The modernization of enterprise applications stands as one of the most significant and costly endeavors in software engineering. As organizations strive to migrate applications across frameworks, the goal is to enhance maintainability, cloud readiness, and developer productivity. Recent advancements in AI-assisted coding agents have generated interest, but a critical question persists: can these agents effectively manage the complexities of real-world enterprise application migration?
To address this challenge, IBM Research has unveiled ScarfBench (Self-Contained Application Refactoring Benchmark), an open benchmark designed to evaluate AI agents on cross-framework migration tasks within the Enterprise Java landscape. ScarfBench specifically targets migrations across three prominent Java ecosystems: Spring, Jakarta EE, and Quarkus.
Understanding Migration Challenges
Framework migration encompasses more than mere code translation; it involves intricate adjustments to build systems and runtime dependencies. A seemingly simple repository migration can necessitate extensive changes across various components, including dependency injection and persistence configurations. Small errors in any of these areas can lead to deployment failures.
Introducing ScarfBench
ScarfBench provides a structured approach to assess AI agents during enterprise Java framework migrations. The benchmark requires that migrated applications:
• Build successfully.
• Deploy correctly.
• Pass behavioral validation.
This framework offers a more realistic measure of modernization quality compared to traditional benchmarks, which often focus solely on code generation.
Performance Insights from ScarfBench
In evaluating several leading coding agents using ScarfBench, it became evident that despite their strong performance on conventional software engineering benchmarks, framework migration remains a formidable task. The success rates for these agents varied significantly across different framework pairs, with whole-application migrations proving particularly challenging. Notably, even the most proficient agents achieved less than 10% behavioral success, underscoring the gap between generating compilable code and maintaining application behavior.
ScarfBench also sheds light on how these agents operate during the migration process. For instance, agents exhibited overconfidence in their assessments, with discrepancies between reported and actual build successes. Furthermore, migration efforts were found to be iterative rather than linear, often requiring repeated visits to configuration-related artifacts to resolve framework differences.
Key Takeaways and Future Directions
The primary obstacle in framework modernization lies not in translating Java code, but in managing the intricate web of dependencies across configuration, infrastructure, and runtime environments. While AI agents can automate significant portions of the migration process, reliable validation and architectural reasoning remain essential for successful outcomes.
ScarfBench serves as a vital resource for both researchers and practitioners, offering a standardized method to measure progress in AI-assisted application modernization. By inviting contributions from the community, ScarfBench aims to accelerate advancements in this critical area of software engineering.
This article was produced by NeonPulse.today using human and AI-assisted editorial processes, based on publicly available information. Content may be edited for clarity and style.
