Automating test oracles generation

Software systems play a more and more important role in our everyday life. Many relevant human activities nowadays involve the execution of a piece of software. Software has to be reliable to deliver the expected behavior, and assessing the quality of software is of primary importance to reduce the risk of runtime errors. Software testing is the most common quality assessing technique for software. Testing consists in running the system under test on a finite set of inputs, and checking the correctness of the results. Thoroughly testing a software system is expensive and requires a lot of manual work to define test inputs (stimuli used to trigger different software behaviors) and test oracles (the decision procedures checking the correctness of the results). Researchers have addressed the cost of testing by proposing techniques to automatically generate test inputs. While the generation of test inputs is well supported, there is no way to generate cost-effective test oracles: Existing techniques to produce test oracles are either too expensive to be applied in practice, or produce oracles with limited effectiveness that can only identify blatant failures like system crashes. Our intuition is that cost-effective test oracles can be generated using information produced as a byproduct of the normal development activities. The goal of this thesis is to create test oracles that can detect faults leading to semantic and non-trivial errors, and that are characterized by a reasonable generation cost. We propose two ways to generate test oracles, one derives oracles from the software redundancy and the other from the natural language comments that document the source code of software systems. We present a technique that exploits redundant sequences of method calls encoding the software redundancy to automatically generate test oracles named CCOracles. We describe how CCOracles are automatically generated, deployed, and executed. We prove the effectiveness of CCOracles by measuring their fault-finding effectiveness when combined with both automatically generated and hand-written test inputs. We also present Toradocu, a technique that derives executable specifications from Javadoc comments of Java constructors and methods. From such specifications, Toradocu generates test oracles that are then deployed into existing test suites to assess the outputs of given test inputs. We empirically evaluate Toradocu, showing that Toradocu accurately translates Javadoc comments into procedure specifications. We also show that Toradocu oracles effectively identify semantic faults in the SUT. CCOracles and Toradocu oracles stem from independent information sources and are complementary in the sense that they check different aspects of the system undertest

Thrombopoietin as Biomarker and Mediator of Cardiovascular Damage in Critical Diseases

Thrombopoietin (TPO) is a humoral growth factor originally identified for its ability to stimulate the proliferation and differentiation of megakaryocytes. In addition to its actions on thrombopoiesis, TPO directly modulates the homeostatic potential of mature platelets by influencing their response to several stimuli. In particular, TPO does not induce platelet aggregation per se but is able to enhance platelet aggregation in response to different agonists (“priming effect”). Our research group was actively involved, in the last years, in characterizing the effects of TPO in several human critical diseases. In particular, we found that TPO enhances platelet activation and monocyte-platelet interaction in patients with unstable angina, chronic cigarette smokers, and patients with burn injury and burn injury complicated with sepsis. Moreover, we showed that TPO negatively modulates myocardial contractility by stimulating its receptor c-Mpl on cardiomyocytes and the subsequent production of NO, and it mediates the cardiodepressant activity exerted in vitro by serum of septic shock patients by cooperating with TNF-α and IL-1β. This paper will summarize the most recent results obtained by our research group on the pathogenic role of elevated TPO levels in these diseases and discuss them together with other recently published important studies on this topic

Evaluating the effectiveness of the Emergency Neurological Life Support educational framework in low-income countries.

BackgroundThe Emergency Neurological Life Support (ENLS) is an educational initiative designed to improve the acute management of neurological injuries. However, the applicability of the course in low-income countries in unknown. We evaluated the impact of the course on knowledge, decision-making skills and preparedness to manage neurological emergencies in a resource-limited country.MethodsA prospective cohort study design was implemented for the first ENLS course held in Asia. Knowledge and decision-making skills for neurological emergencies were assessed at baseline, post-course and at 6 months following course completion. To determine perceived knowledge and preparedness, data were collected using surveys administered immediately post-course and 6 months later.ResultsA total of 34 acute care physicians from across Nepal attended the course. Knowledge and decision-making skills significantly improved following the course (p=0.0008). Knowledge and decision-making skills remained significantly improved after 6 months, compared with before the course (p=0.02), with no significant loss of skills immediately following the course to the 6-month follow-up (p=0.16). At 6 months, the willingness to participate in continuing medical education activities remained evident, with 77% (10/13) of participants reporting a change in their clinical practice and decision-making, with the repeated use of ENLS protocols as the main driver of change.ConclusionsUsing the ENLS framework, neurocritical care education can be delivered in low-income countries to improve knowledge uptake, with evidence of knowledge retention up to 6 months

Long-term physical impairments in survivors of COVID-19-associated ARDS compared with classic ARDS: A two-center study

Purpose: This work aimed to compare physical impairment in survivors of classic ARDS compared with COVID-19-associated ARDS (CARDS) survivors. Material and methods: This is a prospective observational cohort study on 248 patients with CARDS and compared them with a historical cohort of 48 patients with classic ARDS. Physical performance was evaluated at 6 and 12 months after ICU discharge, using the Medical Research Council Scale (MRCss), 6-min walk test (6MWT), handgrip dynamometry (HGD), and fatigue severity score (FSS). We also assessed activities of daily living (ADLs) using the Barthel index. Results: At 6 months, patients with classic ARDS had lower HGD (estimated difference [ED]: 11.71 kg, p < 0.001; ED 31.9% of predicted value, p < 0.001), 6MWT distance (ED: 89.11 m, p < 0.001; ED 12.96% of predicted value, p = 0.032), and more frequent significant fatigue (OR 0.35, p = 0.046). At 12 months, patients with classic ARDS had lower HGD (ED: 9.08 kg, p = 0.0014; ED 25.9% of predicted value, p < 0.001) and no difference in terms of 6MWT and fatigue. At 12 months, patients with classic ARDS improved their MRCss (ED 2.50, p = 0.006) and HGD (ED: 4.13 kg, p = 0.002; ED 9.45% of predicted value, p = 0.005), while those with CARDS did not. Most patients in both groups regained independence in ADLs at 6 months. COVID-19 diagnosis was a significant independent predictor of better HGD (p < 0.0001) and 6MWT performance (p = 0.001), and lower prevalence of fatigue (p = 0.018). Conclusions: Both classic ARDS and CARDS survivors experienced long-term impairments in physical functioning, confirming that post-intensive care syndrome remains a major legacy of critical illness. Surprisingly, however, persisting disability was more common in survivors of classic ARDS than in CARDS survivors. In fact, muscle strength measured with HGD was reduced in survivors of classic ARDS compared to CARDS patients at both 6 and 12 months. The 6MWT was reduced and fatigue was more common in classic ARDS compared to CARDS at 6 months but differences were no longer significant at 12 months. Most patients in both groups regained independent function in ADLs at 6 months

A deep learning approach for Spatio-Temporal forecasting of new cases and new hospital admissions of COVID-19 spread in Reggio Emilia, Northern Italy

Since February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM10, PM2.5, NO2, temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic. In this work, we aimed to predict via Deep Learning the real-time transmission of SARS-CoV-2 in the province of Reggio Emilia, Northern Italy, in a grid with a small resolution (12&nbsp;km&nbsp;×&nbsp;12&nbsp;km), including satellite information

Multi-organ point-of-care ultrasound for COVID-19 (PoCUS4COVID): international expert consensus.

COVID-19 has caused great devastation in the past year. Multi-organ point-of-care ultrasound (PoCUS) including lung ultrasound (LUS) and focused cardiac ultrasound (FoCUS) as a clinical adjunct has played a significant role in triaging, diagnosis and medical management of COVID-19 patients. The expert panel from 27 countries and 6 continents with considerable experience of direct application of PoCUS on COVID-19 patients presents evidence-based consensus using GRADE methodology for the quality of evidence and an expedited, modified-Delphi process for the strength of expert consensus. The use of ultrasound is suggested in many clinical situations related to respiratory, cardiovascular and thromboembolic aspects of COVID-19, comparing well with other imaging modalities. The limitations due to insufficient data are highlighted as opportunities for future research.post-print2.282 K