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.