arXiv:2605.15846v2 Announce Type: replace-cross Abstract: Coding agents are increasingly deployed in real software development, where a single version iteration requires months of coordinated work across many files. However, most existing benchmarks focus predominantly on single-issue bug fixes from Python repositories, with coarse pass/fail evaluation outcomes, and thus fail to capture long-horizon, multi-target development at real engineering scale. To address this gap, we present RoadmapBench, a benchmark of 115 long-horizon coding tasks grounded in real open-source version upgrades across
The proliferation of coding agents in real software development demands more sophisticated evaluation methods to understand their capabilities beyond single-issue fixes.
This benchmark addresses a critical gap in assessing AI agents' ability to handle complex, long-horizon software development tasks, moving beyond simplistic pass/fail metrics.
The development and adoption of 'RoadmapBench' will enable more accurate and realistic evaluation of AI agents' performance in real-world software engineering scenarios, fostering their advancement.
- · AI agent developers
- · Open-source software projects
- · Software development industry
- · Companies adopting AI for software engineering
- · Companies relying on outdated AI agent evaluation
- · Traditional software development workflows (eventually)
Improved benchmarks will accelerate the development of more capable and reliable AI coding agents.
More powerful AI agents will automate larger portions of the software development lifecycle, increasing efficiency and reducing human intervention.
The role of human software engineers may shift significantly towards orchestrating and overseeing AI agents rather than direct coding.
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Read at arXiv cs.AI