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Sinapic acid, 3,5-dimethoxyl-4-hydroxycinnamic acid, belonging to the class of hydroxycinnamic acids, shows antioxidant, anti-inflammatory, anticancer, hepatoprotective, cardioprotective, renoprotective, neuroprotective, antidiabetic, anxiolytic, and antibacterial activity. The aim of this work was to incorporate sinapic acid into solid lipid nanoparticles in order to improve its bioavailability. SLNs were prepared using the hot high-speed homogenization method. The pharmaco-technological properties and thermotropic profile of SLNs encapsulated with sinapic acid, as well as their interaction with biomembrane models, were evaluated. SLNs showed promising physicochemical properties and encapsulation efficiency, as well as a desirable release profile; moreover, they facilitated the interaction of sinapic acid with a biomembrane model made of multilamellar vesicles. In conclusion, this formulation can be used in further studies to assess its suitability to improve sinapic acid activity.

In this work, a blend of PEO, polysulfone (PSF), and lithium bis(trifluoromethanesulfonyl)imide (LiTFSi) was prepared at different PEO–PSf weight ratios (70-30, 80-20, and 90-10) and ethylene oxide to lithium (EO/Li) ratios (16/1, 20/1, 30/1, and 50/1). The samples were characterised using FT-IR, DSC, and XRD. Young’s modulus and tensile strength were evaluated at room temperature with micro-tensile testing. The ionic conductivity was measured between 5 °C and 45 °C through electrochemical impedance spectroscopy (EIS). The samples with a ratio of PEO and PSf equal to 70-30 and EO/Li ratio equal to 16/1 have the highest conductivity (1.91 × 10−4 S/cm) at 25 °C, while the PEO–PSf 80-20 EO/Li = 50/1 have the highest averaged Young’s modulus of about 1.5 GPa at 25 °C. The configuration with a good balance between electrical and mechanical properties is the PEO–PSf 70-30 EO/Li = 30/1, which has a conductivity of 1.17 × 10−4 S/cm and a Young’s modulus of 800 MPa, both measured at 25 °C. It was also found that increasing the EO/Li ratio to 16/1 dramatically affects the mechanical properties of the samples with them showing extreme embrittlement.

Solid lipid nanoparticles (SLNs) are lipid-based colloidal systems used for the delivery of active compounds. Although SLNs have many benefits, they show important issues due to physical and chemical instability phenomena during storage. For these reasons, it is highly desirable to have a dried SLN formulation available. Therefore, the aim of the project was to identify suitable methods to obtain a dry powder formulation from an SLN suspension. The nanoparticle suspension was dried using both freeze- and spray-drying techniques. The suitability of these methods in obtaining SLN dry powders was evaluated from the analyses of nanotechnological parameters, system morphology and thermal behavior using differential scanning calorimetry. Results pointed out that both drying techniques, although at different yields, were able to produce an SLN dry powder suitable for pharmaceutical applications. Noteworthily, the freeze-drying of SLNs under optimized conditions led to a dry powder endowed with good reconstitution properties and technological parameters similar to the starting conditions. Moreover, freeze–thaw cycles were carried out as a pretest to study the protective effect of different cryoprotectants (e.g., glucose and mannitol with a concentration ranging from 1% to 10% w/v). Glucose proved to be the most effective in preventing particle growth during freezing, thawing, and freeze-drying processes; in particular, the optimum concentration of glucose was 1% w/v.

Colorectal cancer is one of the most diffused tumoral diseases. Since most medicaments employed for its treatment are debilitating, the use of naturally derived products, which can be effective against the mutated cells and, in addition, can reduce most inflammatory-related effects, could be extremely beneficial for the continued treatment of this disease. In this research, ethyl protocatechuate (PCAEE), a protocatechuic acid prodrug, was encapsulated in solid lipid nanoparticles (SLN) (prepared without and with Tween 80), which were characterized in terms of size, polydispersity index (PDI), zeta potential and thermotropic behavior. Encapsulation efficiency, release profile and interaction with a model of biomembrane were also assessed. The nanoparticles were tested in vitro on both healthy cells and on a model of tumoral cells. SLN prepared with Tween 80 was promising in terms of physicochemical properties (z-average of 190 nm, PDI 0.150 and zeta potential around −20 mV) and encapsulation efficiency (56%); they showed a desirable release profile, demonstrated an ability to penetrate and release the encapsulated PCAEE into a biomembrane model and were nontoxic on healthy cells. In addition, they caused a greater dose-dependent decrease in the viability of CaCo-2 cells than PCAEE alone. In conclusion, the formulation could be proposed for further studies to assess its suitability for the treatment of colorectal cancer.

In the railway domain, rolling stock maintenance affects service operation time and efficiency. Minimizing train unavailability is essential for reducing capital loss and operational costs. To this aim, prediction of failures of rolling stock equipment is crucial to proactively trigger proper maintenance activities. Indeed, predictive maintenance is a golden example of the digital transformation within Industry 4.0, which affects several engineering processes in the railway domain. Nowadays, it may leverage artificial intelligence and machine learning algorithms to forecast failures and schedule the optimal time for maintenance actions. Generally, rail systems deteriorate gradually over time or fail directly, leading to data that vary extremely slowly. Indeed, ML approaches for predictive maintenance should consider this type of data to accurately predict and forecast failures. This paper proposes a methodology based on Long Short-Term Memory deep learning algorithms for predictive maintenance of railway rolling stock equipment. The methodology allows us to properly learn long-term dependencies for gradually changing data, and both predicting and forecasting failures of rail equipment. In the framework of an academic-industrial partnership, the methodology is experimented on a train traction converter cooling system, demonstrating its applicability and benefits. The results show that it outperforms state-of-the-art methods, reaching a failure prediction and forecasting accuracy over 99%, with a false alarm rate of ∼ 0.4% and a mean absolute error in the order of 10−4, respectively.

Investigation of the interaction between drugs and biomembrane models, as a strategy to study and eventually improve drug/substrate interactions, is a crucial factor in preliminary screening. Synthesized peptides represent a source of potential anticancer and theragnostic drugs. In this study, we investigated the interaction of a novel synthesized peptide, called RH-23, with a simplified dimyristoylphosphatidylcholine (DMPC) model of the cellular membrane. The interaction of RH-23 with DMPC, organized either in multilamellar vesicles (MLVs) and in Langmuir-Blodgett (LB) monolayers, was assessed using thermodynamic techniques, namely differential scanning calorimetry (DSC) and LB. The calorimetric evaluations showed that RH-23 inserted into MLVs, causing a stabilization of the phospholipid gel phase that increased with the molar fraction of RH-23. Interplay with LB monolayers revealed that RH-23 interacted with DMPC molecules. This work represents the first experimental thermodynamic study on the interaction between RH-23 and a simplified model of the lipid membrane, thus providing a basis for further evaluations of the effect of RH-23 on biological membranes and its therapeutic/diagnostic potential.

Skin is the first human barrier that is daily exposed to a broad spectrum of physical and chemical agents, which can increase reactive oxygen species (ROS) and lead to the formation of topical disorders. Antioxidant molecules, such as benzo[k,l]xanthene lignans (BXL), are ideal candidates to eliminate or minimize the effects of ROS. Herein, we aimed to formulate BXL-loaded solid lipid nanoparticles (SLN-BXL) to improve the bioavailability and interaction with the skin, and also to investigate the protective impact against intracellular ROS generation in HFF-1 in comparison with the drug-free situation. SLN-BXL were formulated using the PIT/ultrasonication method, and then were subjected to physicochemical characterizations, i.e., average size, zeta potential (ZP), polydispersity index (PDI), encapsulation efficiency (%EE), thermotropic behavior, and interaction with a biomembrane model. The results show a mean size around 200 nm, PDI of 0.2, and zeta potential of about −28 mV, with values almost unchanged over a period of three months, while the EE% is ≈70%. Moreover, SLN-BXL are able to deeply interact with the biomembrane model, and to achieve a double-action release in mildly hydrophobic matrices; the results of the in vitro experiments confirm that SLN-BXL are cell-safe and capable of attenuating the IL-2-induced high ROS levels. In conclusion, based on our findings, the formulation can be proposed as a candidate for a preventive remedy against skin disorders induced by increased levels of ROS.

We report a series of calculations to elucidate one possible mechanism of SO

Testing of microservices architectures (MSA) – today a popular software architectural style - demands for automation in its several tasks, like tests generation, prioritization and execution. Automated black-box generation of test cases for MSA currently borrows techniques and tools from the testing of RESTful Web Services. This paper: i) proposes the uTest stateless pairwise combinatorial technique (and its automation tool) for test cases generation for functional and robustness microservices testing, and ii) experimentally compares - with three open-source MSA used as subjects - four state-of-the-art black-box tools conceived for Web Services, adopting evolutionary-, dependencies- and mutation-based generation techniques, and the pro- posed uTest combinatorial tool. The comparison shows little differences in coverage values; uTest pairwise testing achieves better average failure rate with a considerably lower number of tests. Web Services tools do not perform for MSA as well as a tester might expect, highlighting the need for MSA-specific techniques.

We report a series of calculations to elucidate one possible mechanism of SO2 chemisorption in amino acid-based ionic liquids. Such systems have been successfully exploited as CO2 absorbents and, since SO2 is also a by-product of fossil fuels’ combustion, their ability in capturing SO2 has been assessed by recent experiments. This work is exclusively focused on evaluating the efficiency of the chemical trapping of SO2 by analyzing its reaction with the amino group of the amino acid. We have found that, overall, SO2 is less reactive than CO2, and that the specific amino acid side chain (either acid or basic) does not play a relevant role. We noticed that bimolecular absorption processes are quite unlikely to take place, a notable difference with CO2. The barriers along the reaction paths are found to be non-negligible, around 7–11 kcal/mol, and the thermodynamic of the reaction appears, from our models, unfavorable.

Software aging -- the phenomenon affecting many long-running systems, causing performance degradation or an increasing failure rate over mission time, and eventually leading to failure - is known to affect mobile devices and their operating systems, too. Software rejuvenation -- the technique typically used to counteract aging -- may compromise the user's perception of availability and reliability of the personal device, if applied at a coarse grain, e.g., by restarting applications or, worse, rebooting the entire device. This article proposes a configurable micro-rejuvenation technique to counteract software aging in Android-based mobile devices, acting at a fine-grained level, namely on in-memory system data structures. The technique is engineered in two phases. Before releasing the (customized) Android version, a heap profiling facility is used by the manufacturer's developers to identify potentially bloating data structures in Android services and to instrument their code. After release, an aging detection and rejuvenation service will safely clean up the bloating data structures, with a negligible impact on user perception and device availability, as neither the device nor operating system's processes are restarted. The results of experiments show the ability of the technique to provide significant gains in aging mobile operating system responsiveness and time to failure., Accepted for publication in Elsevier Journal of Systems and Software

Cell membrane models are useful for obtaining molecular-level information on the interaction of biologically active molecules whose activity is believed to depend also on their effects on the membrane. Cytarabine was conjugated with fatty acids to improve the drug lipophilicity and the interaction with the biomembrane model. Cytarabine was conjugated with fatty acids of different lengths to form the trimyristoyl cytarabine and the tristearoyl cytarabine derivatives. Their interaction with biomembrane models constituted by dimyristoylphosphatidylcholine (DMPC) monolayers was studied by employing the Langmuir–Blodgett technique. DMPC/cytarabine, DMPC/trimyristoyl cytarabine and DMPC/tristearoyl cytarabine mixed monolayers at increasing molar fractions of the compound were prepared and placed on the subphase. The mean molecular area/surface pressure isotherms were recorded at 37 °C. Between the molecules of DMPC and those of cytarabine or prodrugs, repulsive forces act. However, these forces are very weak between DMPC and cytarabine and stronger between DMPC and the cytarabine derivatives, thus avoiding the expulsion of the compounds at higher surface pressure and modifying the stability of the mixed monolayer. The fatty acid moieties could then modulate the affinity of cytarabine for biomembranes.

Deep Neural Networks (DNN) are typically tested for accuracy relying on a set of unlabelled real world data (operational dataset), from which a subset is selected, manually labelled and used as test suite. This subset is required to be small (due to manual labelling cost) yet to faithfully represent the operational context, with the resulting test suite containing roughly the same proportion of examples causing misprediction (i.e., failing test cases) as the operational dataset. However, while testing to estimate accuracy, it is desirable to also learn as much as possible from the failing tests in the operational dataset, since they inform about possible bugs of the DNN. A smart sampling strategy may allow to intentionally include in the test suite many examples causing misprediction, thus providing this way more valuable inputs for DNN improvement while preserving the ability to get trustworthy unbiased estimates. This paper presents a test selection technique (DeepEST) that actively looks for failing test cases in the operational dataset of a DNN, with the goal of assessing the DNN expected accuracy by a small and ''informative'' test suite (namely with a high number of mispredictions) for subsequent DNN improvement. Experiments with five subjects, combining four DNN models and three datasets, are described. The results show that DeepEST provides DNN accuracy estimates with precision close to (and often better than) those of existing sampling-based DNN testing techniques, while detecting from 5 to 30 times more mispredictions, with the same test suite size., Comment: Paper accept at 43rd ACM/IEEE International Conference on Software Engineering, Madrid, Spain. May 2021

The Integration and Verification Testing and characterization of the expected performance of the Large Synoptic Survey Telescope (LSST) Camera is described. The LSST Camera will be the largest astronomical camera ever constructed, featuring a 3.2 Gpixel focal plane mosaic of 189 CCDs. In this paper, we describe the verification testing program developed in parallel with the integration of the Camera, and the results from our performance characterization of the Camera. Our testing program includes electro-optical characterization and CCD height measurements of the focal plane, at several steps during integration, as well as a complete functional and characterization program for the finished focal plane. It also includes a suite of functional tests of the major Camera mechanisms: shutter, filter exchange system and thermal control. Finally, we expect to test the fully assembled Camera prior to its scheduled completion and delivery to the LSST observatory in early calendar 2021.

Event stream processing has recently emerged as a popular paradigm for implementing high-volume distributed (near-)real time data processing applications. Several open source systems are today available, supporting the development of such applications, many of which developed with the technologies of the Apache Software Foundation. These so called stream processors are long-running complex software systems which may be affected by software aging, a well-known phenomenon among operation engineers, consisting of a progressive increase in the failure rate or in performance degradation of a software system over time. We address the problem of identifying symptoms and sources of software aging in the Apache Storm event stream processing system; this helps to identify proper strategies to prevent or mitigate anomalous behaviors in production environments. To this aim, we present an experimental study investigating aging manifestations in a popular system, namely Apache Storm. Results show that Storm presents anomalous behaviors in long runs, which prevent some topologies from working continuously. These can be attributed to software aging, due to Storm internal resource management mechanisms influenced by the garbage collector and the memory assigned to worker processes. We discuss the aging-related Apache Storm behaviors, and we experiment rejuvenation actions, showing that they are actually able to remove them.

Cloud computing is a key supporting technology driving the fourth industrial revolution, spanning from the Internet of Things (IoT) and Cloud for Telecoms (C4T) to Industry 4.0 to Smart Cities, and so on. This pervasiveness of cloud technology is due to the ability to easily share and obtain resources on a pay-per-use and elastic provisioning model. Several enterprises use the cloud as a cheap solution to the achieve computational and storage capabilities they need without incurring the costs associated with owning and maintaining data centers. This implies that they rely on the correctness of the services provided by the cloud platforms and that any possible outage could result in considerable loss of reputation and money, which could effectively bring actions against the cloud service provider for violations to the agreed upon Service Level Agreement (SLA).

Nanosized clusters of protic ionic liquids are investigated using ab-initio DFT methods to understand their propensity to remain ionic. Protic ionic liquids are based on acid/base pairs and are ionic because of proton transfer from the acid to the base. Two prototypical ionic liquids based on triethylamine and two acids of different strengths are used here to trace the tendency toward proton transfer when the system size is gradually increased from an isolated pair of molecules to cluster of several pairs. We have found that the compounds based on the weak acid shows the existence of a well-defined minimal size that represents the threshold for the onset of ionization phenomena.

The adoption of cloud computing for providing resource and delivering services is an irreversible trend. For most IT companies, the quality of provided services is strongly dependent on reliability and performance of the underlying cloud technologies. A widely studied problem that can greatly affect the user experience is runtime software aging, whose main countermeasure is a proactive maintenance action known as rejuvenation. This article reviews the effort conducted so far by the software aging and rejuvenation (SAR) community in the cloud domain. A set of 105 papers has been examined from three source digital libraries in order to have a clear view of the state of the art. The paper characterizes the cloud-related SAR literature according to four dimensions: the publication trends, the aging analysis methods and metrics, the rejuvenation solutions, the validation approach. Results witness an increasing interest in this area (with 58% of the studies published in the last 5 years), an equivalent role of measurement-based and model-based solutions for aging analysis (42 and 40 studies, respectively) and a prevalent interest for rejuvenation (76/105 of the studies deals with rejuvenation).

Likely system invariants model properties that hold in operating conditions of a computing system. Invariants may be mined offline from training datasets, or inferred during execution. Scientific work has shown that invariants’ mining techniques support several activities, including capacity planning and detection of failures, anomalies and violations of Service Level Agreements. However their practical application by operation engineers is still a challenge. We aim to fill this gap through an empirical analysis of three major techniques for mining invariants in cloud-based utility computing systems: clustering, association rules, and decision list. The experiments use independent datasets from real-world systems: a Google cluster, whose traces are publicly available, and a Software-as-a-Service platform used by various companies worldwide. We assess the techniques in two invariants’ applications, namely executions characterization and anomaly detection, using the metrics of coverage, recall and precision. A sensitivity analysis is performed. Experimental results allow inferring practical usage implications, showing that relatively few invariants characterize the majority of operating conditions, that precision and recall may drop significantly when trying to achieve a large coverage, and that techniques exhibit similar precision, though the supervised one a higher recall. Finally, we propose a general heuristic for selecting likely invariants from a dataset.

Microservice architectures (MSA) is an emerging software architectural paradigm for service-oriented applications, well-suited for dynamic contexts requiring loosely coupled independent services, frequent software releases and decentralized governance. A key problem in the engineering of MSA applications is the estimate of their reliability, which is difficult to perform prior to release due frequent releases/service upgrades, dynamic service interactions, and changes in the way customers use the applications. This paper presents an in vivo testing method, named EMART, to faithfully assess the reliability of an MSA application in operation. EMART is based on an adaptive sampling strategy, leveraging monitoring data about microservices usage and failure/success of user demands. We present results of evaluation of estimation accuracy, confidence and efficiency, through a set of controlled experiments with publicly available subjects.

Internet of Vehicles (IoV) requires trust management to implement effective authentication and authorization of nodes, as it is not possible to establish trusted connections to the roadside components that eventually a vehicle will meet during its journey, ahead of time. To cope with this issue, dynamic access control is required, where authorizations are granted considering security policies and the node trustworthiness. However, decentralized trust management is a preferable solution, but it implies a considerable consumption of energy. Consumption is further exacerbated by the means needed to protect from attacks the trust management entities themselves. This work proposes suitable trust management for the IoT and IoV by exploiting the eventual consistency and security guarantees of blockchain. The design of a solution based on such technology is described, and an empirical assessment of its protection degree is provided.

In Continuous Integration (CI), regression testing is constrained by the time between commits. This demands for careful selection and/or prioritization of test cases within test suites too large to be run entirely. To this aim, some Machine Learning (ML) techniques have been proposed, as an alternative to deterministic approaches. Two broad strategies for ML-based prioritization are learning-to-rank and what we call ranking-to-learn (i.e., reinforcement learning). Various ML algorithms can be applied in each strategy. In this paper we introduce ten of such algorithms for adoption in CI practices, and perform a comprehensive study comparing them against each other using subjects from the Apache Commons project. We analyze the influence of several features of the code under test and of the test process. The results allow to draw criteria to support testers in selecting and tuning the technique that best fits their context.

To enter the production stage, in DevOps practices candidate software releases have to pass quality gates, where they are assessed to meet established target values for key indicators of interest. We believe software reliability should be an important such indicator, as it greatly contributes to the end‐user satisfaction. We propose DevOpRET , an approach for reliability testing as part of the acceptance testing stage in DevOps. DevOpRET relies on operational‐profile–based testing, a common reliability assessment technique. DevOpRET leverages usage and failure data monitored in operations to continuously refine its estimate. We evaluate accuracy and efficiency of DevOpRET through controlled experiments with a real‐world open source platform and with a microservice architectures benchmark. The results show that DevOpRET provides accurate and efficient estimates of the true reliability over subsequent DevOps cycles.

Functional, structural and operational testing are three broad categories of software testing methods driven by the product functionalities, the way it is implemented, and the way it is expected to be used, respectively. A large body of the software testing literature is devoted to evaluate and compare test techniques in these categories. Although it appears reasonable to devise hybrid methods to merge their different strengths - because different techniques may complement each other by targeting different types of faults and/or using different artifacts - we still miss clear guidelines on how to best combine them. We discuss differences and limitations of two popular testing approaches, namely coverage-driven and operational-profile testing, belonging to structural and operational testing, respectively. We show why and how test coverage and operational profile can cross-fertilize each other, improving the effectiveness of structural testing or, conversely, the product reliability achievable by operational testing. © 2019, Springer Nature Switzerland AG.

We present a framework for assessing operational reliability and performance of Web Services. The framework, named WS-REPAS, is hybrid in that it combines a modelling approach, based on Discrete Time Markov Chains (DTMC), with monitoring and in vivo testing of the service under assessment. Through the passive observation of the software in operation, field data are gathered and used to continuously update values of the parameters of the service DTMC model; changes in the service provisioning, or in the way it is used, trigger an active strategy, which executes proper testing sessions, ultimately yielding faithful estimates of the current service reliability and/or performance. We illustrate the framework and the automated support it provides. We show how it works describing experiments with a Web service publicly available in the Github repository.

This paper presents the design of ADaRTA, an aging detection and rejuvenation tool for Android. The tool is a software agent which i) performs selective monitoring of system processes and of trends in system performance indicators; ii) detects the aging state and estimates the time-to-aging-failure, through heuristic rules; iii) schedules and applies rejuvenation, based on the estimated time-to-aging-failure. The agent rules and parameters have been defined for ease of configuration and tuning by device designers. A stress testing experiment is discussed, showing ADaRTA's configurability for the device under test, and the ability of detecting the aging state to prevent device enter a failure state.

We present an adaptive software testing strategy for test case allocation, selection and generation, based on the combined use of operational profile and coverage spectrum , aimed at achieving high delivered reliability of the program under test. Operational profile-based testing is a black-box technique considered well suited when reliability is a major concern, as it selects the test cases having the largest impact on failure probability in operation. Coverage spectrum is a characterization of a program’s behavior in terms of the code entities (e.g., branches, statements, functions) that are covered as the program executes. The proposed strategy - named covrel+ - complements operational profile information with white-box coverage measures, so as to adaptively select/generate the most effective test cases for improving reliability as testing proceeds. We assess covrel+ through experiments with subjects commonly used in software testing research, comparing results with traditional operational testing. The results show that exploiting operational and coverage data in an integrated adaptive way allows generally to outperform operational testing at achieving a given reliability target, or at detecting faults under the same testing budget, and that covrel+ has greater ability than operational testing in detecting hard-to-detect faults.

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta, Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overview

We present the mechanical device used to install the Raft Tower Modules (RTMs) into the cryosat of the camera for the Large Synoptic Survey Telescope (LSST). In an RTM, the charge-coupled devices (CCDs) are packaged into a 3 x 3 Raft Sensor Assembly (RSA) and coupled to a Raft Electronics Crate (REC). An RTM weighs ~10 kg, is roughly 500 mm tall, and has a 126.5 mm-square footprint at the CCDs. The grid array which supports the RTM in the cryostat has a center-to-center distance of 127 mm. One of the key challenges for installing the RTMs in the 500 μm gap between CCDs of adjacent modules - contact between adjacent CCDs is strictly forbidden.

International audience; The Integration and Verification Testing of the Large Synoptic Survey Telescope (LSST) Camera is described. The LSST Camera will be the largest astronomical camera ever constructed, featuring a 3.2 giga-pixel focal plane mosaic of 189 CCDs with in-vacuum controllers and readout, dedicated guider and wavefront CCDs, a three element corrector with a 1.6-meter diameter initial optic, six optical filters covering wavelengths from 320 to 1000 nm with a novel filter exchange mechanism, and camera-control and data acquisition capable of digitizing each image in two seconds. In this paper, we describe the integration processes under way to assemble the Camera and the associated verification testing program. The Camera assembly proceeds along two parallel paths: one for the focal plane and cryostat and the other for the Camera structure itself. A range of verification tests will be performed interspersed with assembly to verify design requirements with a test-as-you-build methodology. Ultimately, the cryostat will be installed into the Camera structure as the two assembly paths merge, and a suite of final Camera system tests performed. The LSST Camera is scheduled for completion and delivery to the LSST observatory in 2020.

One of the biggest emerging challenges for telco operators is to dynamically create new services while maintaining the network at its optimal performance/revenue break point. To this end, operators are moving to a much leaner cloud-based Software Defined Network (SDN) infrastructure to achieve a truly programmable network fabric. While cloud services can be provisioned in seconds and tested in-production, service provisioning in SDNs still lasts many weeks and requires substantial manual effort. A large part of this service creation time can be attributed to testing and tuning the control plane. In this paper we present SCP-CLUB (SDN Control Plane CLoUd-based Benchmarking), a platform for in-production performance testing of telco operator SDNs, offering a level of automation as available in deploying cloud services. Telco cloud SDN performance testing with SCP-CLUB focuses on the analysis of how design choices in the cloud and SDN control planes influence SLA metrics like throughput and latency. We describe the SCP-CLUB architecture and its performance testing support capabilities. Experiments are performed on an SDN telco cloud built to demonstrate SCP-CLUB under production load condi- tions.

The idea of turning data centers executing scientific batch jobs into private clouds is as attractive as troubling. Cloud platforms may help both in limiting power consumption and in implementing fault tolerance strategies. However, there is also the fear that performance may worsen, and that the electricity required for longer job duration and fault tolerance implementation may overcome the saved one. In this paper, we present the consumability analysis for assessing the impact of cloud and fault tolerance tunings on scientific processing systems. The analysis considers performance, consumption, and dependability aspects, jointly. The aim is to pinpoint if, for a given system, there is a setting where consumption and job failure rate decrease, while performance is not affected. Applied to the scientific data center at our University, the analysis allowed us to find the proper selection of virtual machines’ configuration, consolidation strategy, and fault tolerance tuning.

Revealing anomalies at the operating system (OS) level to support online diagnosis activities of complex software systems is a promising approach when traditional detection mechanisms (e.g., based on event logs, probes and heartbeats) are inadequate or cannot be applied. In this paper we propose a configurable detection framework to reveal anomalies in the OS behavior, related to system misbehaviors. The detector is based on online statistical analyses techniques, and it is designed for systems that operate under variable and non-stationary conditions. The framework is evaluated to detect the activation of software faults in a complex distributed system for Air Traffic Management (ATM). Results of experiments with two different OSs, namely Linux Red Hat EL5 and Windows Server 2008, show that the detector is effective for mission-critical systems. The framework can be configured to select the monitored indicators so as to tune the level of intrusivity. A sensitivity analysis of the detector parameters is carried out to show their impact on the performance and to give to practitioners guidelines for its field tuning.

This paper explores the usage of search-based techniques for the Testing Resource Allocation Problem (TRAP). We focus on the analysis of the literature, surveying the research proposals where search-based techniques are exploited for different formulations of the TRAP. Three dimensions are considered: the model formulation, solution, and validation. The analysis allows to derive several observations, and finally outline some new research directions towards better (namely, closer to real-world settings) modelling and solutions, highlighting the most promising areas of investigation.

Context:In the industry of large-scale mission-critical systems, software is a pivotal asset and a key business driver. Production and maintenance costs of systems in domains like air/naval traffic control or homeland security are largely dependent on the quality of software, and there are numerous examples where poor software quality is blamed for major business failures. Because of the size, the complexity and the nature of systems and engineering processes in this industry, there is a strong need yet a slow shift toward innovation in software quality management. Objective:We present SVEVIA, a framework for software quality assessment and strategic decisions support for large-scale mission-critical systems engineering, and its application in a three years long industry-academy cooperation. Method:We started with the analysis of the industrial software quality management processes, and identified the key challenges toward a satisfying quality-cost-time trade-off. We defined new methods for product/process quality assessment, prediction, planning and optimization. We experimented them on the industrial partner systems and processes. They finally conflated in the SVEVIA framework. Results:SVEVIA was integrated into the industrial process, and tested with hundreds of software (sub)systems. More than 20 millions of lines of code – deployed in about 20 sites in Italy and UK – have come under the new quality measurement and improvement chain. The framework proved its ability to support systematic management of software quality and key decisions for productivity improvement. Conclusion:SVEVIA supports team leaders and managers coping with software quality in mission-critical industries, yielding figures and projections about quality and productivity trends for a prompt and informed decision-making.

Trust management is extremely important for sensor networks, and the emerging vision of the Internet of Things (IoT) does not represent an exception. In fact, it allows to realize a dynamic access control needed to cope with internal attacks conducted by compromised nodes, which are likely to occur in real world. However, trust management implies a considerable consumption of energy, due to the amount of messages exchanged to collect reputation scores. Consumption is further exacerbated by the means needed to protect from attacks the trust management entities themselves. This work proposes a suitable trust management for the IoT by exploiting the eventual consistency and security guarantees of blockchain. The design of a solution based on such a technology is described, and a qualitative assessment of its protection degree is provided.

Testing resource allocation is the problem of planning the assignment of resources to testing activities of software components so as to achieve a target goal under given constraints. Existing methods build on Software Reliability Growth Models (SRGMs), aiming at maximizing reliability given time/cost constraints, or at minimizing cost given quality/time constraints. We formulate it as a multi-objective debug-aware and robust opti- mization problem under uncertainty of data, advancing the state-of-the-art in the following ways. Multi-objective optimization produces a set of solutions, allowing to evaluate alternative trade-offs among reliability, cost and release time. Debug awareness relaxes the traditional assumptions of SRGMs – in particular the very unrealistic immediate repair of detected faults – and incorporates the bug assignment activity. Robustness provides solutions valid in spite of a degree of uncertainty on input parameters. We show results with a real-world case study.

A relevant objective of software reliability assessment is to get unbiased estimates with an acceptable trade-off between the number of tests required and the variance of the estimate. A low variance is desirable to increase the confidence in the estimate, but too many tests may be required by conventional reliability assessment testing techniques based solely on the operational profile. This article presents probabilistic sampling-based testing, a new technique using unequal probability sampling to exploit auxiliary information about the software under test so as to assess reliability unbiasedly and efficiently. The technique expedites the assessment process assuming the availability of some prior belief about input regions failure proneness. The evaluation by simulation and experimentally shows promising results in terms of estimate accuracy and efficiency.

This paper presents a cloud-based hybrid simulation platform to test large-scale distributed system-of-systems for the management and control of maritime traffic, the so-called vessel traffic systems (VTS). A VTS consists of multiple, heterogeneous, distributed, and interoperating systems, including radar, automatic identification systems, direction finders, electro-optical sensors, and gateways to external VTSs, information systems; identifying, representing, and analyzing interactions is a challenge to the evaluation of the real risks for safety and security of the marine environment. The need for reproducing in fabric the system behaviors that could occur in situ demands for the ability of integrating emulated and simulated environments to cope with the different testability requirements of involved systems and to keep testing cost sustainable. The platform exploits hybrid simulation and virtualization technologies, and it is deployable on a private cloud, reducing the cost of setting up realistic and effective testing scenarios.

—With cloud computing becoming pervasive in IT enterprises, the long-running performance of cloud-based applications and services and of the underlying virtualization systems is a paramount concern. Researchers have been studying the phenomenon of software aging, and the corresponding rejuvenation countermeasures, in cloud-related contexts since about 10 years. This paper reviews the effort conducted so far by the software aging and rejuvenation (SAR) community in the cloud domain. Figures about current research trends and future directions can be derived from the reported results.

In this chapter, the authors describe their experiences in designing, developing, and teaching a course on Software Architecture that tested both in an academic context with their graduate Computer Science students and in an advanced context of professional updating and training with scores of system engineers in a number of different companies. The course has been taught in several editions in the last five years. The authors describe its rationale, the way in which they teach it differently in academia and in industry, and how they evaluate the students' learning in the different contexts. Finally, the authors discuss the lessons learnt and describe how this experience is inspiring for the future of this course.

The practice of defect analysis is recognized as an essential task for software process measurement, yet its effective application in the industrial development of large-scale software systems raises several challenges. We report the results of a study conducted at SELEX ES - a large system integrator leader in the market of software-intensive mission-critical systems. The article describes the defect analysis approach that we tailored to evaluate the software development process with respect to the quality of produced software and its relation with the required effort. Three key phases of the process were addressed, regarding the software implementation, the testing phase and the prerelease defect fixing activity, over a set of six computer software configuration items developed from 2009 to 2012 for the naval and maritime domain product line.

Testing accounts for a relevant part of the production cost of complex or critical software systems. Nevertheless, time and resources budgeted to testing are often underestimated with respect to the target quality goals. Test managers need engineering methods to perform appropriate choices in spending testing resources, so as to maximize the outcome. We present a method to dynamically allocate testing resources to software components minimizing the estimated number of residual defects and/or the estimated residual defect density. We discuss the application to a real-world critical system in the homeland security domain. We describe a support tool aimed at easing industrial technology transfer by hiding to practitioners the mathematical details of the method application.

In a large Infrastructure-as-a-Service (IaaS) Cloud, component failures are quite common. Such failures may lead to occasional system downtime and eventual violation of Service Level Agreements (SLAs) on the Cloud service availability. The availability analysis of the underlying infrastructure is useful to the service provider to design a system capable of providing a defined SLA, as well as to evaluate the capabilities of an existing one. This paper presents a scalable, stochastic model-driven approach to quantify the availability of a large-scale IaaS Cloud, where failures are typically dealt with through migration of physical machines among three pools: hot (running), warm (turned on, but not ready), and cold (turned off). Since monolithic models do not scale for large systems, we use an interacting Markov chain based approach to demonstrate the reduction in the complexity of analysis and the solution time. The three pools are modeled by interacting sub-models. Dependencies among them are resolved using fixed-point iteration, for which existence of a solution is proved. The analytic-numeric solutions obtained from the proposed approach and from the monolithic model are compared. We show that the errors introduced by interacting sub- models are insignificant and that our approach can handle very large size IaaS Clouds. The simulative solution is also considered for the proposed model, and solution time of the methods are compared.