SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios
📰 ArXiv cs.AI
SWE-EVO benchmark evaluates AI coding agents in long-horizon software evolution scenarios
Action Steps
- Identify long-horizon software evolution tasks that require coordination across multiple files and iterations
- Develop AI coding agents that can interpret high-level requirements and preserve functionality
- Evaluate AI coding agents using SWE-EVO benchmark
- Analyze results to improve AI coding agents and software development workflows
Who Needs to Know This
Software engineers and AI researchers on a team benefit from SWE-EVO as it helps assess the capability of AI coding agents in real-world software development scenarios, enabling them to improve their tools and workflows
Key Insight
💡 Existing benchmarks for AI coding agents are limited to isolated tasks, while SWE-EVO addresses the need for evaluating agents in real-world, long-horizon software evolution scenarios
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🚀 SWE-EVO: Benchmarking AI coding agents in long-horizon software evolution scenarios
Key Takeaways
SWE-EVO benchmark evaluates AI coding agents in long-horizon software evolution scenarios
Full Article
Title: SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios
Abstract:
arXiv:2512.18470v5 Announce Type: replace-cross Abstract: Existing benchmarks for AI coding agents focus on isolated, single-issue tasks such as fixing a bug or adding a small feature. However, real-world software engineering is a long-horizon endeavor: developers interpret high-level requirements, coordinate changes across many files, and evolve codebases over multiple iterations while preserving functionality. We introduce SWE-EVO, a benchmark for this long-horizon software evolution challenge
Abstract:
arXiv:2512.18470v5 Announce Type: replace-cross Abstract: Existing benchmarks for AI coding agents focus on isolated, single-issue tasks such as fixing a bug or adding a small feature. However, real-world software engineering is a long-horizon endeavor: developers interpret high-level requirements, coordinate changes across many files, and evolve codebases over multiple iterations while preserving functionality. We introduce SWE-EVO, a benchmark for this long-horizon software evolution challenge
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