🤖 AI Summary
This work addresses the scarcity of large-scale, multilingual, and reproducible training data for reinforcement learning in software engineering (SWE) tasks. It proposes the first language-agnostic automated pipeline that harvests executable SWE tasks from real-world code repositories, automatically generates installation and testing procedures, and filters low-quality samples through a combination of large language model evaluation and human verification. For the first time, this approach enables standardized collection of SWE tasks with test validation across 20 programming languages. The released dataset comprises over 32,000 executable tasks spanning more than 3,600 repositories and includes over 120,000 samples with installation instructions and test cases, accompanied by fine-grained metadata and diagnostic analyses—significantly advancing reproducible training and evaluation of multilingual SWE agents.
📝 Abstract
Software engineering agents (SWE) are improving rapidly, with recent gains largely driven by reinforcement learning (RL). However, RL training is constrained by the scarcity of large-scale task collections with reproducible execution environments and reliable test suites. Although a growing number of benchmarks have emerged, datasets suitable for training remain limited in scale and diversity or often target a limited set of high-resource language ecosystems. We introduce SWE-rebench V2, a language-agnostic automated pipeline for harvesting executable real-world SWE tasks and constructing RL training environments at scale. The pipeline synthesizes repository-specific installation and test procedures via an interactive setup agent, and filters unsound instances using an ensemble of LLM judges, validated against human-verified SWE-bench annotations. Using this pipeline, we construct a dataset of 32,000+ tasks spanning 20 languages and 3,600+ repositories, with pre-built images for reproducible execution. To further scale training data, we additionally release 120,000+ tasks with installation instructions, fail-to-pass tests and rich metadata, where the problem statement is generated based on the original pull request description. We validate the collected instances through a diagnostic study that covers a subset of tasks in five programming languages across seven popular models, and provide instance-level metadata that flags common confounders such as overly restrictive tests and underspecified descriptions. We release the datasets, the collection and execution code, and associated artifacts to enable large-scale training of SWE agents across diverse languages and repositories.