Cognitive Testing System for Dynamic Workload Pattern Analysis
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Solution Overview
Problem
Current product testing methods are inadequate as they often fail to detect and address untested or partially tested features, feature combinations, and deployment configurations, leading to issues that may only manifest in specific usage patterns across customer environments, and do not effectively utilize trends in feature usage for informed product development and testing.
Innovation Solution
A cognitive testing system that analyzes workload characteristics data from customer environments to identify patterns and trends, matches these patterns with existing test buckets, and constructs new test buckets or test cases to ensure comprehensive testing aligned with actual product usage, thereby enabling dynamic and informed testing configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive testing of all product features, combinations, and configurations is performed, then product reliability is improved, but testing time and resources are excessively consumed
Solution Approach 1:
The system performs preliminary analysis of workload characteristics data from customer environments to identify usage patterns before configuring tests. This allows the testing system to proactively prepare targeted test buckets based on predicted usage scenarios, rather than exhaustively testing all possible configurations. The cognitive system analyzes historical data in advance to determine which feature combinations are most likely to be used, enabling efficient test configuration that focuses on high-probability scenarios.
Solution Approach 2:
The system dynamically changes testing parameters based on detected workload patterns. Instead of using fixed test configurations, the cognitive system adjusts test selection, test bucket composition, and testing scope according to the specific usage patterns identified in the customer environment data. This allows the testing process to adapt its parameters (which features to test, which combinations to prioritize) based on actual usage behavior, reducing unnecessary testing while maintaining reliability for critical paths.
2Adaptability or versatility
If manual selection of test cases is performed, then testing coverage is limited to known scenarios, but adaptability to new usage patterns is reduced
Solution Approach 1:
The system implements continuous feedback loops where workload characteristics data from customer environments is collected, analyzed, and used to refine future test configurations. The cognitive system processes usage pattern information and feeds it back into the test bucket configuration process, creating an adaptive cycle that continuously improves testing coverage. This feedback mechanism ensures that emerging usage patterns are automatically detected and incorporated into testing without requiring manual updates to test case selections.
Solution Approach 2:
The testing system performs self-configuration by automatically analyzing workload data and selecting appropriate test cases without manual intervention. The cognitive system autonomously processes usage pattern information, determines which features and combinations require testing, and configures test buckets accordingly. This self-service capability allows the system to adapt to new usage patterns independently, converting usage information into actionable test configurations automatically.
3Manufacturing precision
If exhaustive testing of all feature combinations is performed, then manufacturing precision is improved, but device complexity is increased
Solution Approach 1:
The system segments the testing process into distinct phases: workload data collection, pattern analysis, test selection, and execution. The cognitive system divides the complex task of comprehensive testing into manageable components, analyzing different aspects of usage patterns separately and combining results to form targeted test buckets. This segmentation reduces the apparent complexity by breaking down the exhaustive testing problem into structured, analyzable parts that can be processed systematically.
Solution Approach 2:
The cognitive system acts as an intermediary between raw workload data and test configuration decisions. Rather than directly mapping all possible feature combinations to tests, the cognitive layer processes usage pattern information and translates it into selective test bucket configurations. This intermediary processing layer simplifies the overall system by filtering and prioritizing which combinations require testing, reducing complexity while maintaining precision for critical scenarios.
Data Source
AI summary
In a workload data, a pattern of usage of an aspect of a product is detected in a production system. The pattern is apportioned into a portion. When a test has a characterization that corresponds to the portion within a tolerance, the test is configured in a test bucket. The product is caused to be tested using the test bucket, the test bucket including a set of tests such that the set of tests collectively correspond to the pattern within the tolerance.