ScarfBench: A Benchmark for Cross-Framework Application Migration in Enterprise Java

TL;DR

ScarfBench is a new benchmark designed to evaluate the ability of AI agents to perform behavior-preserving cross-framework refactoring of enterprise Java applications across multiple frameworks, revealing current limitations.

Contribution

It introduces a comprehensive benchmark with expert-crafted tasks and an evaluation framework for cross-framework Java application migration, which was previously unmeasured.

Findings

The best AI agent achieves only 15.3% test pass rate on focused migrations.

Full behavioral equivalence is achieved in only one of 204 tasks.

Migration difficulty varies by framework pair and architectural layer.

Abstract

Java remains central to enterprise software, and many applications outlive their original architecture. Migrating them across frameworks is a behavior-preserving refactoring spanning build configuration, dependency injection, persistence, request handling, and deployment. Existing software-engineering benchmarks cover bug fixing, feature implementation, and language or version modernization, but leave cross-framework refactoring largely unmeasured. We introduce ScarfBench, a benchmark for behavior-preserving cross-framework refactoring of enterprise Java applications. It is built from expert-written implementation triples across Spring, Jakarta EE, and Quarkus: 34 applications (29 focused single-layer, 5 whole) yielding 102 variants (~151K lines across 1946 source and test files) and 204 directed refactoring tasks. Each task gives an agent a working source application and a target framework; the agent must synthesize a target implementation preserving the source behavior. Correctness is evaluated by an application-specific executable oracle: the candidate must compile, deploy in a containerized target runtime, and pass behavioral tests over the application's observable interface. We evaluate five state-of-the-art coding agents on ScarfBench. The strongest achieves only 15.3% aggregate test pass on focused-layer migrations and 12.2% on whole applications, and only one of the 204 tasks yields a fully behaviorally equivalent target. Difficulty is asymmetric across framework directions and architectural layers: Spring<->Quarkus is the most tractable pair, and Jakarta-targeted migrations are hardest. From LLM-as-a-judge and expert adjudication of failed-task traces, we derive a taxonomy of recurring failure categories spanning build, deploy, and test stages. We release the benchmark, harness, and agent traces at https://scarfbench.info.