ML-Based COBOL to Java Translation with Automated Testing
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Solution Overview
Problem
Existing methods for modernizing COBOL and other legacy programming languages into modern languages are error-prone, resource-intensive, and lack accuracy in processing parity, due to the scarcity of subject-matter-experts and the inefficacy of prior Machine Learning and Artificial Intelligence systems.
Innovation Solution
The use of machine learning models, specifically Natural Language Models, to translate COBOL and other legacy languages into modern languages like Java, Golang, Python, Angular, or C++, through a process involving training on specific data sets, iterative testing, and containerization into microservices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and rewriting of COBOL codes is performed, then conversion accuracy can be maintained, but resource intensity and time consumption increase significantly
Solution Approach 1:
The patent introduces an AI/ML-based translation system as an intermediary between COBOL and modern languages. This intermediary automatically analyzes COBOL code, generates modern language equivalents, and creates test cases, thereby maintaining conversion accuracy while dramatically reducing the need for manual expert intervention and associated resource consumption.
Solution Approach 2:
The translation system employs self-service mechanisms through automated testing and validation. The system generates unit test cases that automatically execute against both the original COBOL code and the translated modern language code, enabling self-validation of conversion accuracy without requiring continuous manual verification by experts.
2Extent of automation
If prior Machine Learning and AI systems are used for language translation, then automation is improved, but accuracy in interpreting programming languages and achieving processing parity deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the AI/ML model is iteratively trained using results from automated testing. Unit test cases are generated and executed to compare processing outputs between original COBOL code and translated modern language code. The feedback from these comparisons is used to continuously refine and improve the translation model's accuracy in achieving processing parity.
Solution Approach 2:
The system performs preliminary actions by generating comprehensive unit test cases before finalizing the translation. These test cases are executed against both the source COBOL code and the target modern language code to pre-validate the translation accuracy. This preliminary testing ensures that the automated translation achieves processing parity before deployment.
3Measurement precision
If iterative testing and refinement of the ML model is performed, then translation accuracy is improved, but time consumption increases
Solution Approach 1:
The patent establishes continuity of useful action through automated iterative testing and refinement. Instead of间断性 (intermittent) manual review, the system continuously generates test cases, executes them, and refines the translation model automatically. This continuous automated process improves translation accuracy over time while reducing the need for manual intervention, thereby offsetting the time investment through automation.
Solution Approach 2:
The patent replaces the mechanical system of manual iterative review with an automated computational system. The AI/ML model undergoes iterative refinement through automated testing frameworks that generate, execute, and analyze test cases without human intervention. This substitution of mechanical manual processes with automated computational processes accelerates the iterative improvement cycle.
4Stability of the object's composition
If legacy COBOL systems are maintained for critical business functions, then operational stability is preserved, but risk associated with legacy systems increases
Solution Approach 1:
The patent applies preliminary action by translating and validating modern language equivalents of COBOL code before replacing the legacy systems. Unit test cases are generated and executed to ensure the translated code maintains operational stability. Once validation is complete, the organization can confidently migrate to modern languages, reducing legacy system risk while preserving operational stability.
Solution Approach 2:
The AI/ML translation system serves as an intermediary that enables safe migration from legacy COBOL systems to modern languages. It produces validated translations that maintain operational stability, allowing organizations to transition away from high-risk legacy systems while preserving the stability of critical business functions through rigorously tested modern language implementations.
Data Source
AI summary
Systems and methods for translating a first coding language into a second coding language train a machine learning (ML) model on a first coding language specific data set relating to the first coding language, in which the ML model is trained to translate one or more code sets of the first coding language to respective one or more code sets of the second coding language; using the ML model, generate various unit test cases, in which the unit test cases run the one or more code sets of the second coding language in parallel with the one or more code sets of the first coding language; iteratively test and refine the ML model until a maturity threshold is reached; and upon reaching the maturity threshold, containerize the one or more code sets of the second coding language into one or more applications.