Security flaws in software, including bugs and vulnerabilities, can cause serious issues, and fuzzing has been extensively studied and employed to identify them. A typical fuzzer automatically generates test inputs and feeds back information obtained from executed programs for efficient software verification. As the feedback information, edge coverage, which is based on edge traversals in a control flow graph, is widely used. We focus on branches whose results change depending on the traversal of a particular edge in the control flow graph. In particular, we found that when a branch depends on a combination of multiple edge traversals, it is difficult for existing edge coverage to cover the branch outcome. Based on this observation, we propose a novel coverage metric for such branches: auxiliary composite-edge coverage (AceCov). AceCov works together with existing edge coverage to provide an auxiliary coverage metric only for branches such as the one described above, where the edge coverage does not cover the result. We implemented AceCov in AFL++ and evaluated it using FuzzBench and MAGMA. The evaluation results show a performance improvement of up to 3.36% and an average improvement of 1.19% over AFL++. We also identified four bugs that AFL++ failed to find.

Background:Confusing source code requires deliberate comprehension. Recent psychology studies have characterized individual decision-making differences as model-free (fast and automatic) and model-based (slow and deliberative) decision-making. A framework has been proposed to estimate an individual differences in the degree of model-based control.Aims:This study investigates the correlation of the degree of model-based control and the comprehension performance of confusing source code and compares the correlation of comprehension performance between the degree of model-based control and developer skills reported in previous studies.Method:We conducted an observational study using source code with and without confusing code patterns. We measured the degree of model-based control for each participant.Results:Multiple regression analysis on the results of 91 software engineers showed that the degree of model-based control has a positive correlation with the percentage of correct answers for questions about source code with confusing code patterns with statistical significance.Conclusion:In source code reviews, refactoring, and enhancement development, the appropriate developer assignment criteria using developer attributes differ between source code with and without confusing code patterns.

Various verification methods have been proposed for bug detection in central processing unit (CPU) designs, yet their effectiveness remains insufficient. We have observed that such CPU bugs often occur in exceptional handling, such as pipeline stalls and flushes. We found that corner cases in such exceptional handling can be effectively verified through situations we term a ‘surge’. A surge refers to a situation where events leading to exceptional handling occur frequently over a short period of time. For instance, a surge caused by frequent queue insertions can eventually fill the capacity, triggering exceptional handling such as a pipeline stall. We propose a novel fuzzing method for CPU designs, named SurgeFuzz, that intentionally generates surges. SurgeFuzz mutates input instruction sequences based on annotations to increase the occurrence of surges. This results in a higher density of event occurrences, thereby enabling efficient verification of corner cases in exceptional handling. We evaluated SurgeFuzz on a large processor design and found several unknown hardware bugs that are difficult to find with existing methods.

OBJECTIVE: For chronic diseases, medical history reconstruction is essential for retrospective database analyses. One important aspect is determining which prescriptions belong to the same episode. However, a standard framework for this task is still lacking, particularly for multitherapy datasets. This paper presents a medication episode construction framework for the medical history of patients with chronic diseases. METHODS: Allen's relaxed temporal relations (i.e., temporal relations with time constraints relaxed by ) is used to define the consecutive prescription relations considering the patients' behavior. For example, patients occasionally arrive earlier or later than their appointment. RESULTS: influences the generation of stable periods (i.e., periods of time, at least three months, in which a medication is continuously taken by a patient). When using the lowest selected value (7 days), considerably fewer shorter stable periods (for durations less than 300 days) are produced and more longer stable periods are produced compared to cases without using . Furthermore, the results show that by using , regarding the number of events, where a stable period continues the previous stable period, decreases and the number of medication transition events available to be observed increases. CONCLUSION: Using in medication episode construction from multitherapy prescription datasets enables the longer expression of short-duration fragmented prescriptions and pruning repetitive prescriptions. SIGNIFICANCE: Our proposed framework is designed for multitherapy datasets, which has not been addressed by previous studies. The concept of relaxes the prescription relation against noise caused by the patient behavior and consequently provides a compact, but informative search space for observing medication transition events in a longitudinal analysis.