Input Space Coverage Assessment for Example-Based Systems
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Solution Overview
Problem
Example-based systems, such as neural networks, face challenges in quality assurance due to their 'black box' nature and the uncertainty of how many examples are needed across the input space for effective training, leading to inefficiencies and potential safety risks in critical applications.
Innovation Solution
A method for quality assurance that assesses the coverage of the input space by distributing examples based on their distribution, allowing for selective data capture and reducing the number of required examples, using representations and encodings that are semantically relevant to the task, and adjusting the density of examples in areas of high complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of examples are collected across the entire input space, then the coverage and reliability of the example-based system is improved, but the costs and time required for example capture increase significantly
Solution Approach 1:
The input space is divided into multiple regions or segments, and examples are selectively captured in specific regions rather than uniformly across the entire space. This segmentation allows focused coverage of critical areas while reducing overall example capture requirements.
Solution Approach 2:
Different regions of the input space are assigned different quality requirements based on their importance to the task. High-stakes regions receive denser example coverage while less critical regions use sparser sampling, optimizing the trade-off between reliability and capture costs.
2Productivity
If examples are collected uniformly across the input space, then the distribution is simple to manage, but the coverage efficiency decreases and more examples are needed
Solution Approach 1:
The input space is pre-analyzed to identify critical regions before example capture begins. This preliminary characterization allows examples to be strategically placed in high-impact areas from the outset, improving capture efficiency without requiring complex adaptive management during the collection process.
3Reliability
If the number of independent input features is increased to improve representation, then the dimensionality and coverage of the state space is improved, but the ratio between features and required examples becomes too large
Solution Approach 1:
Different regions of the high-dimensional input space are treated differently in terms of example density. Critical regions receive focused coverage even in high dimensions, while less important regions use dimensionality reduction or coarser sampling, making the overall system manageable despite high feature dimensionality.
Data Source
AI summary
A quality assurance method for an example-based system improves quality assurance by creating and training the example-based system on the basis of collected examples that form a set of examples. A respective example in the set of examples includes an input value that is situated in an input space. A quality assessment representing a coverage of the input space by examples in the set of examples is ascertained on the basis of a distribution of the input values in the input space. A computer program and a computer-readable storage medium are also provided.