Edge Analytics for Predicting Software Failures in Untested Conditions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex software systems deployed in real-world environments face untested input conditions leading to unknown behavior and potential failures due to developer errors, design flaws, or insufficient requirements, which are difficult to predict and mitigate.
Innovation Solution
Implement edge analytics to monitor and analyze operational systems, learn failure patterns from simulated environments, and apply statistical analysis to identify correlations between input conditions and failures, generating a decision boundary to prevent failures and recommend corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive testing of all input conditions is performed, then software reliability is improved, but testing resources and time become insufficient due to the infinite condition space
Solution Approach 1:
The patent applies preliminary action by training machine learning models on historical failure data and simulated environments before deployment. The system pre-learns failure patterns and creates decision boundaries in advance, enabling it to predict and prevent failures in untested conditions without requiring exhaustive testing of all possible input combinations.
Solution Approach 2:
The patent uses copying by creating simulated environments that replicate production system behavior. These virtual copies generate synthetic failure data that mirrors real-world conditions, allowing the system to learn from simulated failures without requiring extensive physical testing of all possible failure scenarios in the actual system.
2Reliability
If comprehensive testing of all input conditions is performed, then software reliability is improved, but device complexity increases due to the infinite condition space
Solution Approach 1:
The patent replaces mechanical testing systems with a machine learning-based predictive system. Instead of physically testing each input condition through automated test scripts, the system uses trained models that analyze input conditions and predict failures mathematically, substituting computational prediction for mechanical testing execution.
Solution Approach 2:
The patent changes parameters by transforming the testing approach from exhaustive enumeration of input conditions to statistical analysis of failure patterns. The system shifts from testing individual parameter combinations to learning relationships between parameters through machine learning, reducing complexity by working with aggregated patterns rather than individual test cases.
3Adaptability or versatility
If the system operates in untested condition space, then adaptability is improved, but software failures increase due to unknown behavior
Solution Approach 1:
The patent implements feedback by continuously monitoring system behavior in production and comparing actual outcomes against predictions from the machine learning model. When the system encounters untested conditions, the feedback loop allows the model to learn from actual failures and update its decision boundaries, enabling the system to adapt to new conditions while maintaining reliability through continuous learning.
Solution Approach 2:
The patent applies preliminary action by pre-training the machine learning model on simulated failures and historical data before deployment. This preliminary learning phase creates initial decision boundaries that protect against known failure patterns, allowing the system to operate safely in untested conditions from the start while continuous learning further improves adaptability.
Data Source
AI summary
An embodiment of the present invention is directed to a novel approach of predicting software failures while executing in an operational or production environment. The innovative method and system provides analytic capabilities that monitor a system and input conditions and further provides a prediction mechanism to anticipate a software failure and present an improved course of action.