SWE-Cycle: Benchmarking Code Agents across the Complete Issue Resolution Cycle

• URL: http://arxiv.org/abs/2605.13139v1
• Date: Wed, 13 May 2026 08:05:16 GMT
• Title: SWE-Cycle: Benchmarking Code Agents across the Complete Issue Resolution Cycle
• Abstract summary: SWE-Cycle is a benchmark of 489 rigorously filtered instances of autonomous code agents.<n>SWE-Judge combines static code review with dynamic testing to accurately verify functional correctness.<n>The results reveal a sharp drop in solve rates when transitioning from isolated tasks to FullCycle execution.
• Score: 46.833916047516716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As autonomous code agents move toward end-to-end software development, evaluating their practical autonomy becomes critical. Current benchmarks hide friction by testing agents in pre-configured environments, and their static evaluation pipelines frequently fail when parsing fully autonomous trajectories. We address these limitations with SWE-Cycle, a benchmark of 489 rigorously filtered instances. SWE-Cycle evaluates agents across three isolated tasks, including environment reconstruction, code implementation, and verification test generation, as well as an end-to-end FullCycle task that integrates all three. The FullCycle task requires agents to work autonomously in a bare repository without human scaffolding. To reliably assess these complex execution paths, we developed SWE-Judge. By combining static code review with dynamic testing, this execution-capable evaluation agent accurately verifies functional correctness and eliminates the systematic measurement errors of traditional static parsers. We evaluate code agents powered by six state-of-the-art LLMs across these four tasks. The results reveal a sharp drop in solve rates when transitioning from isolated tasks to FullCycle execution, exposing critical bottlenecks in handling cross-phase dependencies and maintaining code quality. Together, SWE-Cycle and SWE-Judge provide a comprehensive framework for accurately measuring the end-to-end capabilities of autonomous software agents.

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