Towards Mining Answer Edits to Extract Evolution Patterns in Stack Overflow

Abstract

The current state of practice dictates that in order to solve a problem encountered when building software, developers ask for help in online platforms, such as Stack Overflow. In this context of collaboration, answers to question posts often undergo several edits to provide the best solution to the problem stated. In this work, we explore the potential of mining Stack Overflow answer edits to extract common patterns when answering a post. In particular, we design a similarity scheme that takes into account the text and code of answer edits and cluster edits according to their semantics. Upon applying our methodology, we provide frequent edit patterns and indicate how they could be used to answer future research questions. Assessing our approach indicates that it can be effective for identifying commonly applied edits, thus illustrating the transformation path from the initial answer to the optimal solution.