Software Validation Framework Using ML Prediction
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Solution Overview
Problem
Current software diagnosis techniques face challenges in developing efficient methods for proper diagnosis of software applications post-deployment, lacking effective validation and real-time risk detection.
Innovation Solution
A software validation framework utilizing machine learning models to predict the time required for validation steps and perform predictive root cause analysis, integrating a microservices architecture for automated validation and reduced manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional software diagnosis techniques are used, then software validation can be performed, but the time required for validation is excessive and efficiency is low
Solution Approach 1:
The system performs preliminary analysis of software configurations and requirements before actual validation execution. Machine learning models pre-process and predict potential issues, preparing validation steps in advance to reduce actual validation time.
Solution Approach 2:
Traditional manual software validation processes are replaced with automated machine learning-based prediction systems. The ML models analyze software configurations and predict validation outcomes, substituting manual mechanical validation processes with intelligent automated systems.
2Reliability
If comprehensive software validation is performed, then diagnostic accuracy improves, but system complexity increases
Solution Approach 1:
The validation system is divided into modular components: configuration analysis modules, machine learning prediction modules, and validation execution modules. Each module handles specific aspects of validation independently, making the complex system manageable and maintainable while achieving comprehensive validation.
Solution Approach 2:
Machine learning models serve as intermediaries between software configurations and validation outcomes. The ML models process complex configuration data and translate it into predictive validation results, simplifying the interaction between comprehensive validation requirements and system implementation.
3Productivity
If manual software validation processes are used, then flexibility is maintained, but productivity decreases
Solution Approach 1:
The validation system performs self-analysis of software configurations using machine learning models. The system automatically predicts validation outcomes and identifies potential issues without requiring manual intervention, enabling the system to serve itself in the validation process while maintaining flexibility through configurable parameters.
Data Source
AI summary
A method comprises receiving a request for validation of software comprising one or more applications, analyzing the request and generating one or more validation steps based at least in part on the analysis. In the method, a time to complete the one or more validation steps is predicted. The predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications.