Configuration Parameter Clustering for Test Scope Optimization
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Solution Overview
Problem
Modern Integrated Development Environments (IDEs) face challenges in providing comprehensive test coverage for software applications due to the vast number of configuration parameters and their interdependencies, leading to high computational costs and inefficient resource management, as well as limited insights into actual operational use, which can result in inadequate simulation of real-world operations and increased difficulty in assessing the impact of changes.
Innovation Solution
A configuration parameter analysis and association system is introduced that utilizes statistical data from production systems to generate parameter clusters, optimizing test coverage by identifying relevant configuration parameters and reducing the need to test all combinations, thereby enhancing resource management and providing insights directly within the development tool.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all possible combinations of configuration parameters are tested, then test coverage is maximized, but computational costs and time consumption increase significantly
Solution Approach 1:
The patent applies partial action by testing only a selected subset of configuration parameter combinations rather than all possible combinations. The system identifies and prioritizes relevant combinations based on actual system usage patterns, performing testing on only the necessary portion (high-frequency usage scenarios) while skipping less critical combinations, thus reducing computational cost while maintaining adequate test coverage.
Solution Approach 2:
The patent uses preliminary action by collecting and analyzing usage data from production systems before determining the test scope. The system pre-identifies which configuration parameter combinations are most relevant based on actual operational frequencies, allowing the test planning phase to be informed by real-world usage patterns before actual testing begins, thereby avoiding wasted computational resources on irrelevant scenarios.
2Productivity
If configuration parameters are clustered based on statistical data, then resource management is optimized, but the complexity of the system increases
Solution Approach 1:
The patent introduces an intermediary component (the configuration parameter analysis system) that sits between the production system and the test execution system. This intermediary collects usage data, performs statistical analysis, clusters configuration parameters, and generates test scope recommendations. By adding this intermediate layer, the system automates the complex analysis and decision-making process, reducing manual effort while managing the complexity through modular system design.
Solution Approach 2:
The system applies self-service by automatically analyzing its own usage data and generating test scope recommendations without requiring manual intervention. The configuration parameter analysis system autonomously collects data from production systems, performs clustering analysis, and identifies relevant test scenarios, enabling the system to self-optimize its testing process based on actual operational patterns.
3Loss of information
If statistical data from production systems is analyzed, then insights into actual operational use are gained, but data collection and processing overhead increases
Solution Approach 1:
The patent applies extraction by selectively collecting only the specific configuration parameter data and usage frequency information needed for test scope determination, rather than gathering all possible operational data. The system extracts only the relevant statistical metrics (parameter combinations, usage frequencies, timestamps) from production systems, minimizing data collection overhead while obtaining sufficient insights to identify high-priority test scenarios.
Data Source
AI summary
Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for test optimization based on actual use of configuration parameters. Actions include receiving a parameter set from a monitoring system, the parameter set including multiple configuration parameters corresponding to development artifacts detected by the monitoring system, retrieving statistical data from a central data analysis infrastructure, the statistical data being retrieved from application systems executing software created out of the development artifacts, processing the parameter set using the statistical data to generate parameter clusters, and providing the parameter clusters to an integrated development environment to generate a test scope proposal based on the parameter clusters.