Trace2Eval: Contextualized Evaluation of Coding Agents from Real-World Traces
Story 3, from arXiv
Trace2Eval is a new framework that evaluates coding agents by reconstructing real-world user interactions, allowing for more realistic and dynamic benchmarking of AI models.
Key points
- Trace2Eval constructs benchmarks from real-world interaction trajectories.
- It uses a multi-agent system to recover user queries and rebuild execution environments.
- The framework iteratively refines human rubrics and calibrates judge agents against human assessments.
- Models using Trace2Eval improved their scores by up to 19.28 percentage points.
- The system supports rubric feedback as actionable guidance for artifact improvement.
Why it matters
Software engineers using AI tools for code generation and review need more realistic benchmarks to assess model performance in real-world scenarios. Trace2Eval offers a new way to evaluate coding agents based on actual user workflows, which can lead to better tooling and more effective model training.
Counterpoint
The approach requires access to real-world interaction data, which may be limited or sensitive, and the reconstruction process could introduce inaccuracies that affect evaluation reliability.
Sources
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