  In software engineering-related tasks (such as programming language tag
prediction based on code snippets from Stack Overflow), the programming
language classification for code snippets is a common task. In this study, we
propose a novel method DeepSCC, which uses a fine-tuned RoBERTa model to
classify the programming language type of the source code. In our empirical
study, we choose a corpus collected from Stack Overflow, which contains 224,445
pairs of code snippets and corresponding language types. After comparing nine
state-of-the-art baselines from the fields of source code classification and
neural text classification in terms of four performance measures (i.e.,
Accuracy, Precision, Recall, and F1), we show the competitiveness of our
proposed method DeepSCC
