Stack Overflow is the most popular Q&A website among software developers. As a platform for knowledge sharing and acquisition, the questions posted in Stack Overflow usually contain a code snippet. Stack Overflow relies on users to properly tag the programming language of a question and it simply assumes that the programming language of the snippets inside a question is the same as the tag of the question itself. In this paper, we propose a classifier to predict the programming language of questions posted in Stack Overflow using Natural Language Processing (NLP) and Machine Learning (ML). The classifier achieves an accuracy of 91.1 in predicting the 24 most popular programming languages by combining features from the title, body and the code snippets of the question. We also propose a classifier that only uses the title and body of the question and has an accuracy of 81.1 . Finally, we propose a classifier of code snippets only that achieves an accuracy of 77.7 . These results show that deploying Machine Learning techniques on the combination of text and the code snippets of a question provides the best performance. These results demonstrate also that it is possible to identify the programming language of a snippet of few lines of source code. We visualize the feature space of two programming languages Java and SQL in order to identify some special properties of information inside the questions in Stack Overflow corresponding to these languages.
@cite_5: Community-based question answering services accumulate large volumes of knowledge through the voluntary services of people across the globe. Stack Overflow is an example of such a service that targets developers and software engineers. In general, questions in Stack Overflow are answered in a very short time. However, we found that the number of unanswered questions has increased significantly in the past two years. Understanding why questions remain unanswered can help information seekers improve the quality of their questions, increase their chances of getting answers, and better decide when to use Stack Overflow services. In this paper, we mine data on unanswered questions from Stack Overflow. We then conduct a qualitative study to categorize unanswered questions, which reveals characteristics that would be difficult to find otherwise. Finally, we conduct an experiment to determine whether we can predict how long a question will remain unanswered in Stack Overflow.
@cite_16: Annotating documents with keywords or tags' is useful for categorizing documents and helping users find a document efficiently and quickly. Question and answer (Q&A) sites also use tags to categorize questions to help ensure that their users are aware of questions related to their areas of expertise or interest. However, someone asking a question may not necessarily know the best way to categorize or tag the question, and automatically tagging or categorizing a question is a challenging task. Since a Q&A site may host millions of questions with tags and other data, this information can be used as a training and test dataset for approaches that automatically suggest tags for new questions. In this paper, we mine data from millions of questions from the Q&A site Stack Overflow, and using a discriminative model approach, we automatically suggest question tags to help a questioner choose appropriate tags for eliciting a response.
@cite_4: 
Rekha @cite_5 proposed a hybrid auto-tagging system that suggests tags to users who create questions. When the post contains a code snippet, the system detects the programming language based on the code snippets and suggests many tags to users. Multinomial Naive Bayes (MNB) was trained and tested for the proposed classifier which achieved 72 @cite_16 converted Stack Overflow questions into vectors, and then trained a Support Vector Machine using these vectors and suggested tags used the model obtained. The tag prediction accuracy with this model is 68.47 it works well for some specific tags, it is not effective with some popular tags such as Java. Stanley and Byrne @cite_4 used a cognitive-inspired Bayesian probabilistic model to choose the most suitable tag for a post. This is the tag with the highest probability of being correct given the a priori tag probabilities. However, this model normalizes the top for all questions, so it is unable to differentiate between a post where the top predicted tag is certain, and a post where the top predicted tag is questionable. As a consequence, the accuracy is only 65