Two perspectives on software documentation quality in stack overflow

Abstract

This paper studies the software documentation quality in Stack Overflow from two perspectives: the questioners’ who are accepting answers and the community’s who is voting for answers. We show what developers can do to increase the chance that their questions or answers get accepted by the community or by the questioners. We found different expectations of what information such as code or images should be included in a question or an answer. We evaluated six different quality indicators (such as Flesch Reading Ease or images) which a developer should consider before posting a question and an answer. In addition, we found different quality indicators for different types of questions, in particular error, discrepancy, and how-to questions. Finally we use a supervised machine-learning algorithm to predict when an answer will be accepted or voted.