ML-Based Software Modernization Assessment Service
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Solution Overview
Problem
Existing software modernization processes are labor-intensive, time-consuming, and prone to errors, often relying on personal experience and requiring repeated assessments for each application, which complicates the modernization of legacy systems to improve performance and security in a cloud environment.
Innovation Solution
A cloud provider network's software modernization assessment service uses machine learning models trained on historical data to automate the identification of suitable modernization strategies and tools for software applications, providing objective and consistent recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual modernization processes are used, then expertise and experience can be applied, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical assessment processes with an automated machine learning-based system. The ML model analyzes application profiles, dependencies, and characteristics to generate modernization recommendations, substituting human expert analysis with automated computational analysis that maintains reliability while dramatically improving productivity.
Solution Approach 2:
The system creates a digital representation (profile) of the software application including its dependencies, characteristics, and relationships. This copied representation is then analyzed by the ML model to generate recommendations, allowing repeated assessments without re-examining the actual application code each time.
2Adaptability or versatility
If repeated assessments are performed for each application, then customized recommendations can be obtained, but the process becomes complex and time-consuming
Solution Approach 1:
The system performs preliminary analysis by creating comprehensive application profiles that capture dependencies, characteristics, and relationships before the actual modernization assessment. The ML model is pre-trained on historical modernization data, enabling it to quickly generate accurate recommendations without requiring repeated full assessments for each application.
Solution Approach 2:
The ML model serves as a universal assessment tool that can evaluate multiple different applications with varying characteristics. By training the model on diverse historical data covering various application types, technologies, and modernization scenarios, it achieves adaptability across different applications while maintaining consistent assessment efficiency.
3Adaptability or versatility
If manual modernization processes are used, then flexibility in handling diverse applications can be maintained, but errors increase and consistency decreases
Solution Approach 1:
The system transforms qualitative expert judgment into quantifiable parameters by analyzing application profiles, dependency graphs, and characteristic metrics. The ML model processes these standardized parameters consistently across all applications, eliminating human variability while maintaining adaptability through the model's ability to handle diverse input types and generate context-appropriate recommendations.
Solution Approach 2:
The system incorporates feedback mechanisms where the ML model learns from historical modernization outcomes and assessment results. This feedback loop continuously improves the model's accuracy and consistency across different application types, ensuring reliable and repeatable recommendations while adapting to new patterns and scenarios.
Data Source
AI summary
Techniques are described for enabling a software modernization assessment service to train and use ML models to automatically generate modernization assessment recommendations for users' software applications and systems. A modernization assessment service collects historical assessment data reflecting past modernization processes and assessments (e.g., application profile information and associated modernization strategies and tools used in past modernization projects). The modernization assessment service uses the historical assessment data to train one or more ML models (e.g., classifiers) that can be used to automatically identify relevant modernization strategies, services, and tools for given software application or system. Responsive to user requests to generate modernization assessment recommendations, the modernization assessment service can use the trained models to automatically generate modernization recommendations and reports.