Detecting component changes at run time with behavior models

Modern software systems are composed of several services which may be developed and maintained by third parties and thus they can change independently and without notice during the system’s runtime execution. In such systems, changes may possibly be a threat to system functional correctness, and thus to its reliability. Hence, it is important to detect them as soon as they happen to enable proper reaction. Change detection can be done by monitoring system execution and comparing the observed execution traces against models of the services composing the application. Unfortunately, formal specifications for services are not usually provided and developers have to infer them. In this paper we propose a methodology which exactly addresses these issues by using software behavior models to monitor component execution and detect changes. In particular, we describe a technique to infer behavior model specifications with a dynamic black box approach, keep them up-to-date with run time observations and detect behavior changes. Finally, we present a case study to validate the effectiveness of the approach in component change detection for a component that implements a complex, real communication protocol.European Commission (Programme IDEAS-ERC, Project 227977-SMScom

Holistic recommender systems for software engineering

The knowledge possessed by developers is often not sufficient to overcome a programming problem. Short of talking to teammates, when available, developers often gather additional knowledge from development artifacts (e.g., project documentation), as well as online resources. The web has become an essential component in the modern developer’s daily life, providing a plethora of information from sources like forums, tutorials, Q&A websites, API documentation, and even video tutorials. Recommender Systems for Software Engineering (RSSE) provide developers with assistance to navigate the information space, automatically suggest useful items, and reduce the time required to locate the needed information. Current RSSEs consider development artifacts as containers of homogeneous information in form of pure text. However, text is a means to represent heterogeneous information provided by, for example, natural language, source code, interchange formats (e.g., XML, JSON), and stack traces. Interpreting the information from a pure textual point of view misses the intrinsic heterogeneity of the artifacts, thus leading to a reductionist approach. We propose the concept of Holistic Recommender Systems for Software Engineering (H-RSSE), i.e., RSSEs that go beyond the textual interpretation of the information contained in development artifacts. Our thesis is that modeling and aggregating information in a holistic fashion enables novel and advanced analyses of development artifacts. To validate our thesis we developed a framework to extract, model and analyze information contained in development artifacts in a reusable meta- information model. We show how RSSEs benefit from a meta-information model, since it enables customized and novel analyses built on top of our framework. The information can be thus reinterpreted from an holistic point of view, preserving its multi-dimensionality, and opening the path towards the concept of holistic recommender systems for software engineering

Interaction-aware development environments: recording, mining, and leveraging IDE interactions to analyze and support the development flow

Nowadays, software development is largely carried out using Integrated Development Environments, or IDEs. An IDE is a collection of tools and facilities to support the most diverse software engineering activities, such as writing code, debugging, and program understanding. The fact that they are integrated enables developers to find all the tools needed for the development in the same place. Each activity is composed of many basic events, such as clicking on a menu item in the IDE, opening a new user interface to browse the source code of a method, or adding a new statement in the body of a method. While working, developers generate thousands of these interactions, that we call fine-grained IDE interaction data. We believe this data is a valuable source of information that can be leveraged to enable better analyses and to offer novel support to developers. However, this data is largely neglected by modern IDEs. In this dissertation we propose the concept of "Interaction-Aware Development Environments": IDEs that collect, mine, and leverage the interactions of developers to support and simplify their workflow. We formulate our thesis as follows: Interaction-Aware Development Environments enable novel and in- depth analyses of the behavior of software developers and set the ground to provide developers with effective and actionable support for their activities inside the IDE. For example, by monitoring how developers navigate source code, the IDE could suggest the program entities that are potentially relevant for a particular task. Our research focuses on three main directions: 1. Modeling and Persisting Interaction Data. The first step to make IDEs aware of interaction data is to overcome its ephemeral nature. To do so we have to model this new source of data and to persist it, making it available for further use. 2. Interpreting Interaction Data. One of the biggest challenges of our research is making sense of the millions of interactions generated by developers. We propose several models to interpret this data, for example, by reconstructing high-level development activities from interaction histories or measure the navigation efficiency of developers. 3. Supporting Developers with Interaction Data. Novel IDEs can use the potential of interaction data to support software development. For example, they can identify the UI components that are potentially unnecessary for the future and suggest developers to close them, reducing the visual cluttering of the IDE

Modellistica e progettazione di convertitori elettronici di potenza DC-DC

The present PhD dissertation deals with average modeling, design and experimental verification of power electronic converters. This takes the DC-DC Boost converter as a reference, together with some converter topologies derived from it, such as the interleaved PFC Boost converter. More specifically, in the first part of the dissertation DC-DC converters fundamentals are briefly introduced, i.e. their operating mode, their basic circuit topologies and their parallel and series connections, as well as the basic problems inherent to the design stage of DC-DC converters. Subsequently, this PhD dissertation focuses on the mathematical modeling of the Boost DC-DC converter by means of the averaging technique. In particular, appropriate equivalent switching signals are introduced in order to take into account each converter operating state properly, together with the switch commutation phenomena. In addition, a suitable inductor model is introduced in order to improve inductor losses estimation. As a result, the proposed averaged models are dependent on the switching frequency, still preserving a ripple-free representation of the state variables of the system. The proposed averaged modelling approach enables an enhanced power losses estimation by accounting for switching and current ripple phenomena, over both Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). The worth and effectiveness of the proposed modelling approach has been validated through several simulation studies, which are performed in the Matlab-Simulink and SIMetrix/SIMPLIS environments. The last part of this thesis the Boost PFC converters and new silicon carbide power devices, already available in the market, is provided. In particular, with a constant increase of the switching frequencies and the converters power density, new and most efficient solutions are required, for both circuit topologies and power semiconductors. In this context is presented an extensive experimental analysis of a two-phase Interleaved PFC Boost converter. It aims to highlight the most important features of two-phase interleaved PFC converter operation, in terms of both performances and electromagnetic compatibility issues. This has revealed a low level of harmonic pollution and an excellent result in terms of efficiency at rated load, but also potential conducted EMI issues within low and medium frequency ranges. Efficiencies, switching frequencies and operating temperatures, even in these circuit topologies, are strongly dependent on the power electronics devices used. For this reason it has been dealt an experimental study on the silicon carbide semiconductors. Experimental results are finally reported and discussed; they shown that the reduced power dissipation and the low impact of the parasitic elements, that characterize such semiconductor devices, make these components an interesting solution in the realization of compact and highly efficient energy conversion systems

Inductor losses estimation in DC-DC converters by means of averaging technique

A suitable inductor modeling for power electronic DC-DC converters is presented in this paper. It is developed with the aim of improving inductor losses estimation achievable by averaged models, which inherently neglect inductor current ripple. In order to account for its contribution to the overall inductor losses, an appropriate parallel resistance is thus enclosed into the inductor model, whose value should be chosen in accordance with the DC-DC converter operating conditions. This allows the development of improved averaged models of DC-DC converters, especially in terms of power losses estimation. The effectiveness of the proposed modeling approach has been validated through a simulation study, which refers to the case of a boost DC-DC converter and is performed by means of a suitable circuit simulator designed for rapid modelling of switching power systems (SIMetrix/SIMPLIS)

Using Graph Transformation Systems to Specify and Verify Data Abstractions

This paper proposes an approach for the specification of the behavior of software components that implement data abstractions. By generalizing the approach of behavior models using graph transformation, we provide a concise specification for data abstractions that describes the relationship between the internal state, represented in a canonical form, and the observers of the component. Graph transformation also supports the generation of behavior models that are amenable to verification. To this end, we provide a translation approach into an LTL model on which we can express useful properties that can be model-checked with a SAT solver

Runtime Monitoring of Functional Component Changes with Behavior Models

We consider the problem of run-time discovery and continuous monitoring of new components that live in an open environment. We focus on extracting a formal model—which may not be available— by observing the behavior of the running component. We show how the model built at run time can be enriched through new observations (dy- namic model update). We also use the inferred model to perform run- time verification. That is, we try to identify if any changes are made to the component that modify its original behavior, contradict the previous observations, and invalidate the inferred model

Microsatellites and SNPs linkage analysis in a Sardinian genetic isolate confirms several essential hypertension loci previously identified in different populations

Background. A multiplicity of study designs such as gene candidate analysis, genome wide search (GWS) and, recently, whole genome association studies have been employed for the identification of the genetic components of essential hypertension (EH). Several genome-wide linkage studies of EH and blood pressure-related phenotypes demonstrate that there is no single locus with a major effect while several genomic regions likely to contain EH-susceptibility loci were validated by multiple studies. Methods. We carried out the clinical assessment of the entire adult population in a Sardinian village (Talana) and we analyzed 16 selected families with 62 hypertensive subjects out of 267 individuals. We carried out a double GWS using a set of 902 uniformly spaced microsatellites and a high-density SNPs map on the same group of families. Results. Three loci were identified by both microsatellites and SNP scans and the obtained linkage results showed a remarkable degree of similarity. These loci were identified on chromosome 2q24, 11q23.1–25 and 13q14.11–21.33. Further support to these findings is their broad description present in literature associated to EH or related phenotypes. Bioinformatic investigation of these loci shows several potential EH candidate genes, several of whom already associated to blood pressure regulation pathways. Conclusion. Our search for major susceptibility EH genetic factors evidences that EH in the genetic isolate of Talana is due to the contribution of several genes contained in loci identified and replicated by earlier findings in different human populations

A review of the main genetic factors influencing the course of COVID-19 in Sardinia: the role of human leukocyte antigen-G

Introduction: A large number of risk and protective factors have been identified during the SARS-CoV-2 pandemic which may influence the outcome of COVID-19. Among these, recent studies have explored the role of HLA-G molecules and their immunomodulatory effects in COVID-19, but there are very few reports exploring the genetic basis of these manifestations. The present study aims to investigate how host genetic factors, including HLA-G gene polymorphisms and sHLA-G, can affect SARS-CoV-2 infection. Materials and Methods: We compared the immune-genetic and phenotypic characteristics between COVID-19 patients (n = 381) with varying degrees of severity of the disease and 420 healthy controls from Sardinia (Italy). Results: HLA-G locus analysis showed that the extended haplotype HLA-G*01:01:01:01/UTR-1 was more prevalent in both COVID-19 patients and controls. In particular, this extended haplotype was more common among patients with mild symptoms than those with severe symptoms [22.7% vs 15.7%, OR = 0.634 (95% CI 0.440 – 0.913); P = 0.016]. Furthermore, the most significant HLA-G 3’UTR polymorphism (rs371194629) shows that the HLA-G 3’UTR Del/Del genotype frequency decreases gradually from 27.6% in paucisymptomatic patients to 15.9% in patients with severe symptoms (X2 = 7.095, P = 0.029), reaching the lowest frequency (7.0%) in ICU patients (X2 = 11.257, P = 0.004). However, no significant differences were observed for the soluble HLA-G levels in patients and controls. Finally, we showed that SARS-CoV-2 infection in the Sardinian population is also influenced by other genetic factors such as β-thalassemia trait (rs11549407C>T in the HBB gene), KIR2DS2/HLA-C C1+ group combination and the HLA-B*58:01, C*07:01, DRB1*03:01 haplotype which exert a protective effect [P = 0.005, P = 0.001 and P = 0.026 respectively]. Conversely, the Neanderthal LZTFL1 gene variant (rs35044562A>G) shows a detrimental consequence on the disease course [P = 0.001]. However, by using a logistic regression model, HLA-G 3’UTR Del/Del genotype was independent from the other significant variables [ORM = 0.4 (95% CI 0.2 – 0.7), PM = 6.5 x 10-4]. Conclusion: Our results reveal novel genetic variants which could potentially serve as biomarkers for disease prognosis and treatment, highlighting the importance of considering genetic factors in the management of COVID-19 patients