Rising Complexity in Legacy Aviation Codebases
The client is a leading global aviation technology company delivering advanced solutions for airport and airside operations worldwide. Its platforms support critical operational functions, including deicing planning, airport capacity management, and real-time decision-making for major international airports.
The company maintains highly specialized, long-lived software systems built partly on a proprietary System Definition Language (SDL). As these systems evolved over time, increasing architectural complexity and tightly interconnected dependencies made knowledge transfer, maintenance, and modernization significantly more challenging.
- Complex proprietary legacy codebase: Critical systems were built using a custom SDL language developed over many years, requiring deep institutional knowledge to understand and maintain.
- Fragmented and undocumented knowledge: Business logic and technical knowledge were distributed across source code, PDFs, manuals, and senior engineers, with much of the information outdated or undocumented.
- Slow developer onboarding: New developers often required 3–6 months to become productive due to the steep learning curve and lack of centralized documentation.
- Limited impact analysis for code changes: Developers struggled to understand how changes affected interconnected modules and systems, increasing the risk of regressions in safety-critical environments.
An AI-powered code intelligence platform was required to centralize engineering knowledge, improve developer productivity, and reduce modernization risk across complex aviation systems.
Adopting an AI-Powered Code Assistant for Aviation Systems
To modernize legacy software development workflows and reduce engineering risk, our team implemented an AI-powered code intelligence platform that transforms complex proprietary codebases into a structured, searchable engineering knowledge system.
1. Building Semantic Code Search and Knowledge Retrieval
A semantic search layer was implemented to allow developers to search across codebases, documentation, and technical assets using natural language queries. Instead of relying on exact keyword matching, the platform interprets developer intent and retrieves contextually relevant results.
Semantic retrieval significantly improves knowledge discovery and reduces the time required to understand unfamiliar systems.
2. Transforming Codebases into Knowledge Graphs
The platform automatically converts files, functions, APIs, and services into structured knowledge graphs that visualize relationships across the technology stack.
Knowledge graph architecture enables developers to understand how components interact across systems, improving debugging, refactoring, and architectural analysis.
3. Enabling Conversational Code Intelligence
An AI-powered conversational interface allows developers to explore legacy systems using natural language. Teams can request explanations of functions, understand dependencies, and analyze business logic without manually tracing code.
Conversational interaction simplifies navigation of complex architectures and accelerates onboarding for engineers unfamiliar with proprietary systems.
4. Automating Technical Documentation Generation
The platform automatically generates technical documentation from source code, commit histories, and engineering artifacts. Documentation workflows reduce the burden of manual maintenance while improving consistency and accessibility across teams.
Automated documentation ensures engineering knowledge remains current and easier to distribute across the organization.
Renovating Legacy Systems into Intelligent Engineering Knowledge
This partnership transformed complex legacy codebases into an intelligent and searchable engineering knowledge platform. Developers can now explore system architecture, dependencies, and business logic more efficiently, significantly reducing the time required to understand unfamiliar environments.
Semantic search and AI-driven code intelligence improve developer productivity while reducing reliance on tribal knowledge within engineering teams. Automated documentation and knowledge graph visualization further enhance collaboration and accelerate onboarding across complex aviation technology systems.
Before
- Proprietary legacy code understood by a limited number of experts
- Documentation fragmented across source files and outdated materials
- Long onboarding cycles for new developers
- High risk when modifying interconnected systems
- Limited understanding of dependencies and business logic
After
- Centralized and searchable engineering knowledge platform
- Automatically generated and continuously updated documentation
- Faster onboarding with reduced dependency on key individuals
- Improved visibility into system architecture and code relationships
- Safer maintenance and modernization of mission-critical systems
A scalable, AI-driven architecture positions SITA to continue modernizing its engineering operations while preserving valuable institutional knowledge embedded within legacy systems.
As part of KMS Technology, Addepto continues to deliver enterprise-grade AI solutions that help organizations transform complex technical environments into intelligent, accessible, and future-ready platforms.
Ready to modernize your legacy systems? Contact us today!