Test Portfolio Optimization via Dynamic Cost-Impact Balancing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Regulated enterprises face challenges in efficiently testing adherence to policies, as existing methods lack the capability to optimize test portfolios based on cost and impact, leading to inefficiencies and increased risks due to non-compliance.
Innovation Solution
A test portfolio optimization system comprising a test repository, activity repository, exception repository, and optimization engine that generates test portfolios with varying sample sizes and frequencies, determining costs and impacts to prioritize tests and reduce non-compliance impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test frequency and sample size are increased to improve policy adherence detection, then compliance reliability is improved, but testing cost increases
Solution Approach 1:
The system dynamically adjusts test parameters (frequency and sample size) based on calculated optimization metrics. The optimization engine computes optimal test frequencies and sample sizes that balance compliance detection reliability against testing costs, allowing parameters to change based on enterprise needs and risk profiles rather than using fixed conservative defaults
Solution Approach 2:
The system implements risk-based testing where not all policies receive equal testing intensity. High-risk policies receive more frequent and larger-sample testing while low-risk policies receive reduced testing, avoiding excessive testing of low-priority areas while maintaining adequate coverage across the entire policy portfolio
2Reliability
If comprehensive testing of all policies is conducted to ensure full compliance coverage, then compliance reliability is improved, but test portfolio complexity increases
Solution Approach 1:
The test portfolio is segmented into distinct policy-based test groups rather than treating all tests uniformly. The system divides the comprehensive testing requirement into manageable policy-specific segments, each with its own optimized frequency and sample size, making the overall complex portfolio more controllable and easier to manage through modular organization
Solution Approach 2:
The optimization engine adjusts test portfolio parameters to find the minimum sufficient testing configuration that maintains required compliance coverage. By changing parameters like test frequency and sample size based on risk assessments and historical data, the system reduces unnecessary testing complexity while preserving essential compliance monitoring capabilities
3Productivity
If test frequency and sample size are optimized to reduce costs, then testing efficiency is improved, but compliance detection precision may deteriorate
Solution Approach 1:
The system uses parameter optimization to find the sweet spot where testing frequency and sample size are reduced from maximum levels but maintained at thresholds that still achieve acceptable detection precision. The optimization engine calculates these parameters based on statistical models that predict compliance violation detection rates at different testing intensities
Solution Approach 2:
The system replaces intuitive or conservative manual testing decisions with data-driven optimization algorithms. The optimization engine uses historical compliance data, risk assessments, and statistical models to automatically determine test parameters, substituting mechanical judgment with computational analysis that objectively balances precision and efficiency
Data Source
AI summary
According to one example, a test portfolio optimization system includes a test repository, an activity repository, an exception repository, and an optimization engine. The optimization engine is operable to generate a plurality of test portfolios. Each test portfolio comprises different types of tests of a plurality of tests. The optimization engine is also operable to determine a test cost for each portfolio over the period of time. The test cost for each portfolio is equal to a sum of costs for each test type within the portfolio over the period of time. The optimization engine is also operable to determine an impact per portfolio period of time. The impact per portfolio is equal to a sum of impacts per test type for each test type over the period of time.