Microservice Generation Platform for Legacy Code Modernization
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Solution Overview
Problem
Legacy applications, often monolithic and written in outdated programming languages, are difficult to modify and extend due to limited compatibility and error-prone modernization efforts, which are time-consuming and subjective.
Innovation Solution
A generation platform that receives information about legacy code, generates a technical description, creates business rules in the SBVR format, and automatically generates microservices based on these rules and code templates, improving modularity and reducing manual programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual modernization of legacy applications is performed, then compatibility and functionality can be maintained, but the process is time-consuming and error-prone
Solution Approach 1:
The system performs self-analysis of legacy code automatically, generating technical descriptions and business rules without human intervention. The platform autonomously identifies code segments, extracts business logic, and transforms them into microservices, eliminating the need for manual modernization efforts while maintaining high accuracy through automated validation.
Solution Approach 2:
Manual mechanical processes of code analysis and transformation are replaced with automated computational systems. The platform uses AI-driven code parsing, automatic technical description generation, and algorithmic business rule extraction to substitute human experts, dramatically reducing time while maintaining or improving accuracy through consistent automated processing.
2Adaptability or versatility
If monolithic application structure is maintained, then existing functionality is preserved, but modularity and extensibility are limited
Solution Approach 1:
The monolithic application is automatically segmented into independent microservices based on business rules extracted from the code. Each microservice encapsulates specific business functionality, enabling modular architecture that improves adaptability and extensibility while managing complexity through standardized service interfaces and deployment units.
Solution Approach 2:
Business logic and functional components are extracted from the monolithic structure and isolated into separate microservices. This extraction process identifies and separates independent business capabilities, allowing them to be developed, deployed, and scaled independently, thereby enhancing modularity without increasing overall system complexity.
3Productivity
If automated microservice generation is implemented, then productivity and modularity improve, but complexity of the generation process increases
Solution Approach 1:
The platform performs preliminary actions by automatically generating technical descriptions and business rules from legacy code before microservice creation. This preliminary analysis phase structures the unstructured code into organized representations, simplifying the subsequent microservice generation process and reducing the apparent complexity through systematic preprocessing.
Solution Approach 2:
Technical descriptions and business rules serve as intermediary representations between the legacy code and the generated microservices. These intermediaries bridge the gap by translating complex code structures into simplified business logic models, making the generation process more manageable and reducing direct complexity in the transformation pipeline.
Data Source
AI summary
A device may receive information that identifies code associated with an application. The device may generate a technical description of the code based on the information that identifies the code associated with the application. The device may generate a set of rules based on the technical description of the code. The set of rules being associated with a semantics of business vocabulary and business rules (SBVR) format. The device may generate a set of microservices based on the set of rules. The device may provide information that identifies the set of microservices to cause an action to be performed.