ScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration

ScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration

Enterprise software modernization remains one of the most expensive and risky endeavors in the technology industry. Migrating monolithic Java applications to modern frameworks—such as moving from Java EE to Spring Boot or from legacy Struts to Jakarta EE—often requires months of manual effort, deep domain expertise, and careful regression testing. As artificial intelligence agents become increasingly capable of understanding and generating code, the question arises: can we trust them to automate these complex migrations? Enter ScarfBench, a new benchmark designed specifically to evaluate AI agents on enterprise Java framework migration tasks.

The Challenge of Enterprise Java Migration

Java has been the backbone of enterprise software for over two decades. Countless organizations run critical business logic on frameworks that are now outdated, unsupported, or architecturally incompatible with cloud-native environments. Migrating these systems is not merely a matter of syntax translation. It involves understanding deep framework-specific idioms, dependency injection patterns, transaction management, security configurations, and often hundreds of interconnected classes.

Traditional migration approaches include manual rewriting, semi-automated tools, and pattern-based refactoring. Each method has drawbacks: manual work is slow and error-prone, semi-automated tools often miss edge cases, and pattern-based approaches fail when the codebase deviates from expected conventions. AI agents, particularly large language models (LLMs) fine-tuned for code, offer a promising alternative—but only if they can reliably handle the complexity and nuance of real enterprise code.

What Is ScarfBench?

ScarfBench is a structured evaluation framework that tests AI agents on their ability to perform Java framework migrations. The name stands for "Software Conversion and Refactoring Framework Benchmark." Unlike general-purpose coding benchmarks that focus on isolated algorithm problems or small function completion, ScarfBench is specifically designed for enterprise-scale migration tasks.

The benchmark includes a curated set of Java projects representing common migration scenarios. These projects are not trivial "hello world" examples; they are realistic, multi-file applications with dependencies, configuration files, and business logic. Each migration task requires the AI agent to understand the source framework, map its concepts to the target framework, and produce a working, compilable, and functionally equivalent codebase.

Key dimensions evaluated in ScarfBench include:

• **Functional correctness**: Does the migrated code produce the same outputs as the original?
• **Compilation success**: Can the code be built without errors?
• **Framework idiom compliance**: Does the agent use idiomatic patterns of the target framework rather than simply translating line by line?
• **Configuration completeness**: Are necessary configuration files (e.g., XML, YAML, properties) correctly generated?
• **Edge case handling**: Does the agent correctly manage exceptions, resource cleanup, and thread safety?

The Architecture of the Benchmark

ScarfBench is built around a modular architecture that allows researchers to plug in different AI agents and evaluate them under consistent conditions. The benchmark consists of three main components:

1. The Task Suite

The task suite contains dozens of migration scenarios, each with a source project, a target framework specification, and a set of test cases. Scenarios range from simple library upgrades (e.g., migrating from JUnit 4 to JUnit 5) to full framework overhauls (e.g., migrating from Spring MVC to Quarkus). Each scenario includes:

• A complete Maven or Gradle project with source code, tests, and build files.
• A clear description of the migration requirements.
• A set of automated tests that verify functional equivalence.

2. The Agent Interface

The agent interface standardizes how AI models interact with the migration tasks. It provides a sandboxed environment where agents can read files, write code, run builds, and execute tests. This interface supports both open-source and proprietary models, allowing fair comparison across different approaches. Agents can be given multiple attempts, and their intermediate steps are logged for analysis.

3. The Evaluation Pipeline

After an agent completes a migration attempt, the evaluation pipeline runs the test suite against the migrated code. It also performs static analysis to check for framework idiomatic usage, configuration correctness, and potential security vulnerabilities. Results are aggregated into a scorecard that highlights strengths and weaknesses.

Why ScarfBench Matters for Enterprise AI

The development of ScarfBench addresses a critical gap in AI evaluation. Most existing code benchmarks—such as HumanEval, MBPP, or SWE-bench—focus on writing new code from scratch or fixing bugs in small programs. While these benchmarks are valuable, they do not capture the complexity of enterprise software migration.

Enterprise migration requires:

• **Contextual understanding**: The agent must comprehend how the entire application works, not just isolated functions.
• **Long-range dependencies**: Changes in one file often require corresponding changes in many others.
• **Framework knowledge**: The agent must know not just Java syntax, but the conventions and APIs of specific frameworks.
• **Configuration management**: Many frameworks rely on external configuration files that must be updated consistently.
• **Backward compatibility**: Migrated code must still integrate with existing databases, message queues, and external services.

ScarfBench directly tests these capabilities, making it a more relevant benchmark for organizations considering AI-assisted migration.

Practical Example: Migrating a Struts Application to Spring Boot

To illustrate what ScarfBench evaluates, consider a typical migration task: moving a small e-commerce application from Apache Struts 2 to Spring Boot. The original application has:

• A `LoginAction` class that handles user authentication.
• A `ProductController` that displays product listings.
• Several JSP pages with Struts tags.
• A `struts.xml` configuration file mapping actions to classes.
• A `web.xml` file with servlet configuration.

A successful ScarfBench migration would require the AI agent to:

1. **Identify the architecture**: Recognize that Struts actions map to Spring MVC controllers. 2. **Rewrite action classes**: Convert `LoginAction` into a `@Controller` or `@RestController` class with appropriate request mappings. 3. **Replace Struts tags**: Update JSP pages to use Spring MVC tags or migrate to Thymeleaf. 4. **Recreate configuration**: Generate `application.properties` or `application.yml` with equivalent settings. 5. **Handle dependency injection**: Replace Struts' ActionContext with Spring's `@Autowired` or constructor injection. 6. **Update build files**: Modify `pom.xml` or `build.gradle` to include Spring Boot dependencies and remove Struts dependencies. 7. **Ensure tests pass**: The existing unit tests (written for Struts) must be rewritten or adapted to work with Spring Boot.

The agent might need to make dozens of changes across multiple files. If it misses a single configuration entry or misinterprets an annotation, the entire migration could fail. ScarfBench scores the agent on how many of these tasks it completes correctly.

Insights from Early ScarfBench Evaluations

While detailed results from ScarfBench are still emerging, early evaluations published on platforms like the Hugging Face Blog suggest several interesting patterns:

• **Large models outperform smaller ones, but with diminishing returns**: Models with 70 billion parameters or more generally achieve higher correctness scores than smaller models, but the gap narrows for well-defined migration patterns.
• **Chain-of-thought prompting helps**: Agents that generate step-by-step migration plans before writing code tend to produce more coherent results than those that attempt direct translation.
• **Configuration is the hardest part**: Many agents correctly migrate Java source files but fail to update configuration files properly. This is a critical weakness because misconfigured applications may compile but fail at runtime.
• **Error recovery is poor**: When agents encounter compilation errors, they often make the same mistake repeatedly rather than learning from the failure.

These findings, discussed in analyses from DeepMind Blog and MIT Technology Review AI, highlight that while AI agents are making progress, they are not yet ready for unsupervised enterprise migration.

Alignment and Safety Considerations

The topic of AI alignment is particularly relevant to ScarfBench. The AI Alignment Forum has discussed how code-generating models can introduce subtle errors that are difficult to detect. In the context of framework migration, an AI agent might:

• Introduce security vulnerabilities by misconfiguring authentication.
• Break transaction boundaries, leading to data corruption.
• Remove necessary exception handling, causing crashes in production.
• Introduce performance regressions through inefficient framework usage.

ScarfBench includes alignment metrics that flag such issues. It also tests whether the agent respects invariants that the original code relied on, even if those invariants are not explicitly documented. This focus on safety is essential for building trust in AI-assisted migration tools.

The Future of AI-Assisted Migration

ScarfBench is not just an academic exercise. As organizations grapple with technical debt and the need to modernize, AI-assisted migration could dramatically reduce costs and timelines. However, the benchmark makes clear that we are still in the early stages.

The most promising approach appears to be human-in-the-loop migration, where an AI agent performs the bulk of the mechanical work, and a human expert reviews and corrects the output. ScarfBench provides a way to measure how much human oversight is needed for different migration scenarios.

Looking ahead, we can expect:

• **Specialized fine-tuning**: Models fine-tuned on migration data will likely outperform general-purpose models.
• **Interactive agents**: Future benchmarks may allow agents to ask clarifying questions during migration.
• **Multi-framework support**: ScarfBench could expand to cover migrations to non-Java frameworks, such as Kotlin or Go.
• **Continuous evaluation**: As AI models improve, ScarfBench will be updated with new, harder tasks.

Conclusion

ScarfBench represents a significant step forward in evaluating AI agents for real-world software engineering tasks. By focusing on the specific challenge of enterprise Java framework migration, it addresses a pain point that affects thousands of organizations worldwide. The benchmark reveals both the promise and the limitations of current AI models: they can handle routine migration patterns but struggle with configuration complexity, error recovery, and safety-critical edge cases.

For now, enterprise teams should view AI agents as powerful assistants rather than autonomous migration tools. ScarfBench provides a rigorous way to measure their capabilities and track progress over time. As the technology matures, we may see a future where AI agents handle the bulk of framework migrations, freeing human developers to focus on architecture, design, and innovation. But that future requires benchmarks like ScarfBench to ensure the agents are truly ready for the enterprise.