A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents

Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs. Yet existing repository-level benchmarks typically evaluate only whether the final patch passes tests. Satisfying a user request requires a long chain of interdependent reasoning and decisions: an agent must recover explicit and implicit requirements, formulate a repository-grounded implementation plan, and translate it into correct code. A pass/f

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ethics.ai (10 August 2026), “A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents,” evidence record 18031, https://ethics.ai/record/18031 (originally published by arXiv).

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