2026· European Conference on Object-Oriented Programming· pp. 22:1-22:31· 0 citations· 93 references
Computer Science
TL;DR
Pick is a significant improvement over showing users the candidate expressions, and also helps catch situations where no output is a match, and also helps catch situations where no output is a match.
This paper identifies patch verbosity as a major yet overlooked concern in LLM-based APR and proposes RECAP, a lightweight, plug-and-play adapter that attaches to existing repair frameworks after generation that achieves a substantially better size-correctness tradeoff.
Wen-Qiang Luo, J. Keung, Xiaoyu Shi et al.· 0 citations
It is observed that generated code often omits basic input validation or memory-safety checks, which can lead to overflows, resource exhaustion, or other reliability/security issues, and even the largest models frequently make simple mistakes.
Rodrigo Pato Nogueira, Marco Vieira, João R. Campos· 0 citations
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet their internal mechanisms remain largely opaque, making it difficult to understand, predict, or control their behavior. As LLMs are increasingly deployed in high-stakes settings, this lack of transparency raises ser...
Yinhan He, Wendy Zheng, Tianyi Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
This work investigates whether LLMs can accurately judge functional equivalence across different programming languages in human-written code, a setting that requires deeper reasoning beyond superficial similarity, and identifies a difficulty-dependent breakdown in equivalence judgment.
Hui Sun, Anderson G. Uchôa, Rohit Gheyi et al.· 0 citations
This work creates an “over language” in which to express all the information the authors need to be able to output correct and idiomatic syntax for each of their targets, and details the design of the extensible system.
Reed Mullanix, J. Carette· International Conference on...· 0 citations
This work studies an internal signal of code correctness that is able to judge candidate solutions better than the model's token-level or stated confidence, leaving open an important question: whether it reflects a robust property of the model or an artifact of that choice.
Francisco Ribeiro, Sohaila Abdulsattar, R. Gonzalez et al.· 1 citation
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