Cross-Lingual Code Cloning: Practical Solutions and Value
Introduction
Cross-lingual code cloning is a challenging task in modern software development, involving the identification of identical or nearly identical code segments in multiple programming languages within a single project.
AI and Machine Learning Advancements
Recent advancements in Artificial Intelligence and Machine Learning, particularly Large Language Models (LLMs), have shown promise in addressing code-related tasks like code clone identification.
Research and Findings
A recent study evaluated the performance of LLMs and pre-trained embedding models in detecting cross-lingual code clones. The findings highlighted the strengths and limitations of LLMs in handling different programming tasks and emphasized the effectiveness of embedding models in this context.
Key Contributions
The study provided insights into the capabilities of LLMs in identifying cross-lingual code clones, the influence of programming language similarities on LLM performance, and the comparison of LLMs with traditional ML techniques using learned code representations.
Conclusion and Recommendations
The results suggested that while LLMs are capable, embedding models may be more effective for detecting code clones across languages, especially in complex scenarios.
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