Using Quality Attribute Scenarios for ML Model Test Case Generation

TL;DR

This paper introduces a QA scenario-based approach for generating comprehensive test cases for ML models, addressing testing gaps beyond performance to improve deployment reliability.

Contribution

It presents a novel QA scenario method integrated into MLTE for systematic testing of ML models and their systems, enhancing early failure detection.

Findings

Effective in identifying failures early in development

Supports testing beyond model performance

Integrated into MLTE tool for practical use

Abstract

Testing of machine learning (ML) models is a known challenge identified by researchers and practitioners alike. Unfortunately, current practice for ML model testing prioritizes testing for model performance, while often neglecting the requirements and constraints of the ML-enabled system that integrates the model. This limited view of testing leads to failures during integration, deployment, and operations, contributing to the difficulties of moving models from development to production. This paper presents an approach based on quality attribute (QA) scenarios to elicit and define system- and model-relevant test cases for ML models. The QA-based approach described in this paper has been integrated into MLTE, a process and tool to support ML model test and evaluation. Feedback from users of MLTE highlights its effectiveness in testing beyond model performance and identifying failures early in the development process.