IBM Research Releases ScarfBench to Evaluate AI Agents on Enterprise Java Framework Migrations

IBM Research has launched ScarfBench, an open-source benchmark specifically designed to measure the capability of AI agents in executing framework migrations for enterprise Java applications. While traditional software engineering benchmarks primarily evaluate AI on isolated bug fixes or simple code generation, ScarfBench focuses on complex, cross-framework migrations that require maintaining behavioral consistency and resolving runtime dependencies. The benchmark tests AI agents on real-world engineering hurdles, such as adapting build systems like Maven or Gradle and navigating complex runtime dependencies. It is structured to visualize where LLM-powered agents spend the most resources and whether they can accurately determine when a migration task is fully complete. Preliminary evaluations show that while modern frontier LLMs often succeed in translating code snippets, they struggle with ensuring overall application consistency and fixing build configurations. For organizations looking to integrate AI agents into their migration pipelines, ScarfBench highlights the necessity of monitoring agent performance in dependency resolution and automated post-migration testing.