ML Component Identification for Legacy System Modernization
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Solution Overview
Problem
Legacy computer systems in large organizations face challenges in maintenance and modernization due to a shortage of skilled programmers and lack of infrastructure support, making it difficult to leverage modern technologies like machine learning for improved functionality and efficiency.
Innovation Solution
A method and system that identifies suitable components within these systems for replacement with machine learning components based on factors such as task criticality, code complexity, data modalities, and historical data usage, allowing for semi-automatic integration and monitoring of machine learning components to ensure performance equivalence before replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy systems are maintained using traditional programming approaches, then system stability and reliability are preserved, but maintenance difficulty increases and modernization capabilities are limited due to shortage of skilled programmers
Solution Approach 1:
The system performs self-analysis by automatically scanning legacy code, identifying components suitable for machine learning replacement, and generating replacement recommendations without requiring extensive human expertise in the legacy programming language or system architecture
Solution Approach 2:
An intermediary analysis system is introduced between the legacy system and the modernization process. This intermediary automatically evaluates code components, assesses their suitability for machine learning replacement, and facilitates the transition by providing structured recommendations that bridge the gap between legacy and modern technologies
2Productivity
If machine learning components are introduced to replace legacy components, then system functionality and efficiency are improved, but system complexity increases and integration challenges arise
Solution Approach 1:
The legacy system is segmented into individual components that can be independently evaluated for machine learning replacement. The system analyzes code at the component level, identifying specific functions or modules that are suitable candidates for replacement, rather than requiring complete system-wide replacement
Solution Approach 2:
Instead of replacing entire legacy systems, the approach applies partial action by selectively replacing only those components that meet specific criteria for machine learning suitability. This gradual, targeted replacement reduces complexity while still delivering efficiency improvements
3Measurement precision
If comprehensive analysis of all system components is performed to identify replacement candidates, then accuracy of replacement recommendations is improved, but time and computational resources required increase
Solution Approach 1:
The analysis focuses on local qualities of specific code components rather than uniformly analyzing the entire system. The system identifies and deeply analyzes only those components that exhibit characteristics suitable for machine learning replacement, such as repetitive patterns, data processing functions, or rule-based logic
Data Source
AI summary
A method, system and computer program product, the method comprising: obtaining computer code of an employed system comprising a plurality of components; obtaining data related to operating the plurality of components; based on the computer code and the data, identifying: a first component from the plurality of components, to be maintained; and a second component from the plurality of components, to be at least partly replaced by a machine learning component; and providing to a user an identification of the first component and the second component.