Poster: Automatic Consistency Checking of Requirements with ReqV

In the context of Requirements Engineering, checking the consistency of functional requirements is an important and still mostly open problem. In case of requirements written in natural language, the corresponding manual review is time consuming and error prone. On the other hand, automated consistency checking most often requires overburdening formalizations. In this paper we introduce REQV, a tool for formal consistency checking of requirements. The main goal of the tool is to provide an easy-to-use environment for the verification of requirements in Cyber-Physical Systems (CPS). REQV takes as input a set of requirements expressed in a structured natural language, translates them in a formal language and it checks their inner consistency. In case of failure, REQV can also extracts a minimal set of conflicting requirements to help designers in correcting the specification

Consistency of property specification patterns with boolean and constrained numerical signals

Property Specification Patterns (PSPs) have been proposed to solve recurring specification needs, to ease the formalization of requirements, and enable automated verification thereof. In this paper, we extend PSPs by considering Boolean as well as atomic numerical assertions. This extension enables us to reason about functional requirements which would not be captured by basic PSPs. We contribute an encoding from constrained PSPs to LTL formulae, and we show experimental results demonstrating that our approach scales on requirements of realistic size generated using a probabilistic model. Finally, we show that our extension enables us to prove (in)consistency of requirements about an embedded controller for a robotic manipulator

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

SMT-Based Consistency Checking of Configuration-Based Components Specifications

Cyber-Physical Systems (CPSs) are engineered systems that are built from, and depend upon, the seamless integration of computational algorithms and physical components. CPSs are widely used in many safety-critical domains, making it crucial to ensure that they operate safely without causing harm to people and the environment. Therefore, their design should be robust enough to deal with unexpected conditions and flexible to answer to the high scalability and complexity of systems. Nowadays, it is well-established that formal verification has a great potential in reinforcing safety of critical systems, but nevertheless its application in the development of industrial products may still be a challenging activity. In this paper, we describe an approach based on Satisfiability Modulo Theories (SMT) to formally verify, at the design stage, the consistency of the system design – expressed in a given domain-specific language, called QRML, which is specifically designed for CPSs – with respect to some given property constraints, with the purpose to reduce inconsistencies during the system development process. To this end, we propose an SMT-based approach for checking the consistency of configuration based-components specifications and we report the results of the experimental analysis using three different state-of-the-art SMT solvers. The main goal of the experimental analysis is to test the scalability of the selected SMT solvers and thus to determine which SMT solver is the best in checking the satisfiability of the properties

Combining static and runtime methods to achieve safe standing-up for humanoid robots

Due to its complexity, the standing-up task for robots is highly challenging, and often implemented by scripting the strategy that the robot should execute per hand. In this paper we aim at improving the approach of a scripted stand-up strategy by making it more stable and safe. To achieve this aim, we apply both static and runtime methods by integrating reinforcement learning, static analysis and runtime monitoring techniques

Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

BackgroundWe previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15-20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in similar to 80% of cases.MethodsWe report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded.ResultsNo gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5-528.7, P=1.1x10(-4)) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR=3.70[95%CI 1.3-8.2], P=2.1x10(-4)). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR=19.65[95%CI 2.1-2635.4], P=3.4x10(-3)), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR=4.40[9%CI 2.3-8.4], P=7.7x10(-8)). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD]=43.3 [20.3] years) than the other patients (56.0 [17.3] years; P=1.68x10(-5)).ConclusionsRare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc