SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks

📰 ArXiv cs.AI

SlopCodeBench benchmarks coding agents' degradation over long-horizon iterative tasks

advanced Published 27 Mar 2026
Action Steps
  1. Identify the limitations of existing agentic coding benchmarks
  2. Design a language-agnostic benchmark that allows for flexible design decisions
  3. Evaluate coding agents' performance over long-horizon iterative tasks
  4. Analyze the degradation of code quality and its impact on future extensions
Who Needs to Know This

Software engineers and AI researchers benefit from SlopCodeBench as it helps evaluate coding agents' performance in iterative tasks, informing the development of more efficient and effective coding tools

Key Insight

💡 Coding agents' performance degrades over long-horizon iterative tasks, highlighting the need for benchmarks that evaluate code quality beyond single-shot solutions

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🤖 Benchmarking coding agents' degradation over time 📊

Key Takeaways

SlopCodeBench benchmarks coding agents' degradation over long-horizon iterative tasks

Full Article

Title: SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks

Abstract:
arXiv:2603.24755v1 Announce Type: cross Abstract: Software development is iterative, yet agentic coding benchmarks overwhelmingly evaluate single-shot solutions against complete specifications. Code can pass the test suite but become progressively harder to extend. Recent iterative benchmarks attempt to close this gap, but constrain the agent's design decisions too tightly to faithfully measure how code quality shapes future extensions. We introduce SlopCodeBench, a language-agnostic benchmark c
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