SOTagger - Towards classifying stack overflow posts through contextual tagging

Abstract

There is an ever increasing growth in the use of Q&A websites such as Stack Overflow (SO), so are the number of posts on them. These websites serve as knowledge sharing platforms where Subject Matter Experts (SMEs) and developers answer questions posted by other users. It is effort intensive for developers to navigate to right posts because of the large volume of posts on the platform, despite the presence of existing tags, that are based on technologies. Tagging these posts based on their context and purpose might help developers and SMEs in easily identifying questions they wish to answer and also in identifying contextually similar posts. To support this idea, we propose SOTagger as a prototype plug-in for Stack Overflow to tag questions contextually. We have considered SO data provided on SOTorrent and automated the identification of 6 categories of questions using Latent Dirichlet Allocation. We have also manually verified relevance of these categories. Using these categories and dataset, we have built a classification model to classify a post into one of these six categories using Support Vector Machine. We have evaluated SOTagger by conducting a user survey with 32 developers. The preliminary results are promising with about 80% developers recommending the plugin to others.