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New benchmark reveals frontier coding agents miss most bugs

By PulseAugur Editorial · [1 sources] ·

A new benchmark called PROBE has been introduced to evaluate the bug-finding capabilities of frontier coding agents. This benchmark comprises 2,457 bugs fixed over 18 months across 20 GitHub repositories. Evaluations show that current frontier models achieve less than 21% recall and under 9% precision in identifying these bugs. Despite the low precision, human evaluators still deem 85% of the agents' findings as valid. Notably, the types of bugs found by these agents differ significantly from those repaired by human maintainers. AI

IMPACT Highlights limitations in current AI coding agents' ability to find and fix software bugs, suggesting areas for improvement in model development.

RANK_REASON The cluster contains an academic paper introducing a new benchmark for evaluating AI capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark reveals frontier coding agents miss most bugs

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The cluster contains an academic paper introducing a new benchmark for evaluating AI capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 Deutsch(DE) · Marco Paina, Mark Niklas M\"uller, Niels M\"undler-Sasahara, Martin Vechev ·

    PROBE: Frontier Coding Agents Find Different Bugs Than Maintainers Fix

    arXiv:2610.35987v1 Announce Type: cross Abstract: Proactive bug discovery is an important software-engineering problem. Even though coding agents are already widely deployed in this domain, their capabilities remain understudied. In particular, the challenging task of finding all…

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