Software Release Risk Optimization System
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Solution Overview
Problem
Current software release management techniques fail to reliably predict and mitigate risks associated with software product releases, leading to critical defects and increased support costs due to manual and non-systematic risk analysis and prioritization processes.
Innovation Solution
A system and method that gather parameters related to software products, determine complexity levels, assess stability based on baseline software, and calculate overall complexity to quantify release risks, using a complexity quantifier and risk optimization engine to provide stakeholders with risk information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual risk analysis and prioritization is performed by business users, then flexibility in assessment is maintained, but error-prone results and lack of confidence occur due to non-systematic approach
Solution Approach 1:
The patent replaces manual mechanical risk analysis processes with an automated computer-based system that systematically evaluates risks. The system automatically gathers parameters, determines complexity levels, assesses stability, and calculates overall risk, eliminating human error and providing consistent, reliable risk assessments without requiring complex manual procedures.
Solution Approach 2:
The patent transforms the risk analysis process by changing from subjective manual parameters to objective measurable parameters. The system gathers specific parameters (business requirements, components, defects, support requirements) and converts them into quantifiable complexity levels and risk scores, enabling systematic and reliable risk assessment.
2Reliability
If comprehensive parameter gathering and systematic risk analysis is implemented, then risk prediction reliability improves, but system complexity and implementation effort increase
Solution Approach 1:
The patent divides the risk analysis system into distinct functional modules: parameter gathering module, complexity level determination module, stability assessment module, and risk calculation module. Each module handles specific parameters and operations independently, making the overall system manageable and easier to implement while maintaining comprehensive risk analysis capability.
Solution Approach 2:
The system performs preliminary actions by pre-establishing the framework for parameter gathering and complexity level determination before actual risk assessment. The system准备好 the structure and methodology in advance, allowing for systematic and reliable risk prediction without requiring complex ad-hoc analysis during execution.
3Reliability
If impact analysis and risk mitigation are performed before release, then critical defects can be identified and corrected early, but release timeline may be extended
Solution Approach 1:
The patent implements feedback mechanisms where the risk analysis system continuously monitors and provides feedback on risk levels. By identifying high-risk items early and providing feedback to stakeholders, the system enables timely corrections while maintaining efficient release cycles through automated, continuous risk assessment rather than lengthy manual analysis.
Data Source
AI summary
This disclosure relates generally to software release management, and more particularly to a system and method for optimizing risk during a software release. In one embodiment, a method is provided for determining a risk associated with a release of a software product. The method comprises gathering a plurality of parameters related to the software product, determining a plurality of complexity levels based on the plurality of parameters, determining a stability of the software product based on a stability of a baseline software product, determining an overall complexity level of the release of the software product based on the plurality of complexity levels and the stability of the software product, and determining the risk associated with the release of the software product based on the overall complexity level.