Legacy Source Code Translation Engine for Cloud Migration
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Solution Overview
Problem
Legacy systems are costly and inefficient in handling large data volumes and analytics, requiring expensive infrastructure maintenance and manual data migration processes, which are time-consuming and prone to errors when transitioning to cloud-based solutions.
Innovation Solution
A system and method utilizing a source code translation engine to convert legacy source code into cloud-native code by constructing an abstract syntax tree, identifying patterns, and translating tokens into a cloud-native abstract syntax tree, minimizing human intervention and reducing migration duration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual translation of legacy code to cloud native code is performed, then translation accuracy can be maintained, but migration time and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical translation processes with an automated computer-based translation system. The system uses abstract syntax trees, pattern matching algorithms, and rule-based transformation to automatically convert legacy code into cloud native code, eliminating the need for manual line-by-line translation while maintaining accuracy through structured parsing and validation mechanisms.
Solution Approach 2:
The translation system performs self-service by automatically analyzing legacy code structure, generating abstract syntax trees, identifying translation patterns, and producing cloud native code without requiring continuous human intervention. The system includes built-in validation and error handling that enable it to autonomously manage the translation process.
2Productivity
If automated translation system is implemented, then migration speed increases, but system complexity increases
Solution Approach 1:
The translation system is segmented into distinct functional modules: legacy code parser, abstract syntax tree generator, pattern identifier, translation rule engine, and cloud native code generator. Each module handles a specific aspect of the translation process, making the overall complex system manageable through modular architecture where each component can be developed, tested, and maintained independently.
3Reliability
If legacy systems are maintained with manual processes, then control over translation quality is maintained, but labor costs and time consumption increase
Solution Approach 1:
The translation system incorporates feedback mechanisms that validate translated code against predefined rules and patterns. The system includes error detection, validation against cloud platform requirements, and the ability to identify and report translation issues, ensuring that automated translation maintains quality standards comparable to manual review while significantly improving migration efficiency.
Data Source
AI summary
The present invention provides for a system and a method for translating a legacy source code to a cloud native code. The present invention provides for receiving a source code and deriving a plurality of queries from the source code and the queries comprise a plurality of tokens. The present invention provides for constructing an abstract syntax tree in the form of a data structure from the tokens. The present invention provides for traversing the abstract syntax tree, the identified pattern, the scope table and the syntax table for translation of the tokens of the abstract syntax tree into new tokens stored in the form of a cloud native abstract syntax tree. The present invention provides for concatenating the new tokens stored in the cloud native abstract syntax tree to generate a translated cloud native code to be hosted on a cloud platform.