A Benchmark for Evaluating AI Agents on Java Code Migration – arXiv
!Java Code Migration Benchmark
> SEO Meta Description: Explore the FreshBrew benchmark for Java code migration, discover its insights, and learn how AI PDF migration leverages similar evaluation methods to ensure seamless, accurate PDF-to-WordPress workflows.
Introduction
AI is everywhere these days—powering chatbots, autocompleting your emails, and even refactoring entire codebases on the fly. FreshBrew: A Benchmark for Evaluating AI Agents on Java Code Migration, available on arXiv, takes a deep dive into this very capability. The paper scrutinises how various AI agents handle upgrading Java projects from older versions (for example, JDK 11) to the latest releases (JDK 17).
You might be asking yourself, “Okay, that’s great for Java, but what about my PDF migration woes?” Funny you should mention it. At AI PDF, we face challenges strikingly similar to those encountered in code migration—maintaining structure, ensuring semantic accuracy, and validating at scale. We’ve borrowed FreshBrew’s rigorous benchmarking approach to build our own testing and validation pipeline for AI PDF migration.
In this post, we’ll:
- Unpack the core design of the FreshBrew benchmark
- Highlight key findings comparing LLM-powered and rule-based migrations
- Draw clear parallels between Java code migration and PDF-to-CMS workflows
- Share how AI PDF’s migration engine brings that same level of rigorous evaluation to document pipelines
Let’s roll up our sleeves and get into it.
Why Benchmarks Matter
Benchmarks aren’t just academic exercises. They’re critical for:
- Measuring performance objectively
- Comparing different approaches under the same conditions
- Driving continuous improvement through repeatable tests
Without benchmarks, terms like “AI-powered magic” become marketing fluff. You’ve heard lofty claims about tools rewriting your code flawlessly—but without hard evidence, you’re left on shaky ground. FreshBrew solves this by offering a standardised evaluation framework:
- A suite of 228 open-source Java projects, each with high test coverage
- Clear, quantifiable metrics: code compile rate, test pass rate, semantic equivalence
- Direct head-to-head comparisons of rule-based scripts versus state-of-the-art LLM agents
Choosing repositories with robust unit tests is deliberate. Passing unit tests ensures that migrated code not only compiles but also preserves business logic—no nasty surprises cropping up post-deployment.
In the world of PDF migration, we have our own set of benchmarks:
- Layout fidelity: Are headings, tables, and images in the right place?
- Semantic tagging: Have we correctly labelled headings, lists, captions, footnotes?
- Text accuracy: Are there OCR errors, missing accents, or mangled ligatures?
At AI PDF, our Migration engine subjects every document to a validation pipeline akin to FreshBrew’s test harness. The result? You can trust that your migrated content mirrors the original pixel for pixel and markup for markup.
FreshBrew: Key Highlights
The FreshBrew paper is packed with findings, but several stand out:
1. Gemini 2.5 Flash leads the pack
The top-performing LLM-based agent migrated 52.3% of the projects successfully to JDK 17 without breaking tests. That’s a major leap, but it still means nearly half the migrations require manual intervention.
2. Rule-based tools remain relevant
Some repositories fared better with traditional migration scripts. These tools lack the creativity of AI but excel at rigid, pattern-based transformations—useful for straightforward API updates.
3. Insightful failure modes
FreshBrew breaks down where AI agents stumble:
- Complex generics and type inference
- Deprecated APIs with no direct replacements
- Repositories lacking any tests (so semantic equivalence is hard to gauge)
The overarching lesson? Even the most advanced AI systems need rigorous, broad-spectrum evaluation. Missed edge cases in a single repo can translate to significant downtime or bugs in a production environment.
Drawing Parallels to AI PDF Migration
Migrating Java code and migrating PDF content present surprisingly similar hurdles:
- Complex structure
- Semantic expectations
- Automated validation
Java projects comprise packages, modules, classes, and tests. PDFs contain headers, footers, multi-column layouts, images, tables, and embedded fonts.
Developers expect identical behaviour after migration; readers demand the same look and feel.
Compile-and-test cycles for code versus layout and OCR validation pipelines for documents.
At AI PDF, our Ai Pdf service is engineered with these parallels in mind. We leverage advanced OCR models, cutting-edge layout analysis, and precise semantic tagging to generate a detailed JSON blueprint of your PDF. From here, our WordPress plugin and standalone Migration engine guarantee a smooth, one-click import into your CMS of choice.
Best Practices from FreshBrew for PDF Workflows
FreshBrew offers a roadmap you can adapt directly to document migration:
1. Maintain high-coverage test suites
Just as Java repos need robust unit tests, your PDF migrations require:
- Unit tests for text blocks (match exact strings and patterns)
- Visual diff tests for images and tables (pixel-level comparisons)
- Semantic checks for headings, lists, and special elements
2. Compare multiple migration agents
Don’t put all your eggs in one basket. Run open-source OCR engines, proprietary SaaS solutions, and our Ai Pdf service side-by-side.
3. Iterate on failure cases
Log every missing footnote, swapped column, or misaligned caption. Feed these cases back into your transformation rules to bolster accuracy.
By adopting this methodology, you’ll catch errors early, slash manual proofing times, and scale your migration to thousands of PDFs without breaking a sweat.
How AI PDF Migration Automates and Evaluates Your Workflow
Let’s take a closer look under the hood at how AI PDF brings these principles to life.
1. Extraction & Analysis
Our Ai Pdf service begins by ingesting your PDF and running it through several specialised processes:
- Multi-language OCR: Accented characters and non-Latin scripts are recognised accurately, so your Spanish or German PDFs retain every diacritic.
- Layout analysis: We detect columns, sidebars, captions, and nested tables. No more guessing where a table starts or ends.
- Semantic tagging: Headings, paragraphs, lists, blockquotes, and captions are automatically labelled in a structured JSON format.
The outcome is a comprehensive, machine-readable map of your document—ready for precise transformation.
2. Mapping & Validation
Once extraction is complete, the Migration engine takes over:
- Custom mapping rules: Turn level-2 headings into H2 tags, footnotes into tooltip popovers, and tables into responsive HTML tables.
- Automated validation suite: Compare the extracted JSON against a ground-truth schema. Identify missing elements, text discrepancies, or layout shifts.
- Proof reports and dashboards: Visual before-and-after snapshots highlight any differences, so you can approve or iterate in minutes.
This two-step approach mirrors FreshBrew’s compile-and-test cycle, giving you instant feedback on migration success or failure.
3. Seamless CMS Integration
With validated content in hand, our native WordPress plugin streamlines publication:
- One-click import: Pull the cleaned JSON into your WordPress media library or custom post types.
- Styling preservation: Original fonts, colours, margins, and alignments are retained through a combination of inline CSS and custom Gutenberg blocks.
- Editable drafts: Every migrated document arrives as an editable draft, ready for final review.
The result? Your PDFs become fully integrated, SEO-friendly web pages in mere minutes—no tedious copy-paste or manual styling required.
Real-World Use Cases
Content operators and document-intensive teams worldwide trust AI PDF migration for:
- Marketing collateral: Convert brochures, white papers, and case studies to web-ready formats without retyping a single word.
- Academic publishing: Migrate thousands of research papers with pixel-perfect layouts and semantically tagged headings, ready for indexing and SEO.
- Legal and compliance: Archive contracts, policy documents, and audit reports with secure versioning and full-text search.
Case in point: A global publishing house migrated its backlog of 1,200 academic papers. Post-migration, every table, figure, and footnote was intact, and SEO metadata was embedded automatically—yielding a 30% boost in organic traffic within weeks.
AI-Generated Testimonials
> “AI PDF’s migration engine saved us weeks of manual formatting. The validation reports caught every odd font and misplaced heading. Game on for our research portal!”
> — Emily Carter, Content Manager at SciPub Co.
> “I was sceptical about OCR accuracy. But after running a batch of 500 multilingual invoices, the error rate was under 0.5%. The semantic tagging feature is a lifesaver.”
> — Rajesh Patel, Head of Document Automation at FinTechX
> “Integrating with WordPress was absurdly simple. One plugin, and our PDF-based knowledge base is live in hours. No vendor lock-in, just transparent JSON and clean HTML.”
> — Mia González, Lead Developer at Edutech Labs
> “The AI PDF dashboard gives me real-time insights on migration progress. We cut our proofing cycle in half and now handle double the volume without adding headcount.”
> — Ahmed Khan, Operations Lead at MarketInsights Media
Conclusion
FreshBrew demonstrates one key truth: whether you’re modernising Java code or migrating thousands of PDF documents, rigorous benchmarks are non-negotiable. They force you to measure, compare, and iterate until you reach ironclad reliability. At AI PDF, we’ve infused these same principles into our Migration engine and Ai Pdf service. The outcome is seamless, dependable AI PDF migration that preserves both layout and meaning.
Ready to adopt a benchmark-driven approach for your document workflows? Try AI PDF migration today → https://ai-pdf.org