arXiv:2607. 01903v1 Announce Type: new Abstract: LLM-integrated applications blend natural language prompts with program code, and much of their runtime behavior originates in the prompt layer rather than in the code itself.
By Zihao Xu, Yuekang Li, Gelei Deng, Yi Liu, Zhenchang Xing
arXiv:2508. 16131v3 Announce Type: replace-cross Abstract: Code completion entails the task of providing missing tokens given a surrounding context.
By Zoe Kotti, Konstantina Dritsa, Diomidis Spinellis, Panos Louridas
arXiv:2609.37405v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly used for software engineering tasks that require understanding existing source code, including behavior...
By Ali Mohammadi Esfahani, Nafiseh Kahani, Samuel A. Ajila
arXiv:2606. 00308v1 Announce Type: cross Abstract: Large-language-model code generation has shifted from single-shot prompting to multi-agent orchestrations - analyst, coder, tester, and debugger pipelines - and is evaluated almost exclusively on functional correctness.
By Nazmus Ashrafi
arXiv:2606. 08840v1 Announce Type: new Abstract: Code generation models are typically compared using compact execution benchmarks and aggregate pass rates, but such summaries obscure how performance varies across programming languages, problem families, and failure modes.
By Sayed Erfan Arefin
Complexity measured from generated code is failure-dependent: a difficult prompt can yield a short failing program and be assigned low output complexity. We introduce a six-dimension prompt-side struc...