ScarfBench provides a new open benchmark to evaluate AI agents on Enterprise Java framework migrations. It focuses on ensuring successful builds, deployments, and behavior preservation across major Java ecosystems like Spring and Jakarta EE, addressing gaps in existing AI-assisted modernization efforts.
ScarfBench (Self-Contained Application Refactoring Benchmark) has been introduced to fill the gap in assessing AI agents' abilities for real-world enterprise Java applications. Unlike previous benchmarks that only focus on code generation, ScarfBench evaluates the entire process of framework migration, which includes various complexities beyond simple code translation.
ScarfBench sets clear criteria for success in Java framework migrations. Applications must not only compile successfully but also deploy without issues and pass behavioral validation. This multi-faceted approach provides a more realistic measure of the AI agent's ability to maintain original application functionality post-migration.
Migrating between frameworks such as Spring and Jakarta EE involves comprehensive changes, including adaptations to dependency injection, persistence configurations, and various framework-specific annotations. Each of these elements presents risks for potential errors, which can hinder successful deployment, thus emphasizing the need for thorough validation during the migration process.
Initial evaluations of several top AI coding agents on ScarfBench highlight significant challenges. Despite notable performance in traditional coding benchmarks, these agents show less than 10% success in maintaining application behavior during real-world migrations. This underscores the complexity of framework migration as compared to simpler coding tasks.
The implementation of ScarfBench marks an important development for the tech industry, particularly in AI-assisted software modernization. By providing a structured way to assess AI capabilities in complex migration scenarios, it could lead to advancements in how organizations approach modernization of legacy systems.
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ScarfBench provides a new open benchmark to evaluate AI agents on Enterprise Java framework migrations. It focuses on ensuring successful builds, deployments, and behavior preservation across major Java ecosystems like Spring and Jakarta EE, addressing gaps in existing AI-assisted modernization efforts.