TL;DR
The proposed Siamese Network-based Code Similarity Detection (SNCSD) approach computes contextual embeddings to capture both syntactic and semantic similarities between student-submitted code and reference solutions to offer a more accurate, transparent, and scalable alternative to traditional grading approaches.
The case of synthetic code detection (i.e., written by a human or generated by AI) becomes more important in the field of education and security. Current code detection tools that depend on probabilistic and classification-based approaches have limitations in detecting various programming styles. To address this challe...
This study investigates the application of UniXcoder, a pre-trained transformer model for source code, to classify Java source code methods across multiple projects as smell or clean, with a particular focus on the Switch Statements smell, and confirms that pre-trained transformer models, particularly UniXcoder, are ca...
Refactoring is a disciplined process of improving the internal structure of software without changing its external behavior. Empirical studies have shown that refactoring contributes to maintainability and code quality; however, existing machine learning approaches for refactoring prediction rely predominantly on struc...
Two novel contributions are introduced: CodeEval and CodeQual, an open-source execution framework that provides researchers with a ready-to-use evaluation pipeline for evaluating and improving LLMs in software engineering contexts, encompassing both functional correctness assessment and subjective code quality evaluati...
One of the primary challenges in training deep neural networks is the requirement for a robust and diverse data source. To address this limitation, data augmentation techniques have emerged as a promising solution, enabling the expansion of training datasets without requiring additional data collection. This study inve...
Binary Code Similarity Detection (BCSD) is a foundational capability in software security, underpinning critical applications ranging from vulnerability detection to malware analysis. While recent tools based on Machine Learning (ML) have achieved significant performance improvements, their efficacy is heavily continge...
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