions to increase acceptance likelihood

Abstract

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.