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The Centre for Dating and Diagnostics was established in 2001 at the University of Salento with the aim of becoming a National Centre for radiocarbon dating by AMS. After more than fifteen years of operation this goal has been fully achieved with thousands of dated samples for researches spanning from Archaeological, Earth and Forensics sciences to industrial and environmental applications. Furthermore, in the frame of different large scale National projects, the experimental potential of the facility has been substantially improved through the installation of new beam lines and experimental set-ups.

SUSHI and TARDIS are two automatic test generators for Java programs. They generate test suites, aiming to satisfy the branch coverage adequacy criterion. Both tools exploit symbolic execution to analyze the control flow paths of the program under test and generate path conditions, i.e., constraints on the program inputs that determine the execution of a path, focusing in particular on control flow paths that lead to uncovered branches. Then, SUSHI and TARDIS exploit these path conditions with a search-based algorithm to generate unit tests (in JUnit) that execute those program paths. In this paper we report the results of SUSHI and TARDIS at the seventh edition of the unit testing tool competition at the 2019 edition of the International Workshop on Search-Based Software testing.

Background: Continuous subcutaneous insulin infusion (CSII) is increasing worldwide, mostly because of improved technology. The aim of this study was to evaluate the current status of CSII in Italy. Materials and Methods: Physicians from 272 diabetes centers received a questionnaire investigating clinical features, pump technology, and management of patients on CSII. Results: Two hundred seventeen centers (79.8%) joined the study and, by the end of April 2013, gave information about 10,152 patients treated with CSII: 98.2% with type 1 diabetes mellitus, 81.4% adults, 57% female, and 61% with a conventional pump versus 39% with a sensor-augmented pump. CSII advanced functions were used by 68% of patients, and glucose sensors were used 12 days per month on average. Fifty-eight percent of diabetes centers had more than 20 patients on CSII, but there were differences among centers and among regions. The main indication for CSII was poor glucose control. Dropout was mainly due to pump wearability or nonoptimal glycemic control. Twenty-four hour assistance was guaranteed in 81% of centers. A full diabetes team (physician+ nurse + dietician + psychologist) was available in 23% of adult-care diabetes centers and in 53%of pediatric diabetes units. Conclusions: CSII keeps increasing in Italy. More work is needed to ensure uniform treatment strategies throughout the country and to improve pump use.

The European Rail Traffic Management System (ERTMS) represents a class of embedded software-intensive systems of systems for the management of the European rail traffic, in a scenario where interoperability between different European rail traffic infrastructures must be maintained. Due to the intrinsically safety-critical nature of ERTMS, software testing must meet the strict prescriptions imposed by the certification authorities, which often require the availability of the entire system. This potentially leads to perform the tests at a late stage of development, even though the best practices suggest that it would be more convenient to test the system incrementally since the early stages of development. This paper deals with the solution we devised to address this issue in a recent ERTMS project. We adopted a design-for-testability pattern which purposely enhances the observability and the controllability of the software under test to make it testable at any stage of development, allowing the exploitation of system tests even during component and integration testing.

Random and search-based test generators yield realistic test cases based on program APIs, but often miss structural test objectives that depend on non-trivial data structure instances; Whereas symbolic execution can precisely characterise those dependencies but does not compute method sequences to instantiate them. We present SUSHI, a high-coverage test case generator for programs with complex structured inputs. SUSHI leverages symbolic execution to generate path conditions that precisely describe the relationship between program paths and input data structures, and converts the path conditions into the fitness functions of search-based test generation problems. A solution for the search problem is a legal method sequence that instantiates the structured inputs to exercise the program paths identified by the path condition. Our experiments indicate that SUSHI can distinctively complement current automatic test generation tools.

Automatically generating effective test suites promises a significant impact on testing practice by promoting extensively tested software within reasonable effort and cost bounds. Code-based test generators rely on the source code of the software under test to identify test objectives and to steer the test case generation process accordingly. Currently, the most mature proposals on this topic come from the research on random testing, dynamic symbolic execution, and search-based testing. This paper studies the effectiveness of a set of state-of-the-research test generators on a family of industrial programs with nontrivial domain-specific peculiarities. These programs are part of a software component of a real-time and safety-critical control system and integrate in a control task specified in LabVIEW, a graphical language for designing embedded systems. The result of this study enhances the available body of knowledge on the strengths and weaknesses of test generators. The empirical data indicate that the test generators can truly expose subtle (previously unknown) bugs in the subject software and that there can be merit in using different types of test generation approaches in a complementary, even synergic fashion. Furthermore, our experiment pinpoints the support for floating point arithmetics and nonlinear computations as a major milestone in the path to exploiting the full potential of the prototypes based on symbolic execution in industry.

Structural code coverage criteria have been studied since the early seventies, and now they are well supported by commercial and open source tools, and are commonly embedded in several advanced industrial processes. Most industrial applications still refer to simple criteria, like statement and branch coverage, and consider more complex criteria, like modified condition decision coverage, only rarely and often driven by the requirements of certification agencies. The industrial value of structural criteria is limited by the difficulty of achieving high coverage, due to both the complexity of deriving test cases that execute specific uncovered elements and the presence of many infeasible elements in the code. In this paper, we propose a technique that both generates test cases that execute yet uncovered branches and identifies infeasible branches that can be eliminated from the computation of the branch coverage. In this way, we can increase branch coverage up to closely approximate full coverage, thus improving its industrial value. The algorithm combines symbolic analysis, abstraction refinement, and a novel technique named coarsening, to execute unexplored branches, identify infeasible ones, and mitigate the state space explosion problem. In the paper, we present the technique, and illustrate its effectiveness through a set of experimental results obtained with a prototype implementation.

This paper investigates the transmission of spot electricity prices and price volatility among the five Australian regional electricity markets. In particular, VAR(k)-BEKK(p, q) models with optimized lag lengths and different distributional assumptions are analysed. Empirical results suggest that a VAR(3)-BEKK(1,2) under Student-t assumption can better describe the complex dynamics between the markets.

Inductive learning methods allow the system designer to infer a model of the relevant phenomena of an unknown process by extracting information from experimental data. A wide range of inductive learning methods is nowadays available, potentially ensuring different levels of accuracy on different problem domains. In this critical review of theoretic results gained in the last decade, we address the problem of designing an inductive classification system with optimal accuracy when domain knowledge is limited and the number of available experiments is possibly small. By analyzing the formal properties of consistent learning methods and of accuracy estimators, we wish to convey to the reader the message that the common practice of aggressively pursuing error minimization with different training algorithms and classification families is unjustified.

This chapter presents test generation techniques that address the automatic production of test cases to validate evolving software, aiming to improve the adequacy of testing in the light of a performed upgrade. For human experts it is usually hard to achieve high test case coverage by manually generating test cases. In particular, when a program is upgraded, testers need to adapt the test suite of the base version of the program to the new version, to cover the relevant code according to the kind of upgrade that has been implemented. The test case generation techniques presented in this chapter aim to automatically augment the existing test suites with test cases that exercise the uncovered regions of the code. These test cases represent extremely useful executions to give as complete a view as possible of the behavior of the upgraded program. We will describe ARC-B, a technique for the automatic generation of test cases, and its extension as DeltaTest that we have developed in the context of the European FP7 Project PINCETTE. DeltaTest extends ARC-B to target software changes in a more specific fashion, according to ideas that resulted from the feedback gained while using ARC-B during the project. Specifically, DeltaTest exploits a program slicer to distinguish the code impacted by modifications, and builds on this information to generate test suites that specifically address the testing of software changes. In the next sections, we describe the technology that underlies ARC-B, report our experience of applying ARC-B to industrial software provided as case studies by industrial partners of the project, present the DeltaTest technique, and discuss initial data on the strength of DeltaTest.

Bloodstream infection due to Rhodotorula glutinis is extremely rare and mostly associated with underlying immunosuppression or cancer. Vascular access devices provide the necessary surfaces for biofilm formation and are currently responsible for a significant percentage of human infections. In this work, we describe a rare case of central venous catheter-related Rhodotorula glutinis fungemia in a female patient with acute myelogenous leukemia in remission. The timely removal of central venous catheter was an essential element for overcoming this CVC-related Rhodotorula fungemia.

Code smells are structural characteristics of software that may indicate a code or design problem that makes software hard to evolve and maintain, and may trigger refactoring of code. Recent research is active in defining automatic detection tools to help humans in finding smells when code size becomes unmanageable for manual review. Since the definitions of code smells are informal and subjective, assessing how effective code smell detection tools are is both important and hard to achieve. This paper reviews the current panorama of the tools for automatic code smell detection. It defines research questions about the consistency of their responses, their ability to expose the regions of code most affected by structural decay, and the relevance of their responses with respect to future software evolution. It gives answers to them by analyzing the output of four representative code smell detectors applied to six different versions of GanttProject, an open source system written in Java. The results of these experiments cast light on what current code smell detection tools are able to do and what the relevant areas for further improvement are

The software industry favors dynamic testing over static analysis of software, because traditional static software analysis techniques do not adequately balance automation, precision and scalability. Recently several researchers have combined static and dynamic techniques to overcome these problems. Undergoing efforts include concolic execution, testing-based correctness prove, execution driven abstract interpretation and dynamic invariant generation. This paper summarizes the state of the art about combining dynamic testing and static analysis, and designs a roadmap towards a modern approach to software V&V that enhances dynamic testing with static analysis techniques. In particular, this paper surveys the most promising approaches to combine dynamic testing and static program analysis. It classifies the techniques against a framework of combination patterns, to facilitate the identification of commonalities and complementarities between the techniques. It quantifies analytically the gain that stems from the most important combination patterns. It provides a roadmap for future research.

Infeasible execution paths reduce the precision of structural testing coverage and limit the industrial applicability of structural testing criteria. In this paper, we propose a technique that combines static and dynamic analysis approaches to identify infeasible program elements that can be eliminated from the computation of structural coverage to obtain accurate coverage data. The main novelty of the approach stems from its ability to identify a relevant number of infeasible elements, that is, elements that belong statically to the code, but cannot be executed under any input condition. The technique can also generate new test cases that execute uncovered elements, thus increasing the structural coverage of the program. The experimental results obtained on a prototype implementation for computing accurate branch coverage and reported in this paper indicate that the technique can effectively improve structural coverage measurements and can thus increase the industrial applicability of complex structural coverage criteria.

Modern industrial production lines are characterized by rapid dynamics, high noise levels, and low knowledge of the underlying physical phenomena. In these situations, inductive learning methods allow the system designer to infer a model of the relevant process phenomena by extracting information from experimental data. A wide range of inductive learning methods is available to the system designer, potentially ensuring different levels of accuracy on different problem domains. In this paper we consider the problem of designing an inductive classification system with optimal accuracy when domain knowledge is limited and the number of available experiments is small. By analyzing the formal properties of consistent learning methods and of accuracy estimators, we wish to convey to the reader the message that the common practice of aggressively pursuing error minimization with different training algorithms and classification families is unjustified. Our position is illustrated by analyzing a classification problem with industrial relevance.

In this paper some representative peer-to-peer file sharing applications are compared against two sets of features. The first set describes the semantics of the relevant primitive operations over the shared data space. The second set describes the algorithmic and architectural solutions to implement these primitives. The obtained classification points out the mutual relationships between the expressive power and the degree of abstraction over low-level issues offered by each application.

The hyperglycemic effect of 28 g sucrose, taken during a mixed meal, was studied in six insulin-dependent diabetes mellitus (IDDM) patients controlled by artificial pancreas. On 2 consecutive days the patients were given, in random order, two Italian meals containing macaroni, bread, meat, vegetables, fruit, olive oil, and an eggnog made with sucrose (meal A) or saccharin (meal B). The two meals were isocaloric and contained equal amounts of carbohydrates. The feedback control on blood glucose continued for 180 min after the meals. Plasma glucose levels and insulin infusion rates delivered by the artificial pancreas after the two test meals did not show any significant differences regarding basal and peak values, peak times, and areas under the curves. A modest amount of sucrose, taken during a mixed meal, does not produce a hyperglycemic effect higher than an equal amount of complex carbohydrates in IDDM patients controlled by artificial pancreas. The same may be expected in well-controlled IDDM patients in conventional therapy because a correlation exists between insulin requirement for conventional therapy and insulin delivered during glucose-controlled insulin infusion.

Modern distributed computing demands unprecedented levels of dynamicity and reconfiguration. Mobile computing, peer-to-peer networks, computational grids, multi-agent systems, are examples of domains exhibiting a continuously changing system configuration. In these settings, the context where computation occurs is not only dynamically changing, but also affecting the components’ behavior in a fundamental way, by enabling or inhibiting some of their actions. This paper is a first step in laying the formal foundation for a process calculi specification style that: i) fosters a coordination approach by sharply separating the process behavior from the computational context defined by system changes; ii) enables the specifier to define her notion of context and the rules governing how it affects the application process behavior.

Online quality detection and online laser beam control are important research topics to improve the overall quality of presentday laser beam material processing units. In both cases innovative units are being studied where the state is monitored by a set of heterogeneous in-process sensors conveying a large amount of information. However, low experimental reproducibility, lack of dominion knowledge and high costs greatly limit our ability to find an optimal solution. In this paper we propose a methodology to guide the engineer's design choices towards an optimal implementation of the inductive classifier.