Recently, with the widespread use of deep neural networks (DNNs) in high-stakes decision-making systems (such as fraud detection and prison sentencing), concerns have arisen about the fairness of DNNs in terms of the potential negative impact they may have on individuals and society. Therefore, fairness testing has become an important research topic in DNN testing. At the same time, the neural network coverage criteria (such as criteria based on neuronal activation) is considered as an adequacy test for DNN white-box testing. It is implicitly assumed that improving the coverage can enhance the quality of test suites. Nevertheless, the correlation between DNN fairness (a test property) and coverage criteria (a test method) has not been adequately explored. To address this issue, we conducted a systematic empirical study on seven coverage criteria, six fairness metrics, three fairness testing techniques, and five bias mitigation methods on five DNN models and nine fairness datasets to assess the correlation between coverage criteria and DNN fairness. Our study achieved the following findings: 1) with the increase in the size of the test suite, some of the coverage and fairness metrics changed significantly, as the size of the test suite increased; 2) the statistical correlation between coverage criteria and DNN fairness is limited; and 3) after bias mitigation for improving the fairness of DNN, the change pattern in coverage criteria is different; 4) Models debiased by different bias mitigation methods have a lower correlation between coverage and fairness compared to the original models. Our findings cast doubt on the validity of coverage criteria concerning DNN fairness (i.e., increasing the coverage may even have a negative impact on the fairness of DNNs). Therefore, we warn DNN testers against blindly pursuing higher coverage of coverage criteria at the cost of test properties of DNNs (such as fairness).

With the applications of intelligent systems in areas (such as self-driving cars, robotics, and smart cities), the impact of these intelligent systems’ defects cannot be ignored. For example, in a recent report, the self-driving car collided with another self-driving car because it incorrectly identified a roadblock. Therefore, it is necessary to conduct adequate testing of intelligent systems to avoid dangerous behaviors as much as possible. However, due to the particularity of its own structure, the low efficiency, and the high cost of manual collection the large-scale test cases, it is important and challenging to design tools to test the adequacy of intelligent systems.To overcome the above problems, we propose an intelligent system test adequacy evaluation tool ISTA. ISTA implements the automatic generation and optimization of test cases based on coverage analysis, which can improve the test adequacy of the intelligent system while expanding the dataset. To evaluate the usefulness of our developed tool, we analyze the application of ISTA on the five-layer fully-connected dnn model and german credit dataset (text data type) for binary classification as well as on the Rambo model and hmb dataset (image data type) for self-driving car. The evaluation results show that the test dataset is expanded and the models are more fully tested after ISTA’s test case generation and optimization for both text and image data types, with a corresponding increase in the average 80% coverage criteria used.