On Extending the Automatic Test Markup Language (ATML) for Machine Learning
It addresses the problem of standardized testing for ML applications in edge systems like robots and satellites, which is incremental as it adapts an existing standard.
This paper tackles the need for messaging standards in operational test and evaluation of machine learning applications by examining and proposing extensions to the IEEE Standard 1671 (ATML) to handle ML-specific challenges like adversarial robustness and drift detection, concluding that ATML is a promising tool for effective, near real-time testing.
This paper addresses the urgent need for messaging standards in the operational test and evaluation (T&E) of machine learning (ML) applications, particularly in edge ML applications embedded in systems like robots, satellites, and unmanned vehicles. It examines the suitability of the IEEE Standard 1671 (IEEE Std 1671), known as the Automatic Test Markup Language (ATML), an XML-based standard originally developed for electronic systems, for ML application testing. The paper explores extending IEEE Std 1671 to encompass the unique challenges of ML applications, including the use of datasets and dependencies on software. Through modeling various tests such as adversarial robustness and drift detection, this paper offers a framework adaptable to specific applications, suggesting that minor modifications to ATML might suffice to address the novelties of ML. This paper differentiates ATML's focus on testing from other ML standards like Predictive Model Markup Language (PMML) or Open Neural Network Exchange (ONNX), which concentrate on ML model specification. We conclude that ATML is a promising tool for effective, near real-time operational T&E of ML applications, an essential aspect of AI lifecycle management, safety, and governance.