Verifying temporal specifications of Java programs

Many Java programs encode temporal behaviors in their source code, typically mixing three features provided by the Java language: (1) pausing the execution for a limited amount of time, (2) waiting for an event that has to occur before a deadline expires, and (3) comparing timestamps. In this work, we show how to exploit modern SMT solvers together with static analysis in order to produce a network of timed automata approximating the temporal behavior of a set of Java threads. We also prove that the presented abstraction preserves the truth of MTL and ATCTL formulae, two well-known logics for expressing timed specifications. As far as we know, this is the first feasible approach enabling the user to automatically model check timed specifications of Java software directly from the source code

Semantics-driven extraction of timed automata from Java programs

The automatic verification of time properties of models extracted from programs is challenging, mainly because modern programming languages, such as Java, represent time without a proper semantics. Current approaches to extract time models from source code either represent time only as a tree-like sequence of events or require developers to manually provide a formal model of the time behavior. This makes it difficult for software developers to verify various aspects of their systems, such as timeouts, delays and periodicity of the execution. In this paper, we introduce a formal definition of the time semantics for the Java programming language. Based on the semantics, we present an approach to automatically extract timed automata and their time constraints from Java programs at method level. First, our approach detects the Java statements that involve time, from which it then extracts the timed automata. Our extracted automata are directly amenable to the verification of time properties of the corresponding Java methods. We evaluated the accuracy of our approach on twenty open source Java projects that implement time behavior in their source code. The results show that our approach achieves 100% precision and recall in identifying time related information. They also show that 95% of the timed automata extracted from source code correctly model the time behavior of the method. Finally, we show the applicability of our timed automata to identify eight real errors in four open source Apache systems

Automatic repair of timestamp comparisons

Automated program repair has the potential to reduce the developers’ effort to fix errors in their code. In particular, modern programming languages, such as Java, C, and C#, represent time as integer variables that suffer from integer overflow, introducing subtle errors that are hard to discover and repair. Recent researches on automated program repair rely on test cases to discover failures to correct, making them suitable only for regression errors. We propose a new strategy to automatically repair programs that suffer from timestamp overflows that are manifested in comparison expressions. It unifies the benefits of static analysis and automatic program repair avoiding to depend on testing to identify and correct defected code. Our approach performs an abstract analysis over the time domain of a program using a Time Type System to identify the problematic comparison expressions. The repairing strategy rewrites the timestamp comparisons exploiting the binary representation of machine numbers to correct the code. We have validated the applicability of our approach with 20 open source Java projects. The results show that it is able to correctly repair all 246 identified errors. Furthermore, several patches for three open source projects have been acknowledged and accepted by their developers

Negative Shocks Predict Change in Cognitive Function and Preferences: Assessing the Negative Affect and Stress Hypothesis

In the context of the current COVID-19 pandemic, households throughout the world have to cope with negative shocks. Previous research has shown that negative shocks impair cognitive function and change risk, time and social preferences. In this study, we analyze the results of a longitudinal multi-country survey conducted in Italy (N = 1652), Spain (N = 1660) and the United Kingdom (N = 1578). We measure cognitive function using the Cognitive Reflection Test and preferences traits (risk, time and social preferences) using an experimentally validated set of questions to assess the differences between people exposed to a shock compared to the rest of the sample. We measure four possible types of shocks: labor market shock, health shock, occurrence of stressful events, and mental health shock. Additionally, we randomly assign participants to groups with either a recall of negative events (more specifically, a mild reinforcement of stress or of fear/anxiety), or to a control group (to recall neutral or joyful memories), in order to assess whether or not stress and negative emotions drive a change in preferences. Results show that people affected by shocks performed worse in terms of cognitive functioning, are more risk loving, and are more prone to punish others (negative reciprocity). Data do not support the hypotheses that the result is driven by stress or by negative emotions

City data ecosystems between theory and practice: A qualitative exploratory study in seven European cities

The exponential growth of data collection opens possibilities for analyzing data to address political and societal challenges. Still, European cities are not utilizing the potential of data generated by its citizens, industries, academia, and public authorities for their public service mission. The reasons are complex and relate to an intertwined set of organizational, technological, and legal barriers, although good practices exist that could be scaled, sustained, and further developed. The article contributes to research on data-driven innovation in the public sector comparing high-level expectations on data ecosystems with actual practices of data sharing and innovation at the local and regional level. Our approach consists in triangulating the analysis of in-depth interviews with representatives of the local administrations with documents obtained from the cities. The interviews investigated the experiences and perspectives of local administrations regarding establishing a local or regional data ecosystem. The article examines experiences and obstacles to data sharing within seven administrations investigating what currently prevents the establishment of data ecosystems. The findings are summarized along three main lines. First, the limited involvement of private sector organizations as actors in local data ecosystems through emerging forms of data sharing became evident. Second, we observed the concern over technological aspects and the lack of attention on social or organizational issues. Third, a conceptual decision to apply a centralized and not a federated digital infrastructure is noteworthy

Modeling time in Java programs for automatic error detection

Modern programming languages, such as Java, represent time as integer variables, called timestamps. Timestamps allow developers to tacitly model incorrect time values resulting in a program failure because any negative value or every positive value is not necessarily a valid time representation. Current approaches to automatically detect errors in programs, such as Randoop and FindBugs, cannot detect such errors because they treat timestamps as normal integer variables and test them with random values verifying if the program throws an exception. In this paper, we present an approach that considers the time semantics of the Java language to systematically detect time related errors in Java programs. With the formal time semantics, our approach determines which integer variables handle time and which statements use or alter their values. Based on this information, it translates these statements into an SMT model that is passed to an SMT solver. The solver formally verifies the correctness of the model and reports the violations of time properties in that program. For the evaluation, we have implemented our approach in a prototype tool and applied it to the source code of 20 Java open source projects. The results show that our approach is scalable and it is capable of detecting time errors precisely enough allowing its usability in real-world applications

Assessing the impacts of digital government transformation in the EU

This report presents the results of the conceptual and empirical work conducted as part of the JRC research on “Exploring Digital Government Transformation: understanding public sector innovation in a data-driven society” conducted within the framework of the “European Location Interoperability Solutions for eGovernment (ELISE)" Action of the ISA2 Programme on Interoperability solutions for public administrations, businesses and citizens, coordinated by DIGIT. Building on the systematisation of the state of the art carried out in the previous phase of the research, the report presents an original conceptual framework for assessing the impacts of Digital Government transformation in the EU and discusses the results of case studies carried out using an experimental or quasi-experimental approach to test and validate it, carried out in different policy areas in various EU countries. The report concludes outlining the final proposal of DigiGov F 2.0, which defines the dimensions and elements of analysis for assessing the effects that can be generated by digital innovation in the public sector and the impacts they have at social, economic and political levels in different policy-cycle phases and governance contexts.JRC.B.6-Digital Econom

Exploring Digital Government transformation in the EU

This report presents the findings of the analysis of the state of the art conducted as part of the JRC research on “Exploring Digital Government Transformation in the EU: understanding public sector innovation in a data-driven society” (DIGIGOV), within the framework of the “European Location Interoperability Solutions for eGovernment (ELISE)" Action of the ISA2 Programme on Interoperability solutions for public administrations, businesses and citizens, coordinated by DIGIT. The results of the review of literature, based on almost 500 academic and grey literature sources, as well as the analysis of digital government policies in the EU Member States provide a synthetic overview of the main themes and topics of the digital government discourse. The report depicts the variety of existing conceptualisations and definitions of the digital government phenomenon, measured and expected effects of the application of more disruptive innovations and emerging technologies in government, as well as key drivers and barriers for transforming the public sector. Overall, the literature review shows that many sources appear overly optimistic with regard to the impact of digital government transformation, although the majority of them are based on normative views or expectations, rather than empirically tested insights. The authors therefore caution that digital government transformation should be researched empirically and with a due differentiation between evidence and hope. In this respect, the report paves the way to in-depth analysis of the effects that can be generated by digital innovation in public sector organisations. A digital transformation that implies the redesign of the tools and methods used in the machinery of government will require in fact a significant change in the institutional frameworks that regulate and help coordinate the governance systems in which such changing processes are implemented.JRC.B.6-Digital Econom

an ecological study on the association between characteristics of hospital units and the risk of occupational injuries and adverse events on the example of an italian teaching hospital

OBJECTIVES: We explored the association of workplace characteristics with occupational injuries and adverse events in an Italian teaching hospital. MATERIAL AND METHODS: This ecological study was conducted using data routinely collected in the University Hospital of Udine, Northeastern Italy. Poisson regression models were used to investigate, at the hospital unit level, the association between 5 outcomes, including: occupational injuries, patient falls, medication errors, other adverse events and near-misses, and various characteristics of the units. RESULTS: The proportion of female workers in a unit, the average number of sick-leave days and of overtime hours, the number of medical examinations requested by employees, and being a surgical unit were significantly associated with some of the outcomes. CONCLUSIONS: Despite ecological nature of the study, which does not allow for inferences to be drawn at the individual level, the results of our study provide useful clues to support strategies and interventions directed towards healthier work environments and better patient care in hospitals. Language: e