📝 SummarXiv

Abstract

Testing allows developers to determine whether a system functions as expected. When such systems include deep neural networks (DNNs), Testing becomes challenging, as DNNs approximate functions for which the formalization of functional requirements is intractable. This prevents the application of well-developed approaches to requirements-based testing to DNNs. To address this, we propose a requirements-based testing method (RBT4DNN) that uses natural language requirements statements. These statements use a glossary of terms to define a semantic feature space that can be leveraged for test input generation. RBT4DNN formalizes preconditions of functional requirements as logical combinations of those semantic features. Training data matching these feature combinations can be used to fine-tune a generative model to reliably produce test inputs satisfying the precondition. Executing these tests on a trained DNN enables comparing its output to the expected requirement postcondition behavior. We propose two use cases for RBT4DNN: (1) given requirements defining DNN correctness properties, RBT4DNN comprises a novel approach for detecting faults, and (2) during development, requirements-guided exploration of model behavior can provide developers with feedback on model generalization. Our further evaluation shows that RBT4DNN-generated tests are realistic, diverse, and aligned with requirement preconditions, enabling targeted analysis of model behavior and effective fault detection.