AI Semantic Analysis for Legacy Software Modernization
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Solution Overview
Problem
Legacy software programs face challenges in updating due to inconsistencies in optimization and documentation, as well as the use of outdated programming languages, which can lead to inefficiencies and inaccuracies in translation and virtualization processes.
Innovation Solution
A legacy software processing system that automates the identification of requirements, generates new operational code, and converts legacy code into modern programming languages, eliminating the need for emulators and converters, while providing a clear understanding of the software's functionality and facilitating future maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If legacy software is updated using traditional virtualization or translation methods, then compatibility with modern hardware is improved, but inconsistencies in optimization and documentation arise, and the software requires emulators or converters that add complexity
Solution Approach 1:
The patent replaces mechanical translation/virtualization systems with an AI-based semantic understanding system. Instead of using emulators or converters that mechanically translate code, the system uses natural language processing and semantic analysis to directly generate optimized modern code, eliminating the need for intermediate translation layers and reducing complexity
Solution Approach 2:
The system enables legacy software to update itself automatically through AI-driven semantic analysis. The process autonomously identifies requirements, generates new operational code, and maintains documentation without human intervention, replacing the complex manual or semi-automated traditional update processes
2Adaptability or versatility
If legacy code is translated to modern programming languages using conventional methods, then the software can run on current systems, but inaccuracies and inconsistencies in the translation process occur
Solution Approach 1:
The patent replaces mechanical code translation with AI-based semantic understanding and generation. The system analyzes the semantic meaning of legacy code through natural language processing and generates accurate modern code that preserves the original functionality, eliminating translation inaccuracies inherent in conventional mechanical translation methods
Solution Approach 2:
The system incorporates feedback mechanisms where the AI model continuously learns from translation results and refines its semantic understanding. This iterative feedback process improves translation accuracy over time by identifying and correcting inconsistencies in the generated code
3Adaptability or versatility
If multiple programming languages are used in legacy software updates, then the software can leverage modern language features, but unique compilers and interpreters are required for each language, increasing complexity
Solution Approach 1:
The patent implements a universal AI-based code generation system that can produce modern code in any programming language from a single semantic analysis process. This multi-functional approach eliminates the need for separate compilers and interpreters for each language, as the AI system can generate code in the target language directly
Solution Approach 2:
The system introduces an AI-based semantic intermediary that sits between legacy code and modern programming languages. This intermediary analyzes the semantic meaning and generates code in the desired modern language, replacing the need for multiple language-specific translation tools and simplifying the update process
4Ease of repair
If legacy software is updated manually by different programmers, then the software can be maintained, but inconsistencies in optimization and documentation occur over time
Solution Approach 1:
The system enables automatic self-updating of legacy software through AI-driven semantic analysis and code generation. This eliminates manual intervention by multiple programmers, ensuring consistent optimization and documentation standards are applied uniformly across all updates without human variability
Solution Approach 2:
The system standardizes update parameters by using consistent AI-based semantic analysis and generation processes. This ensures that optimization criteria, documentation standards, and code quality metrics remain uniform across all updates, preventing the inconsistencies that arise from manual updates by different individuals
Data Source
AI summary
A method includes analyzing operational code to determine identifiers used within the operational code. The method further includes grouping like identifiers based a relational aspect of the identifiers. The method further includes, for one or more identifier groups, determining potential feature(s) of the identifier group(s). The method further includes testing the potential feature(s) based on a corresponding feature test suite to produce feedback regarding meaningfulness of the potential feature(s). The method further comprises, when the meaningfulness is above a threshold, adding the potential feature(s) to a feature set. The method further includes, when the meaningfulness is at or below the threshold, adjusting analysis parameter(s), grouping parameter(s), feature parameter(s), and/or testing parameter(s).