System Modernization through AI driven Code Refactoring
System Modernization through AI driven Code Refactoring
Business Overview
A legacy industrial monitoring system was under strain—frequent memory leaks, inconsistent outputs, and tightly coupled code were making maintenance a nightmare. NeST Digital stepped in to modernize the system through AI-supported code refactoring, improving system stability, performance, and maintainability without a full rewrite.
Business Problem
An embedded sensor data monitoring system—used for analyzing vibration, temperature, and wavelength—was facing significant technical debt. With duplicated logic across channels, poor error handling, and unstable performance, the system experienced frequent crashes and inconsistent analog outputs. This unreliability affected real-time monitoring and downstream processes, delayed upgrades, and increased the engineering overhead for support and testing. The client needed a scalable, efficient way to stabilize the system and prepare it for future enhancements.
Unstable System
Frequent crashes and inconsistent analog outputs.
High Engineering Overhead
Manual fixes slowed support and testing cycles.
Blocked Upgrades
Technical debt delayed new feature development.
The AI-Powered Solution
NeST Digital adopted an AI-driven refactoring approach to improve code quality and maintainability without introducing regressions. The solution included:
- Auto-generating NUnit-based test cases to validate functionality during refactoring.
- Structuring prompts with architectural context and standards to enable precise improvements.
- AI-supported identification and elimination of redundant logic (especially channel-specific code).
- Refactoring memory management using the IDisposable pattern.
Centralized error handling and logging. - Use of AI tools like Copilot, ChatGPT, and TabNine to speed up and validate changes.
Refactoring was done incrementally, with each module tested and validated before integration—ensuring minimal disruption to the live system.
The Impact
These improvements resulted in a more stable, reliable platform—cutting support overhead and unplanned downtime. AI-led refactoring sped up release cycles, streamlined onboarding, and reduced maintenance costs, allowing engineers to focus on innovation instead of constant issue resolution
Stable Performance
Reduced crashes and unplanned downtime.
Faster Releases
AI-led refactoring sped up deployments.
Lower Maintenance
Cut support overhead and costs.
Freed-Up Engineering
Teams focused more on innovation.
Why this Matters
The result wasn’t just cleaner code—it was a future-proof system. With the help of AI, NeST Digital helped the client stabilize a business-critical platform, enabling easier testing, quicker enhancements, and a more confident roadmap for innovation.
Quantifiable Improvements Summary
54%
Code Reduction
Significant reduction in lines of code.
95%
Class Size
Reduction per file.
80%
Duplication
Code Duplication minimized.
100%
Memory Leaks
Resolved and stabilized.
Benefits Achieved Through Refactoring
Code Reduction
Reduced total lines of code by ~54%.
Maintainability
Clear separation of concerns for easier modifications.
Error Handling
Centralized error logging via Error Logging Service.
Resource Management
Eliminated memory leaks; improved stability.
SHARE
