Business Process Testing Optimization via Usage Data Correlation
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Solution Overview
Problem
Business processes are often misaligned with their actual criticality, leading to inefficient testing resources and unresolved field issues due to a lack of correlation between perceived and actual criticality, as well as discarded testing data.
Innovation Solution
A system comprising an analyzer component that retrieves and compares testing data with actual deployment data to identify and match business processes, generating reports to optimize testing and correlate field problems with testing situations, utilizing tools like Hewlett Packard Real User Monitor and SiteScope software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If testing resources are allocated based on perceived criticality, then critical processes receive adequate testing, but frequently used non-critical processes are under-tested
Solution Approach 1:
The system performs preliminary analysis of actual usage data before finalizing testing priorities. By examining deployment logs and usage patterns in advance, the system identifies frequently used processes that may have been overlooked, allowing testing resources to be reallocated proactively rather than reactively
Solution Approach 2:
The system implements a feedback loop where actual usage data from deployed systems continuously informs testing priority adjustments. Usage metrics are collected, analyzed, and fed back into the test planning process, creating a dynamic system that adapts testing coverage based on real-world process frequency
2Reliability
If testing is performed on all business processes, then comprehensive coverage is achieved, but resource waste occurs on rarely used processes
Solution Approach 1:
The system applies different testing intensities to different business processes based on their actual usage characteristics. Frequently used processes receive enhanced testing coverage while rarely used processes receive minimal testing, creating a non-uniform testing strategy optimized for each specific process's importance
Solution Approach 2:
The system dynamically adjusts testing parameters such as test depth, test frequency, and resource allocation based on usage data analysis. Processes with high usage frequency trigger increased testing parameters, while low-usage processes automatically receive reduced testing parameters
3Quantity of substance
If testing data is discarded after deployment, then storage resources are freed, but opportunity is lost to correlate field problems with testing situations
Solution Approach 1:
The system introduces an intermediary data retention layer that selectively preserves testing data based on its potential value for problem correlation. Rather than completely discarding or retaining all data, the intermediary layer filters and stores only the subset of testing data that can be meaningfully correlated with field issues
Solution Approach 2:
The retained testing data serves multiple functions: it continues to support regression testing, provides a baseline for performance comparison, and enables correlation analysis with field problems. This multi-functional use of testing data maximizes its value while minimizing storage requirements
Data Source
AI summary
One embodiment provides a system for optimizing business processes comprising an analyzer component configured to retrieve testing data on a business application, wherein the testing data is generated before deployment of the business application. The analyzer component is further configured to retrieve actual data on the business application, wherein the actual data is generated during deployment of the business application. The analyzer component is further configured to analyze the actual data and the testing data to identify at least one business process from the actual data, wherein an identified business process from the actual data is attempted to be matched with a baseline business process defined during testing. The system further comprises a report generator configured to output results from analysis of the actual data and the testing data. Output results may be used to optimize testing and to correlate problems encountered in the field with situations encountered during testing.