SWE-Bench++: A Framework for the Scalable Generation of Software Engineering Benchmarks from Open-Source Repositories
Lilin Wang, Lucas Ramalho, Alan Celestino, Phuc Anthony Pham, Yu Liu, Umang Kumar Sinha, Andres Portillo, Onassis Osunwa, Gabriel Maduekwe

TL;DR
SWE-Bench++ is an automated, scalable framework that generates multilingual, repository-level software engineering benchmarks from open-source GitHub projects, enabling better evaluation and training of large language models.
Contribution
It introduces an automated pipeline for creating dynamic, repository-level coding tasks from live pull requests across multiple languages, surpassing prior manual and static datasets.
Findings
Initial benchmark has 11,133 instances from 3,971 repositories.
State-of-the-art models achieve pass@10 scores around 36%.
Fine-tuning on SWE-Bench++ improves performance on existing benchmarks.
Abstract
Benchmarks like SWE-bench have standardized the evaluation of Large Language Models (LLMs) on repository-level software engineering tasks. However, these efforts remain limited by manual curation, static datasets, and a focus on Python-based bug fixes. We introduce SWE-Bench++, an automated framework that generates repository-level coding tasks from open-source GitHub projects. Unlike synthetic approaches, our pipeline harvests live pull requests to cover both bug fixes and feature requests across 11 languages. SWE-Bench++ turns GitHub pull requests (PRs) into reproducible, execution-based tasks via four stages: programmatic sourcing, environment synthesis, test oracle extraction, and quality assurance. A final hint-guided trajectory synthesis step converts instances that strong models fail on into training trajectories. Our initial benchmark consists of 11,133 instances from 3,971…
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Taxonomy
TopicsSoftware Engineering Research · Software Testing and Debugging Techniques · Scientific Computing and Data Management
