ScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration

Modernizing legacy enterprise applications is one of the most significant technical debts facing large organizations today. Specifically, in the Java ecosystem, migrating from Spring Boot 2.x to 3.x or transitioning from the older javax namespace to the modern jakarta namespace involves thousands of manual code changes, dependency resolutions, and configuration updates. ScarfBench has emerged as a critical benchmark to evaluate how effectively Large Language Models (LLMs) and AI agents can navigate these treacherous waters. By leveraging the unified API access provided by n1n.ai, developers can now test multiple high-end models against these benchmarks to find the most cost-effective path for their infrastructure.

The Challenge of Java Modernization

Java enterprise applications are notoriously complex. Unlike small Python scripts, a typical enterprise Java project consists of deeply nested dependency trees, XML or YAML-based configurations, and heavy use of reflection and annotations. When a framework like Spring Boot undergoes a major version jump, the breaking changes are not just syntactic; they are structural.

ScarfBench (Source Code Analysis and Refactoring Framework Benchmark) focuses on these real-world complexities. It provides a standardized environment to measure how well an AI agent can:

1. Identify Deprecated APIs: Detecting methods and classes that no longer exist in the target version.
2. Resolve Dependency Conflicts: Updating Maven or Gradle build files while ensuring version compatibility.
3. Refactor Boilerplate: Converting legacy patterns into modern Java 17+ or 21+ idioms.
4. Handle Ecosystem Shifts: Such as the massive migration from javax.servlet to jakarta.servlet.

Why ScarfBench Matters for AI Agents

Standard benchmarks like HumanEval focus on simple function-level completion. However, migration is a repository-level task. An AI agent must understand the context of the entire project to make a change in one file that doesn't break a dependency in another. This requires high context windows and sophisticated reasoning capabilities.

When using the aggregated model endpoints at n1n.ai, enterprises can compare the performance of models like Claude 3.5 Sonnet and GPT-4o on ScarfBench-style tasks. Our internal testing suggests that while GPT-4o excels at general logic, Claude 3.5 Sonnet often shows superior performance in maintaining strict adherence to Java type systems during complex refactoring.

Technical Implementation: A Migration Workflow

To implement an AI-driven migration agent, one typically follows a ReAct (Reasoning and Acting) pattern. Below is a conceptual Python snippet demonstrating how to interface with an LLM via n1n.ai to analyze a legacy Java file for Jakarta migration.

import requests

def analyze_java_file(file_content):
    api_key = "YOUR_N1N_API_KEY"
    url = "https://api.n1n.ai/v1/chat/completions"

    prompt = f"""
    Analyze the following Java code for migration from Spring Boot 2.7 to 3.0.
    Focus on:
    1. javax to jakarta package renames.
    2. Security configuration changes.
    3. Deprecated WebSecurityConfigurerAdapter usage.

    Code:
    {file_content}
    """

    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }

    data = {
        "model": "claude-3-5-sonnet",
        "messages": [{"role": "user", "content": prompt}],
        "temperature": 0.2
    }

    response = requests.post(url, headers=headers, json=data)
    return response.json()['choices'][0]['message']['content']

ScarfBench Performance Comparison

According to recent data, the performance of AI models on ScarfBench varies significantly based on the task difficulty. Below is a generalized comparison of top-tier models accessible via our platform:

Note: Success rates are based on the ScarfBench automated test suite where the code must compile and pass unit tests after migration.

Advanced Strategy: RAG-Enhanced Migration

One of the "Pro Tips" for developers using ScarfBench is the integration of Retrieval-Augmented Generation (RAG). Instead of feeding the LLM the entire codebase, which might exceed the context limit or lead to "lost in the middle" syndrome, you can use a vector database to store official Spring migration guides.

When the agent encounters a specific error (e.g., a missing WebSecurityConfigurerAdapter), it queries the RAG system for the specific migration path. This ensures that the agent's output is grounded in official documentation rather than hallucinated patterns. Using n1n.ai as your backbone, you can swap between models to find the one that best interprets the retrieved documentation context.

Key Considerations for Enterprise Use

1. Token Cost vs. Manual Labor: While high-reasoning models like o1-preview are expensive, they are significantly cheaper than hiring a senior Java developer for six months to perform a manual upgrade.
2. Privacy: Enterprise code is sensitive. Always ensure you are using enterprise-grade API providers that offer data privacy guarantees.
3. Verification: Never trust an AI agent blindly. The output of an AI migration tool must always be piped into a CI/CD pipeline where automated tests (JUnit, Mockito) can verify the integrity of the changes.

Conclusion

ScarfBench has proven that while we are not yet at the point of "one-click migrations" for massive monoliths, AI agents are becoming incredibly proficient at the heavy lifting of framework modernization. By utilizing specialized benchmarks and robust API aggregators like n1n.ai, organizations can systematically reduce their technical debt with unprecedented speed.

Get a free API key at n1n.ai