Fault Tree Analytical Model for Deployment Risk Assessment
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Solution Overview
Problem
Conventional deployment failure risk assessments in software product deployment are subjective, unreliable, and not scalable, often conducted retrospectively at later stages, failing to predict issues early enough for effective mitigation.
Innovation Solution
The development of methods and systems for objective deployment failure risk assessments using fault tree analytical models (FTAMs) that predict risks by assigning values and refining them through iterative training with historical and project data, employing novel valuation and evaluation systems to describe relationships between Root Causes, Minor Effects, and Primary Effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If subjective opinion-based DF risk assessment is used, then individual experience can be applied, but the assessment is unreliable and not scalable
Solution Approach 1:
The patent replaces the subjective human judgment mechanism with an automated computational model (FTAM). The system uses objective data processing and algorithmic evaluation to assess deployment failure risks, eliminating reliance on individual expertise while enabling scalable application across multiple projects simultaneously.
Solution Approach 2:
The FTAM system performs self-evaluation by automatically processing project data, historical DF information, and risk factors through predefined analytical models. The system generates risk assessments autonomously without requiring continuous human intervention, making the process both reliable and scalable.
2Measurement precision
If conventional retrospective DF risk assessment is conducted, then accurate assessment can be achieved at later stages, but mitigation opportunities are missed
Solution Approach 1:
The FTAM model performs preliminary risk assessments continuously throughout the development lifecycle, evaluating deployment failure risks before they materialize. By analyzing project characteristics, historical data, and risk factors in real-time, the system enables proactive mitigation actions at optimal points in the development process.
Solution Approach 2:
The system incorporates continuous feedback loops where risk assessment results are fed back into the development process. The FTAM model is iteratively refined using historical DF data and project outcomes, improving prediction accuracy over time while enabling ongoing risk monitoring and mitigation throughout the project lifecycle.
3Adaptability or versatility
If multiple subjective analyses are undertaken for different projects, then individualized assessment can be performed, but different expertise is required for each project
Solution Approach 1:
The FTAM system serves as a universal platform that can assess deployment failure risks across diverse software projects simultaneously. The model adapts to different project types, sizes, and complexities using the same core analytical framework, eliminating the need for different expertise levels while maintaining individualized assessment capability through data-driven analysis.
Data Source
AI summary
Systems and methods for objective Deployment Failure risk assessments are provided, which may include fault trees. Systems and methods for the analysis of fault trees are provided as well. The risk assessments system may involve the development of a fault tree, assigning initial values and weights to the events within that fault tree, and the subsequent revision of those values and weights in an iterative fashion, including comparison to historical data. The systems for analysis may involve the assignment of well-ordered values to some events in a fault tree, and then the combination those values through the application of specialized, defined gates. The system may further involve the revision of specific gates by comparison to historical or empirical data.