ScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration

The introduction of ScarfBench marks a significant milestone in the evaluation of artificial intelligence agents tasked with enterprise software migration. This benchmark addresses a critical gap in AI assessment tools by focusing specifically on the complex, real-world challenge of migrating legacy Java frameworks to modern architectures. As organizations worldwide grapple with aging codebases and technical debt, the ability to accurately measure AI agent performance in framework migration has become essential for determining whether autonomous AI tools can reliably handle high-stakes enterprise transformations.

• Standardized Measurement Framework: ScarfBench establishes measurable criteria for evaluating AI agents' ability to refactor and migrate code, moving beyond generic coding benchmarks to address domain-specific enterprise needs.

• Enterprise Adoption Acceleration: By providing a validated testing ground, the benchmark reduces risk perception around AI-assisted migration projects, potentially accelerating enterprise adoption of AI tools for modernization initiatives.

• Performance Transparency: The benchmark enables transparent comparison between different AI agents and platforms, helping enterprises make informed decisions when selecting tools for Java framework migration tasks.

• Technical Debt Resolution: Accurate benchmarking supports organizations in understanding whether AI agents can meaningfully assist in addressing accumulated technical debt from outdated Java frameworks like Spring 3.x or legacy application servers.

• Cost-Benefit Analysis: Clear performance metrics allow enterprises to calculate return on investment for AI-assisted migration projects versus traditional manual refactoring approaches.

Java remains foundational to enterprise software infrastructure globally, yet many organizations run outdated frameworks that create security vulnerabilities and maintenance challenges. The emergence of capable AI agents has generated significant interest in automating migration work, but without proper benchmarking, enterprises face uncertainty about whether these tools actually work at scale. ScarfBench transforms this landscape by establishing concrete evaluation criteria, enabling both AI developers to improve their systems and enterprises to make confident adoption decisions. This benchmark represents a maturation moment in applied AI, where assessment tools catch up with capability claims.