SWE-Next: Scalable Real-World Software Engineering Tasks for Agents

• URL: http://arxiv.org/abs/2603.20691v1
• Date: Sat, 21 Mar 2026 07:26:22 GMT
• Title: SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
• Abstract summary: We present SWE-Next, an execution-grounded framework for scalable SWE task and trajectory collection.<n>SWE-Next mines real merged pull requests, executes candidate base/merged commit pairs, and retains only those that produce strict test improvements without regressions.<n>It also applies strict submission gating so that collected trajectories remain evidence-driven rather than speculative.
• Score: 46.235209026216246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Executable software engineering data is valuable for training SWE agents, but scaling it remains difficult for two reasons: only a small fraction of real repository changes yield verifiable, high-signal task instances, and naively building repository-specific environments quickly becomes the dominant systems cost. We present SWE-Next, an execution-grounded framework for scalable SWE task and trajectory collection. On the data side, SWE-Next mines real merged pull requests, executes candidate base/merged commit pairs, and retains only those that produce strict test improvements without regressions, yielding self-verifying instances. It also applies strict submission gating so that collected trajectories remain evidence-driven rather than speculative. On the systems side, SWE-Next introduces reusable repo-quarter profiles, which reuse the same environment across nearby commits in time while keeping each task run separate and reproducible. Using only 30 hours and 639GB of environment storage, SWE-Next processes 3,971 seed repositories and 102,582 candidate commit pairs mined from real merged PRs to construct a dataset of 2,308 self-verifying instances. Experiments show that SWE-Next improves downstream pass@1 with fewer or comparable training trajectories, indicating that its gains come not from a stronger trajectory generator, but from higher-signal execution-grounded supervision and more efficient data collection.

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