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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 31·5 min read
RealSWE: A Compositional Evaluation of Coding Agents under Realistic User Requests
TL;DR
RealSWE introduces a new evaluation framework for coding agents that reflects real user requests, revealing significant performance gaps compared to traditional benchmarks. Key findings indicate that including explicit desired behavior and motivation in prompts can enhance model performance substantially.
✦ Why It Matters
Focus on explicitly stating desired behavior and motivation in prompts to improve coding agent performance.
Key Takeaways
- Use explicit desired behavior in prompts for better outcomes.
- Incorporate motivation to enhance coding agent performance.
- Recognize that real user requests differ significantly from benchmarks.
- Adapt prompt styles to match the casual nature of real requests.
How It Works
RealSWE evaluates coding agents by creating task variants that share the same underlying problem but differ in information composition and linguistic style. This approach allows for a more realistic assessment of how agents perform under typical user conditions.