AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The goal is to understand the code generation errors of foundation LLMs and explore the solution to resolve directly fixable errors, and to design and evaluate the LlmFix fixing method and constructed the LlmErrorEval dataset.
Findings show that functional-only evaluation overestimates agents'ability to satisfy the full requirements of repository-level repair tasks, and introduces SWE-Gate, a repository-level benchmark for software engineering agents that explicitly evaluates review constraint compliance alongside functional correctness.
MCR-Bench is introduced, the first defect state-aware benchmark designed for realistic multi-round code review, and in-depth error analysis dissects the distinct drivers of false positives and false negatives, revealing critical weaknesses such as cross-round temporal misalignment and inadequate long-range memory.
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