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Agent-Based Code Analysis and Migration of a Logistics System
Brief description
In this project, a subsystem of a freight forwarding and logistics service provider´s core operational system is being modernized using an agent-based approach. The system, which has evolved over decades, consists of COBOL services, a Java Swing fat client, and a JSF-based web interface; it is being migrated to a modern target architecture featuring an Angular front end and a Kotlin back end. Instead of translating the legacy code line by line, the approach first establishes a complete understanding of the existing system, uses this to create a human-readable design specification, and only generates new source code in the third step. The goal is a production-ready system that can run in hybrid mode alongside the parts of the software that have not yet been migrated, as well as demonstrating that the effort, process, and cost share associated with AI usage can be measured with sufficient reliability to derive the costs for migrating the entire system.
Supplement
The process is divided into three phases. First, the team maps the legacy system using six independent sources: the architectural model, the physical data model, COBOL source code, Java and JSF source code, recorded user workflows, and expert interviews. An extraction pipeline consolidates these sources into a shared knowledge base that can be queried via SQL, cross-checks them against one another, and resolves inconsistencies with documented confidence levels. A field glossary containing approximately 7,500 field codes translates technical abbreviations into business terms. Building on this, domain-expert-reviewed design specifications are created, from which target code is generated only in the third step. Between phases, approvals, deterministic checks, automated tests, and a nightly build validate the results. AI development, software development, the business unit, and operations all work within a shared repository so that all stakeholders use the same project context.
Subject description
From a technical standpoint, the focus is on operational order and transport management. The migration includes the quick entry of freight forwarding orders as well as order- and transport-related additional functions-that is, the dialog boxes in which shipment data is entered, verified, scheduled, and released. This includes the associated validation rules, value lists, derived fields, calculations, and conflict resolution during order approval. Consistency in behavior is key: Given the same inputs, the new application delivers the same results, error codes, and workflows as the legacy system, allowing business units to continue working without having to adjust to a new system. The target architecture replaces the 'fat client' delivered via a terminal server with a browser-based application and allows for the switching between migrated and non-migrated functions during live operations, so that the replacement occurs gradually rather than all at once.
IT project data
- Industries Logistics
- Specialist tasks artificial intelligence, haulier, Migration, order processing, scheduling, transportation
- DP tasks Migration
- System environments Artificial Intelligence (AI), Client/Server, Internet/Intranet, multitier-architecture
- Procedure model agile software development
- Programming languages Cobol, Java, Kotlin, Python, TypeScript
- Databases Informix, SQLite
- Development and test tool Git, JEE, Playwright
- Middleware and frameworks Angular, Hibernate, JPA, Swing, Wildfly
Project start09.04.2026
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