arXiv:2605.20251v2 Announce Type: cross Abstract: Existing benchmarks for LLM coding agents primarily evaluate final outcomes. While useful for measuring overall capability, these metrics provide limited visibility and often miss defects that arise during execution. We present ProcBench, a benchmark for execution-process evaluation in LLM coding agents. ProcBench organizes recurrent execution defects into a reusable ontology covering 11 defect types in 4 categories, and evaluates agent trajectories through standardized process evidence rather than final outcomes alone. To support comparison ac
The rapid advancement and deployment of LLM coding agents necessitate more robust evaluation methodologies to ensure their reliability and practical utility, moving beyond superficial success metrics.
This development allows for a more granular understanding of LLM coding agents' actual performance, identifying and categorizing execution defects critical for their integration into complex software development workflows.
The focus shifts from merely evaluating the final output of LLM agents to meticulously analyzing their execution processes, enabling targeted improvements and more trustworthy autonomous systems.
- · AI Agent Developers
- · Software Development Teams
- · Cybersecurity Professionals
- · AI Testing & Assurance Platforms
- · Developers reliant on ad-hoc LLM agent evaluation
- · AI models with high defect rates
- · Companies with poor software quality control
Improved reliability and safety of actively deployed LLM coding agents.
Faster adoption and integration of robust AI agents into critical enterprise systems and development pipelines.
Reduced 'hallucination' and critical error rates, leading to broad trust in AI-driven automation.
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Read at arXiv cs.AI