Workload Threshold Alerts for Software Testing
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Solution Overview
Problem
Software testing often fails to identify all issues in a software program before its release, leading to unforeseen problems in customer environments due to a lack of effective workload and operational profiling.
Innovation Solution
A method and system that compare historical workload data with current test data, determining statistical measures and providing alerts when threshold values are not met, enabling real-time remediation and improving test efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional software testing methods are used, then testing can be completed with basic procedures, but software problems remain unidentified in customer environments
Solution Approach 1:
The system performs preliminary workload profiling and threshold determination during the testing phase before software deployment. By pre-establishing workload thresholds and comparing actual customer workload data against these thresholds in advance, the system identifies potential performance issues before they affect customers, thereby improving software reliability without requiring complex post-deployment monitoring systems.
Solution Approach 2:
The testing system segments the software evaluation process into distinct workload profiles representing different customer scenarios. Each workload profile is tested independently with specific threshold criteria, allowing comprehensive coverage of various usage conditions while maintaining manageable test complexity through modular, organized testing sequences.
2Measurement precision
If workload profiling and threshold monitoring are implemented, then software performance can be validated against customer expectations, but testing time and computational resources increase
Solution Approach 1:
The system implements threshold-based monitoring that focuses measurements only on critical workload parameters that exceed predetermined thresholds. Rather than continuously monitoring all workload metrics at full detail, the system applies partial monitoring focused on threshold violations, achieving sufficient measurement precision for quality validation while significantly reducing the time and computational resources required compared to comprehensive continuous monitoring.
3Manufacturing precision
If historical workload data is compared with current test data, then accurate threshold determination can be achieved, but data processing complexity increases
Solution Approach 1:
The system creates simplified copies of historical workload data in standardized formats suitable for comparison with current test data. By transforming raw historical data into structured threshold criteria and using standardized data representations, the system achieves accurate threshold determination through systematic comparison while reducing data processing complexity through consistent data formatting and structured comparison protocols.
Data Source
AI summary
A method obtains from a database historical data values for each of a plurality of workload data points relating to a prior workload run; determines a threshold value for each of the plurality of workload data points relating to the prior workload run; obtains current data values for each of a plurality of workload data points relating to a current workload test run and corresponding to the plurality of workload data points in the historical data values; determines one or more statistical measures relating to the historical data values and the current data values; determines whether the threshold value for at least one of the plurality of data points relating to the prior workload run is not achieved in a set amount of time by the current data value of the same data point relating to the prior workload run; and provides an alert that the threshold value has not been achieved.