Proposing a model to predict relatedness between knowledge units in programming question-answering websites using deep learning techniques: a case study of Stack

Document Type : Original Article

Authors

Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.

Abstract

The Stack Overflow website is one of the most popular communities used by millions of programmers. If we consider a question and its corresponding answers as a knowledge unit on the Stack Overflow website, then there are different semantic relationships between two knowledge units, which include duplicate, direct, and indirect relationships with the proposed question. Recognizing different categories of semantic relationship between knowledge units in Stack Overflow can significantly improve the effectiveness and efficiency of information search. In this study, a hybrid approach based on deep learning methods and traditional similarity criteria is presented to detect the relationship between questions. In particular, two deep network architectures are presented, the first architecture consists of a long short-term memory network as well as a cosine similarity calculation layer. The second architecture is an extension of the first architecture by adding an attention mechanism. The proposed approach was evaluated on a dataset of Java programming language contining 40000 questions. The obtained results show that in terms of F1, Recall and Precision, the proposed model performs better than the existing models. Specifically, the model proposed in this article has a 17.3% improvement in terms of F1 measure compared to the best current model. Also, the results of the experiments show that using the pre-trained word embedding model significantly improves the performance of the presented models.

Keywords

References

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