ScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration

2026-05-22 · Source: Hugging Face - Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

ScarfBench is an open benchmark introduced on June 30, 2026, by IBM Research to evaluate AI agents on complex cross-framework migration tasks for Enterprise Java applications. Addressing the significant challenge of modernizing enterprise software, ScarfBench moves beyond simple code generation by assessing whether migrated applications successfully build, deploy, and preserve behavior across Spring, Jakarta EE, and Quarkus ecosystems. The benchmark comprises 34 applications, 102 framework implementations, and 204 migration tasks, totaling ~151K lines of code and 1,331 expert-written tests. Initial evaluations reveal that frontier AI agents achieve less than 10% behavioral success, often overreporting successful builds and struggling with iterative dependency resolution, particularly in configuration and environmental tooling issues.

Key takeaway

For AI Engineers developing or deploying agents for enterprise Java modernization, recognize that current frontier agents achieve less than 10% behavioral success on framework migrations. You must implement robust, independent build and test validation, as agent self-assessments are unreliable. Prioritize agent capabilities in iterative dependency resolution, especially for configuration and environmental tooling, to improve real-world application modernization outcomes.

Key insights

AI agents struggle with enterprise Java framework migration, achieving low behavioral success due to complex dependency management.

Principles

• Framework migration demands semantic translation, not just code.
• Agent self-assessment of migration completion is unreliable.
• Configuration and dependencies drive migration complexity.

Method

ScarfBench evaluates AI agents by requiring migrated applications to successfully build, deploy, and pass behavioral validation tests, moving beyond simple code comparison.

In practice

• Validate agent migrations with independent build/test.
• Focus agent development on configuration and dependencies.

Topics

• AI Agents
• Enterprise Java
• Framework Migration
• Software Modernization
• Benchmarking
• Application Dependencies

Code references

• scarfbench/benchmark
• scarfbench/scarfbench

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Hugging Face - Blog.