Year: 2021
Type: preprint
Abstract: 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
Source: arXiv (Cornell University)
Authors: Yang, Guang, Zhou, Yanlin, Yu, Chi, Chen, Xiang
Institution: Nantong University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Government of Jiangsu Province
OpenAlex ID: W3203979932