Search

The papers in this special section focus on software testing and program analysis. Software plays an integral part in our lives today because of its near-ubiquitous influence on our increasingly technological society. Taking appropriate steps to improve software quality is, therefore, of paramount importance. This has fueled the demand for techniques in software testing and program analysis. To meet the demand, there has been a surge in the study and development of techniques such as test case generation, static and dynamic program analysis, and test case prioritization. However, these techniques are still suffering from immaturity and impracticality. Test case generation can hardly be automated; efficiency and state-explosion are still the biggest concerns for most program analysis; and the effectiveness of test case prioritization is still based on small empirical comparisons. Furthermore, the vast majority of empirical studies available on the subject are lab based. Researchers typically evaluate their techniques based on simplified assumptions and selected subject programs that do not necessarily reflect the complexity of large-scale, industrial software. These shortcomings have prevented contemporary software testing and program analysis techniques from yielding truthful benefits to the software in the real world.

Deep neural networks (DNNs) have been widely used in classification tasks. Studies have shown that DNNs may be fooled by artificial examples known as adversaries. A common technique for testing the robustness of a classification is to apply perturbations (such as random noise) to existing examples and try many of them iteratively, but it is very tedious and time-consuming. In this paper, we propose a technique to select adversaries more effectively. We study the vulnerability of examples by exploiting their class distinguishability. In this way, we can evaluate the probability of generating adversaries from each example, and prioritize all the examples accordingly. We have conducted an empirical study using a classic DNN model on four common datasets. The results reveal that the vulnerability of examples has a strong relationship with distinguishability. The effectiveness of our technique is demonstrated through 98.90 to 99.68% improvements in the F-measure.

CUDA is a parallel computing platform and programming model for the graphics processing unit (GPU) of NVIDIA. With CUDA programming, general purpose computing on GPU (GPGPU) is possible. However, the correctness of CUDA programs relies on the correctness of CUDA compilers, which is difficult to test due to its complexity. In this work, we propose CUDAsmith, a fuzzing framework for CUDA compilers. Our tool can randomly generate deterministic and valid CUDA kernel code with several different strategies. Moreover, it adopts random differential testing and EMI testing techniques to solve the test oracle problems of CUDA compiler testing. In particular, we lift live code injection to CUDA compiler testing to help generate EMI variants. Our fuzzing experiments with both the NVCC compiler and the Clang compiler for CUDA have detected thousands of failures, some of which have been confirmed by compiler developers. Finally, the cost-effectiveness of CUDAsmith is also thoroughly evaluated in our fuzzing experiment.

AimThis study aimed to examine the relationships between socio-economic status, health-promoting lifestyles, and quality of life among Chinese nursing students.BackgroundNursing students will be future health promoters, but they may not always adopt the recommended healthy lifestyle. Currently, there are insufficient studies examining the health-promoting lifestyles of Chinese nursing students, and the impact of socio-economic status and health-promoting lifestyle on their health.MethodsThis was a cross-sectional survey. Data were collected from nursing students studying in pre-registration nursing programs of a university in Hong Kong. The survey was conducted through a self-administered questionnaire that solicited information regarding their socio-economic status, health-promoting lifestyle, quality of life, and perceptions of the barriers to adopting a health-promoting lifestyle.FindingsA total of 538 students returned completed questionnaires for analysis. Among the health-promoting lifestyle subscales, the participants performed best in interpersonal relations and worst in physical activity, and the vast majority of them did not actively engage in health-risk behaviors. Hierarchical regression analyses revealed that only 5% of the variance in quality of life was explained by socio-economic variables, whereas a total of 24% of the variance was explained when health-promoting lifestyle variables were added. In particular, health responsibility, physical activity, spiritual growth, and stress management were statistically significant predictors of quality of life.ConclusionsEarly concerns about how prepared nurses are to take on the role of promoting health still apply today. School administrators should plan the nursing curriculum to include activities that encourage student nurses to participate in health-promoting lifestyles. Future studies are needed to explore the barriers that prevent students from practicing health-promoting behavior.