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

TL;DR

SWE-Next is a scalable framework for collecting high-quality, execution-grounded software engineering data from real repositories, enabling more effective training of SWE agents with less data and system cost.

Contribution

It introduces a novel method for mining and verifying real-world repository changes, along with reusable environment profiles to efficiently generate high-signal training data.

Findings

Improves downstream pass@1 with fewer training trajectories

Processes large datasets with minimal environment storage

Enhances data quality through self-verifying instances

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.