An Empirical Study of Code Clones from Commercial AI Code Generators
Deep learning (DL) has revolutionized various software engineering tasks, including code generation and program repair. The emergence of AI code generators has pushed the boundaries of automatic programming to synthesize entire programs based on user-defined specifications in natural language. However, it remains a mystery if these AI code generators rely on copy-and-paste programming practices, with possible implications for copyright infringement and code cloning. In this work, we conduct an empirical study on three state-of-the-art commercial AI code generators to investigate the existence of code clone issues. Our experimental results show that the total Type-1 and Type-2 clone rates of the state-of-the-art commercial AI code generators can reach up to 7.50%, indicating marked code clone issues. Furthermore, it is observed that AI code generators risk infringing copyrights and propagating vulnerable code resulting from cloning code and show a certain degree of stability in generating code clones.
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10:30 - 12:30 | |||
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Aurora A is the first room in the Aurora wing.
When facing the main Cosmos Hall, access to the Aurora wing is on the right, close to the side entrance of the hotel.