Abstract
With the increasing software developer community, questions answering (QA) sites, such as StackOverflow, have been gaining its popularity. Hence, in recent years, millions of questions and answers are posted in StackOverflow. As a result, it takes an enormous amount of effort to find out the suitable answer to a question. Luckily, StackOverflow allows their community members to label an answer as an accepted answer. However, in the most of the questions, answers are not marked as accepted answers. Therefore, there is a need to build a recommender system which can accurately suggest the most suitable answers to the questions. Contrary to the existing systems, in this work, we have utilized the textual features of the answers’ comments with the other metadata of the answers to building the recommender system for predicting the accepted answer. In the experimentation, our system has achieved 89.7% accuracy to predict the accepted answer by utilizing the textual metadata as a feature. We have also deployed our recommendation system web application, which is publicly accessible: http://210.4.73.237:8888/. We also deployed our system as Facebook Messenger Bot Application, which is accessible at https://www.facebook.com/RAiTABOT/, for helping the developers to easily find the suitable answer for a StackOverflow question.
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Mofijul Islam, M. et al. (2019). RAiTA: Recommending Accepted Answer Using Textual Metadata. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_11
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DOI: https://doi.org/10.1007/978-981-13-1498-8_11
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