AI & Agent Evaluation
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Reliable and Developer-Aligned Evaluation of Agents for Software Engineering

peer-reviewed paper · source date 2026-07-07 · added 2026-08-07 15:52:43 · updated 2026-08-07 15:52:43 · Open original blog

Problems / challenges / motivations

  • SE-agent benchmarks are contaminated, syntactic, and outcome-only, so their scores drift away from what developers actually care about.
  • The same benchmarks are used to argue capability, prioritize research, and support safety cases.
  • The SWE-Bench Pro retraction made the cost of this concrete rather than theoretical.

Key ideas

  • Proposes a multi-dimensional evaluation methodology for LLM coding agents grounded in real-world development.
  • Three pillars: contamination-awareness, in-the-wild agentic behavior, and trajectory-aware benchmarks and metrics.
  • The aim is a community-facing standard rather than another leaderboard.
  • Accepted to FSE '26.

Why it matters for evals

  • It is a peer-reviewed anchor joining two of the month's dominant themes at once: contamination and validity in coding evals, and the shift from outcome-only to trajectory evaluation.
  • It is the constructive counterpart to the SWE-Bench Pro retraction — the retraction says what is broken, this says what to build instead.
  • Caveat: a methodology and standard-setting paper rather than a turnkey benchmark. Adoption and cross-team reproducibility remain to be demonstrated.

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