2022
DOI: 10.48550/arxiv.2204.07940
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WhyGen: Explaining ML-powered Code Generation by Referring to Training Examples

Abstract: Deep learning has demonstrated great abilities in various code generation tasks. However, despite the great convenience for some developers, many are concerned that the code generators may recite or closely mimic copyrighted training data without user awareness, leading to legal and ethical concerns. To ease this problem, we introduce a tool, named WhyGen, to explain the generated code by referring to training examples. Specifically, we first introduce a data structure, named inference fingerprint, to represen… Show more

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