The diffusion of Open Government Data (OGD) in recent years kept a very fast pace. However, evidence from practitioners shows that disclosing data without proper quality control may jeopardize datasets reuse and negatively affect civic participation. Current approaches to the problem in literature lack of a comprehensive theoretical framework. Moreover, most of the evaluations concentrate on open data platforms, rather than on datasets. In this work, we address these two limitations and set up a framework of indicators to measure the quality of Open Government Data on a series of data quality dimensions at most granular level of measurement. We validated the evaluation framework by applying it to compare two cases of Italian OGD datasets: an internationally recognized good example of OGD, with centralized disclosure and extensive data quality controls, and samples of OGD from decentralized data disclosure (municipalities level), with no possibility of extensive quality controls as in the former case, hence with supposed lower quality. Starting from measurements based on the quality framework, we were able to verify the difference in quality: the measures showed a few common acquired good practices and weaknesses, and a set of discriminating factors that pertain to the type of datasets and the overall approach. On the basis of this evaluation, we also provided technical and policy guidelines to overcome the weaknesses observed in the decentralized release policy, addressing specific quality aspect

The paper describes a systematic approach for automatically introducing data and code redundancy into an existing program written using a high-level language. The transformations aim at making the program able to detect most of the soft-errors affecting data and code, independently of the Error Detection Mechanisms (EDMs) possibly implemented by the hardware. Since the transformations can be automatically applied as a pre-compilation phase, the programmer is freed from the cost and responsibility of introducing suitable EDMs in its code. Preliminary experimental results are reported, showing the fault coverage obtained by the method, as well as some figures concerning the slow-down and code size increase it causes.

Empirical studies have revealed a discrepancy between academic theory and industrial practices regarding the selection and integration of commercial off-the-shelf and open source software components in software system development.

In recent years, several design notations have been proposed to model domain-specific applications or reference architectures. In particular, Conallen has proposed the UML Web Application Extension (WAE): a UML extension to model Web applications. The aim of our empirical investigation is to test whether the usage of the Conallen notation supports comprehension and maintenance activities with significant benefits, and whether such benefits depend on developers ability and experience. This paper reports and discusses the results of a series of four experiments performed in different locations and with subjects possessing different experience-namely, undergraduate students, graduate students, and research associates-and different ability levels. The experiments aim at comparing performances of subjects in comprehension tasks where they have the source code complemented either by standard UML diagrams or by diagrams stereotyped using the Conallen notation. Results indicate that, although, in general, it is not possible to observe any significant benefit associated with the usage of stereotyped diagrams, the availability of stereotypes reduces the gap between subjects with low skill or experience and highly skilled or experienced subjects. Results suggest that organizations employing developers with low experience can achieve a significant performance improvement by adopting stereotyped UML diagrams for Web applications.