ML Test Case Generation for Low-Coverage Feature Spaces
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Solution Overview
Problem
Evaluating and monitoring machine learning models in software systems is resource-consuming and prone to errors due to the complexity of evaluating their behavior as a function of inputs, particularly when covering a wide range of datasets and learning types.
Innovation Solution
A method for generating tests of a machine learning engine that automatically identifies areas of low coverage in a feature space defined by configuration parameters, creating new test cases to cover these areas, and providing them for testing to find suitable machine learning models, using a computer system and program product to facilitate this process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional evaluation methods are used to monitor machine learning model behavior, then model performance can be assessed, but the process becomes resource-consuming and error-prone
Solution Approach 1:
The patent applies preliminary action by generating test cases in advance that cover diverse configurations of dataset characteristics and learning parameters. These pre-generated test cases are stored and can be reused for evaluating different machine learning models, eliminating the need for resource-intensive real-time evaluation while maintaining reliability.
Solution Approach 2:
The patent uses parameter changes by systematically varying configuration parameters such as dataset size, number of features, learning rate, and other hyperparameters to create a comprehensive test suite. This allows evaluation of model behavior across different parameter spaces without re-running expensive training experiments for each parameter combination.
2Adaptability or versatility
If comprehensive testing of machine learning models is performed across wide ranges of datasets and learning types, then evaluation coverage is improved, but complexity and resource consumption increase
Solution Approach 1:
The patent implements universality by creating a unified test case framework that can evaluate multiple types of machine learning models (classification, regression, clustering) across various dataset characteristics. The same test case generation mechanism serves multiple evaluation purposes, reducing overall system complexity while maintaining broad adaptability.
3Ease of operation
If manual test case generation is used for machine learning model evaluation, then flexibility is maintained, but human intervention increases error-proneness and time consumption
Solution Approach 1:
The patent applies self-service by implementing an automated test case generation system that autonomously creates, validates, and manages test cases without human intervention. The system automatically selects configuration parameters, generates appropriate test datasets, and produces test cases tailored to specific machine learning models, eliminating manual effort while maintaining flexibility through configurable parameters.
Data Source
AI summary
A method for generating tests of a machine learning (ML) engine includes determining a feature space of the set of configuration parameters of at least part of initial test cases of the ML engine. One or more areas of low coverage may be identified in the feature space. A configuration of a new test case may be defined such that the new test case covers a low coverage area of the feature space. The new test case may be provided for testing a performance of the ML engine to find a ML model using an input dataset according to the configuration of the new test case.