Concurrency bugs are notoriously difficult to detect because there can be vast combinations of interleavings among concurrent threads, yet only a small fraction can reveal them. Atomic-set serializability characterizes a wide range of concurrency bugs, including data races and atomicity violations. In this paper, we propose a two-phase testing technique that can effectively detect atomic-set serializability violations. In Phase I, our technique infers potential violations that do not appear in a concrete execution and prunes those interleavings that are violation-free. In Phase II, our technique actively controls a thread scheduler to enumerate these potential scenarios identified in Phase I to look for real violations. We have implemented our technique as a prototype system ASSETFUZZER and applied it to a number of subject programs for evaluating concurrency defect analysis techniques. The experimental results show that ASSETFUZZER can identify more concurrency bugs than two recent testing tools RACEFUZZER and ATOMFUZZER.

NesC is a programming language for applications that run on top of networked sensor nodes. Such an application mainly uses an interrupt to trigger a sequence of operations, known as contexts, to perform its actions. However, a high degree of inter-context interleaving in an application can cause it to be error-prone. For instance, a context may mistakenly alter another context's data kept at a shared variable. Existing concurrency testing techniques target testing programs written in general-purpose programming languages, where a small scale of inter-context interleaving between program executions may make these techniques inapplicable. We observe that nesC blocks new context interleaving when handling interrupts, and this feature significantly restricts the scale of inter-context interleaving that may occur in a nesC application. This paper models how operations on different contexts may interleave as inter-context flow graphs. Based on these graphs, it proposes two test adequacy criteria, one on inter-context data-flows and another on intercontext control-flows. It evaluates the proposal by a real-life open-source nesC application. The empirical results show that the new criteria detect significantly more failures than their conventional counterparts.

Purpose: To investigate the quality of discharge teaching, readiness for hospital discharge (RHD), and post-discharge outcomes (PDO) of cataract patients in a day ward and to explore the relationships among these three variables. Methods: This cross-sectional study used an opportunistic sample from the ophthalmic day ward in a general hospital in Sichuan province, China. Data were collected using four questionnaires. Results: The total average score on the Quality of Discharge Teaching Scale was 192.95, and the dimension with the lowest score was “guidance obtained practically.” The total average score on the Readiness for Hospital Discharge Scale was 175.51, and the dimension with the lowest score was “knowledge of disease.” The total average score on the Post-Discharge Outcome Questionnaire was 77.08, and the four dimensions with the lowest scores were “compliance behaviors,” “avoiding excessive use of eye,” “avoiding strenuous exercise,” and “regular check-up.” Pearson correlation coefficients indicated low to moderate correlations between discharge teaching quality and PDO (0.245, P < 0.01), RHD and PDO (0.271, P < 0.01), and discharge teaching quality and PDO (0.559, P < 0.01). Conclusion: The quality of discharge teaching among cataract patients who underwent day surgery was relatively high, and patient preparation for discharge and PDO were good. However, medical staff should focus more attention on patients’ individualized needs for discharge teaching while emphasizing the importance of compliance behavior.