An Empirical Study of Fine-Tuning Pre-Trained Code Models and Adapters for the Classification of Source Code Plagiarism Instances

Source code plagiarism is a significant challenge in software engineering and computer science education, affecting academic integrity, intellectual property rights, and software quality assurance. However, Source Code Plagiarism Classification (SCPC) remains difficult because labelled training data are limited, mainly due to the sensitivity of plagiarism cases. This restricts the effective use of machine learning (ML) and deep learning (DL) methods, especially in low-resource settings. This work investigates low-resource SCPC using Pre-trained Code Models (PCMs). We first examine Full Fine-Tuning (FFT), where all model parameters are updated, across multiple public datasets. We then evaluate Parameter-Efficient Fine-Tuning (PEFT), where only a small subset of parameters is trained. Specifically, we apply three adapter-based PEFT methods and compare them with FFT in terms of classification performance, training time, inference time, GPU usage, trainable parameter percentage, and model size. The results show that, when labelled training data are available, fine-tuned PCMs achieve strong SCPC performance and higher $F_1$ scores than the unsupervised open-source plagiarism-detection tools in our evaluation, JPlag and Dolos. Overall, PEFT achieves a performance that is similar, comparable to, or slightly lower than that of FFT, while requiring fewer trainable parameters and lower GPU usage, at the cost of slightly higher inference time.