Modernizing Enterprise Java with AI
Modernizing mission-critical Java applications at an enterprise scale enable innovation but also brings risks of disruption, dependency conflicts, and operational instability. Traditional approaches often fall short, extending project timelines while increasing security, performance, and readiness risks.
MoreModernizing mission-critical Java applications at an enterprise scale enable innovation but also brings risks of disruption, dependency conflicts, and operational instability. Traditional approaches often fall short, extending project timelines while increasing security, performance, and readiness risks.
In a large-scale Java modernization initiative, we worked with leading North American automotive enterprise, with over 500K lines of legacy code and thousands of daily users relying on the system. The effort demanded a careful balance between modernization, compliance, and future readiness.
By co-innovating with AWS and leveraging an AI-driven development assistant, we enabled a smarter, faster, and more reliable path forward.
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Client Background and Challenges
The client is a renowned North American automotive enterprise, with its core operations running on Java 8–based applications. The enterprise sought to upgrade its systems to Java 21 while minimizing disruption and ensuring operational continuity. The transformation was far more than a version upgrade.
It required resolving legacy APIs, dependency conflicts, security exposure, performance optimization, and exhaustive testing and validation, all without interrupting critical business operations.
Our Approach and Solution
API Modernization
Migrated legacy Java 8 APIs and constructs to modern Java 21 patterns.
Performance Tuning
Recommended optimizations for SQL queries and stream operations to improve runtime efficiency.
Security Hardening
Detected and addressed security risks, including SQL injection and unsafe deserialization.
Dependency Updates
Upgraded Spring Boot, Hibernate, and Apache Commons for seamless compatibility.
Test Automation
Generated JUnit test cases, enabling us to achieve 92% coverage.
Business and Community Impact
- Reduced Manual Effort: AI-assisted development cut manual migration effort by 30%, lowering delivery risk and accelerating execution.
- High Code Acceptance Rate: Over 60% of AI-recommended codes were accepted without modification, expediting the modernization process.
- Accelerated Testing Cycles: Test generation enabled 40% faster test creation, reducing validation time and regression risk.
- Improved Developer Productivity: Achieved a 35% gain in productivity by reducing manual refactoring and repetitive tasks.
- Augmented Code Quality and Compliance: Resulted in a 45% improvement in code quality, strengthening system stability and compliance.