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cs.SE2025
Effective Code Membership Inference for Code Completion Models via Adversarial Prompts
Yuan Jiang, Zehao Li, Shan Huang +3
Membership inference attacks (MIAs) on code completion models offer an effective way to assess privacy risks by inferring whether a given code snippet was part of the training data…
cs.SE2025
Enhancing the Non-Functional Quality Compliance of LLM-Generated Code through Quality-Aware Preference Learning
Liang Lu, Yuan Jiang, Christoph Treude +4
Large Language Models (LLMs) have been widely adopted in commercial code completion engines, significantly enhancing coding efficiency and productivity. However, even functionally…