Conformance Testing based on UML State Machines: Automated Test Case Generation, Execution and Evaluation

We describe a comprehensive approach for conformance testing of reactive systems. Based on a formal specification, namely UML state machines, we automatically generate test cases and use them to test the input-output conformance of a system under test. The test cases include not only the stimuli to trigger the system under test, they also include the test oracles to automatically evaluate the test execution. In contrast to Harel Statecharts, state machines behave asynchronously, which makes automatic test case generation a particular challenge. As a prerequisite we have completely formalized a substantial subset of UML state machines that includes complex structured data. The TEAGER tool suite implements our test approach and proves its applicability

An Executable Formal Semantics for a UML State Machine Kernel Considering Complex Structured Data

We present a comprehensive formal semantics for a UML state machine kernel which also considers the use and manipulation of complex structured data. We refer to the UML standard Version 2.1.1 which was published in year 2007. There has been no work that completely integrates complex structured data into a UML state machine semantics. We follow a ”semantics-first” approach (in opposite to a ”complete-notation-first” approach) in which we consider a sound basic kernel of the UML state machine notation, and extend this kernel only ater a thorough investigation of the impacts. We define an operational semantics which is intended to be implemented in a standard programming language. Currently we use such an implementation to automatically generate test cases out of a state machine specification. This document is intended to be adapted if necessary. We will indicate that by the version number given above, whereat the major version number indicates changes of the considered subset and the minor version number indicates adoptions and corrections

Test Case Generation from UML State Machines

In this paper we describe a comprehensive approach for conformance testing of embedded reactive systems. Based on a formal specification, namely UML state machines, we automatically generate test cases and use them to check the functional conformance of a system under test. Our test cases include not only stimuli to trigger the system under test, they also include possible correct observations to automatically evaluate the test case execution. In contrast to classical Harel Statecharts, state machines behave asynchronously, which makes automatic test case generation a challenge. The TEAGER Tool Suite implements the automatic generation, execution and evaluation of test cases and proves the applicability of our test approach

Using UML Protocol State Machines in Conformance Testing of Components

In previous works we designed a comprehensive approach for conformance testing based on UML behavioral state machines. In this paper we propose two extensions to this approach. First, we apply our approach in the context of a component-based development, and address the problem of checking the interoperability of two connected components. Second, we address the problem of selecting relevant input sequences. Therefore we use UML protocol state machines to specify restricted environment models. This means that we restrict the valid protocol at the provided interface of the component under test with respect to a specific test purpose. Based on these models we select relevant input sequences. We implemented both extensions presented here in our TEAGER tool suite to show their applicability. Both extensions address the behavior at the interfaces of components. We use UML state machines as a unified notation for behavioral and protocol conformance testing as well as for test input selection. This considerably eases the work of test engineers

Weakly Supervised Learning for Breast Cancer Prediction on Mammograms in Realistic Settings

Automatic methods for early detection of breast cancer on mammography can significantly decrease mortality. Broad uptake of those methods in hospitals is currently hindered because the methods have too many constraints. They assume annotations available for single images or even regions-of-interest (ROIs), and a fixed number of images per patient. Both assumptions do not hold in a general hospital setting. Relaxing those assumptions results in a weakly supervised learning setting, where labels are available per case, but not for individual images or ROIs. Not all images taken for a patient contain malignant regions and the malignant ROIs cover only a tiny part of an image, whereas most image regions represent benign tissue. In this work, we investigate a two-level multi-instance learning (MIL) approach for case-level breast cancer prediction on two public datasets (1.6k and 5k cases) and an in-house dataset of 21k cases. Observing that breast cancer is usually only present in one side, while images of both breasts are taken as a precaution, we propose a domain-specific MIL pooling variant. We show that two-level MIL can be applied in realistic clinical settings where only case labels, and a variable number of images per patient are available. Data in realistic settings scales with continuous patient intake, while manual annotation efforts do not. Hence, research should focus in particular on unsupervised ROI extraction, in order to improve breast cancer prediction for all patients.Comment: 10 pages, 5 figures, 5 table

C-MAC videolaryngoscope compared with direct laryngoscopy for rapid sequence intubation in an emergency department: A randomised clinical trial

BACKGROUND Airway management in the emergency room can be challenging when patients suffer from life-threatening conditions. Mental stress, ignorance of the patient's medical history, potential cervical injury or immobilisation and the presence of vomit and/or blood may also contribute to a difficult airway. Videolaryngoscopes have been introduced into clinical practice to visualise the airway and ultimately increase the success rate of airway management. OBJECTIVE The aim of this study was to test the hypothesis that the C-MAC videolaryngoscope improves first-attempt intubation success rate compared with direct laryngoscopy in patients undergoing emergency rapid sequence intubation in the emergency room setting. DESIGN A randomised clinical trial. SETTING Emergency Department of the University Hospital, Zurich, Switzerland. PATIENTS With approval of the local ethics committee, we prospectively enrolled 150 patients between 18 and 99 years of age requiring emergency rapid sequence intubation in the emergency room of the University Hospital Zurich. Patients were randomised (1 : 1) to undergo tracheal intubation using the C-MAC videolaryngoscope or by direct laryngoscopy. INTERVENTIONS Owing to ethical considerations, patients who had sustained maxillo-facial trauma, immobilised cervical spine, known difficult airway or ongoing cardiopulmonary resuscitation were excluded from our study. All intubations were performed by one of three very experienced anaesthesia consultants. MAIN OUTCOME MEASURES First-attempt success rate served as our primary outcome parameter. Secondary outcome parameters were time to intubation; total number of intubation attempts; Cormack and Lehane score; inadvertent oesophageal intubation; ease of intubation; complications including violations of the teeth, injury/bleeding of the larynx/pharynx and aspiration/regurgitation of gastric contents; necessity of using further alternative airway devices for successful intubation; maximum decrease of oxygen saturation and technical problems with the device. RESULTS A total of 150 patients were enrolled, but three patients had to be excluded from the analysis, resulting in 74 patients in the C-MAC videolaryngoscopy group and 73 patients in the direct laryngoscopy group. Tracheal intubation was achieved successfully at the first attempt in 73 of 74 patients in the C-MAC group and all patients in the direct laryngoscopy group (P = 1.0). Time to intubation was similar (32 ± 11 vs. 31 ± 9 s, P = 0.51) in both groups. Visualisation of the vocal cords, represented as the Cormack and Lehane score, was significantly better using the C-MAC videolaryngoscope (P < 0.001). CONCLUSION Our study demonstrates that visualisation of the vocal cords was improved by using the C-MAC videolaryngoscope compared with direct laryngoscopy. Better visualisation did not improve first-attempt success rate, which in turn was probably based on the high level of experience of the participating anaesthesia consultants. TRIAL REGISTRATION Clinicaltrials.gov identifier NCT02297113

Staphylococcus lugdunensis Pacemaker-related Infection

We report the first known case of a device-related bloodstream infection involving Staphylococcus lugdunensis small-colony variants. Recurrent pacemaker-related bloodstream infection within a period of 10 months illustrates the poor clinical and microbiologic response even to prolonged antimicrobial drug therapy in a patient infected with this staphylococcal subpopulation