CODEX: Testing Machine Learning with the Coverage of Data Explorer Tool

Artificial Intelligence (AI) and the subfield machine learning (ML) deployed in systems with direct impact on human lives necessitates assurance of these systems through rigorous test and evaluation (T&E) methodologies such as those applied to testing software systems. Yet, ML differs from other software in important ways, limiting the unmodified adoption of software T&E approaches. A key difference between software systems that are programmed and ML systems is that ML derives logic by learning from data samples. This data dependence makes T&E of the training data an important part of AI testing. Combinatorial testing (CT) is an approach matured within software testing that has been adapted to measure coverage of the data input space. CT-based metrics and methods facilitate measuring the dimensions of a model’s operating envelope, understanding the input space distance between domains for model transfer, identifying the top information gain data points for fine-tuning a model on a new domain, designing test sets that distinguish a model’s performance on learned domains from its generalization to new domains, and measuring imbalance in training data frequency coverage and determining impact on resulting model performance. The Coverage of Data Explorer (CODEX) tool implements these functionalities for the primary purpose of research experimentation and validation of the methods and metrics. This tool paper describes CODEX’s functionalities and maps them to AI T&E applications as well as gives an overview of the workflow of the tool. CODEX is available in a public open-source repository hosted at https://github.com/vtnsi/codex/.