Application Modernization Assessment with LLM Code Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for application modernization are time-consuming, error-prone, and inefficient, especially when evaluating multiple applications, due to the complexity of human-readable code and the loss of developer insights, leading to inadequate modernization and resource wastage.
Innovation Solution
A system utilizing machine learning (ML) models, including large language models (LLMs) to interpret human-readable code and generate application modernization information, such as technical requirements and recommendations, with the option of an ML chatbot to emulate developer knowledge, facilitating automated and informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation methods are used to assess application modernization, then developer insights and technical accuracy are improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent creates a virtual copy of the application developer through an AI agent trained on the developer's historical code, comments, and technical knowledge. This virtual developer copy can autonomously evaluate applications without requiring the actual developer's time, thus maintaining technical accuracy while significantly reducing time consumption.
Solution Approach 2:
The patent replaces the mechanical manual evaluation process with an automated AI-based system. The AI agent uses machine learning models to analyze application code, metadata, and technical requirements, substituting human manual assessment with automated computational analysis that achieves similar or superior accuracy.
2Measurement precision
If manual code analysis is performed to understand application characteristics, then comprehension accuracy is improved, but complexity and time required increase
Solution Approach 1:
The patent replaces complex manual code analysis with automated AI-powered analysis tools. The system uses natural language processing and machine learning models to automatically comprehend application code, metadata, and technical requirements, eliminating the need for manual parsing and interpretation while maintaining high comprehension accuracy.
Solution Approach 2:
The patent introduces an AI intermediary layer between the evaluator and the complex application code. This intermediary (AI agent) translates complex code structures into simplified technical requirements and recommendations, making the evaluation process less complex while maintaining accuracy.
3Loss of time
If application developer is unavailable for insights, then time for knowledge transfer is reduced, but quality of modernization assessment deteriorates
Solution Approach 1:
The patent performs preliminary action by training the AI agent on the application developer's historical work, code comments, and technical knowledge before the developer becomes unavailable. This preparation ensures that when the developer leaves or is unavailable, the AI agent can immediately provide accurate assessments without requiring time for knowledge transfer or retraining.
Solution Approach 2:
The patent creates a virtual copy of the application developer's knowledge and expertise through the AI agent. This copy preserves the developer's insights, coding style, and technical judgment, allowing continuous high-quality assessment even when the original developer is unavailable.
4Productivity
If automated ML-based assessment is implemented, then time and cost are reduced, but accuracy and reliability of technical requirements extraction must be maintained
Solution Approach 1:
The patent implements feedback mechanisms where the AI agent iteratively refines its analysis by reviewing application code, comparing it against technical requirements templates, and adjusting its interpretations. This feedback loop ensures high accuracy in technical requirements extraction while maintaining rapid automated evaluation speeds.
Solution Approach 2:
The patent uses advanced machine learning models and natural language processing techniques to replace simple automated parsing with intelligent analysis. This substitution enables the system to understand nuanced technical requirements and code structures accurately while maintaining automation and speed.
Data Source
AI summary
Systems and methods for application modernization using machine learning (ML) are disclosed herein. An example system receives software development information corresponding to one or more applications, the software development information including human-readable code. The system provides the software development information to an ML model. The ML model is trained using application modernization training data corresponding to best practices for modernizing historical applications based upon historical software development information. The ML model includes a large language model trained to interpret the human-readable code. The ML model generates application modernization information corresponding to at least one application of the one or more applications. The application modernization information includes technical requirements of a corresponding application, and application modernization recommendations of the corresponding application based upon the one or more technical requirements. In response to generating the application modernization information, the system provides the application modernization information to a computing device.