Dynamic Test Resource Allocation via Customer Profiling Analytics
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Solution Overview
Problem
Software testing often fails to identify all issues before a software program is released, leading to suboptimal resource utilization and inefficiencies in test environments, resulting in product quality issues and increased costs.
Innovation Solution
A method and system that perform resource accounting and analysis within organizations, using customer profiling and analytics to determine if test resources are optimal or non-optimal, and adjust resource allocation to improve test coverage and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional software testing methods are used, then testing can be performed with basic resources, but test coverage is insufficient and problems remain undetected
Solution Approach 1:
The system implements feedback by continuously monitoring test execution data, resource utilization metrics, and defect detection rates. This feedback loop enables dynamic adjustment of test resource allocation and identification of coverage gaps, allowing the system to improve test coverage while managing complexity through data-driven decisions rather than ad-hoc adjustments
Solution Approach 2:
The patent replaces manual, mechanical testing processes with an automated intelligent system that uses machine learning algorithms and analytics engines. This substitution transforms the testing system from a manual, complexity-prone process into an automated system that handles resource optimization and coverage analysis algorithmically, reducing operational complexity while improving reliability
2Reliability
If test resources are increased to improve coverage, then more problems can be detected, but resource costs and system complexity increase
Solution Approach 1:
The system changes parameters dynamically by adjusting test resource allocation based on monitored performance metrics and coverage analysis. Instead of statically increasing all resources, the system modifies specific parameters such as test case selection, resource distribution, and execution priorities to achieve optimal defect detection with minimal resource expenditure
Solution Approach 2:
The patent applies partial action by focusing test resources on critical areas identified through analytics rather than uniformly distributing resources across all test cases. The system performs excessive action selectively by running additional tests only in areas where coverage gaps or high-risk defects are detected, avoiding wasteful over-testing in already-validated areas
3Measurement precision
If test environments are expanded to match customer environments, then test accuracy improves, but resource requirements and operational complexity increase
Solution Approach 1:
The system creates simplified copies or representations of customer environments rather than maintaining full-scale replicas.通过使用虚拟化和环境抽象技术，系统能够捕获关键环境特征和配置参数，在测试环境中重现必要的客户环境特性，从而在保持测试准确性的同时显著降低环境管理的复杂性
Solution Approach 2:
The patent implements universal test environments that can adapt to multiple customer scenarios through configuration rather than requiring separate dedicated environments for each customer. The test platform performs multiple functions by dynamically provisioning and configuring environments based on test requirements, reducing overall environmental complexity while maintaining the ability to accurately simulate diverse customer settings
4Reliability
If more test resources are allocated, then test coverage can be expanded, but resource utilization efficiency decreases
Solution Approach 1:
The system transforms static resource allocation into dynamic allocation that automatically adjusts based on real-time test execution needs and coverage analysis. Test resources are dynamically provisioned, allocated, and de-provisioned according to actual demand signals from the test process, ensuring optimal utilization efficiency while maintaining necessary coverage levels
Solution Approach 2:
The patent implements self-service mechanisms where the testing system automatically monitors its own resource utilization, identifies inefficiencies, and reallocates resources without external intervention. The analytics engine continuously evaluates resource usage patterns and autonomously optimizes allocation to maintain high coverage while maximizing utilization efficiency
Data Source
AI summary
Aspects of the present invention include a method, system and computer program product. The method includes a processor performing an accounting of available test resources within one or more organizations; storing data relating to the accounting of available test resources; storing data relating to one or more test resource goals; determining to maintain the data relating to the accounting of available test resources; determining to analyze the data relating to the accounting of available test resources; analyzing the data relating to the accounting of available test resources with respect to the data relating to one or more test resource goals; and determining from the analyzing, by the processor, the data relating to the accounting of available test resources with respect to the data relating to one or more test resource goals that the available test resources within one or more organizations are either optimal or non-optimal.