# DeepSCC: Source Code Classification Based on Fine-Tuned RoBERTa

arXiv: https://arxiv.org/abs/2110.00914 · HTML: https://mlsanity.com/paper/2110.00914/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2021.

## Built on by (most cited first, 4 of 4)

- [Learning to Parallelize in a Shared-Memory Environment with Transformers](https://mlsanity.com/paper/2204.12835/) (2022)
- [Advising OpenMP Parallelization via a Graph-Based Approach with Transformers](https://mlsanity.com/paper/2305.11999/) (2023)
- [MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers](https://mlsanity.com/paper/2305.09438/) (2023)
- [Enhancing Source Code Classification Effectiveness via Prompt Learning Incorporating Knowledge Features](https://mlsanity.com/paper/2401.05544/) (2024)

## Abstract (arXiv)

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