The adoption of Open Source in industrial applications has increased in the last years. In this context the need to provide answers to high levels of Maintenance arises. Therefore it is critical to select Open Sources components to be integrated in a software system according to their Maintenance characteristics. The work presents a Metric Model and its related Decision Model for OS Governance and in particular for selecting OSs according to their Maintenance Level. The Metric Model was obtained individuating some automatically calculable measures from a group of projects available on the Web. The measures were validated on several OSs used in industrial projects. The results are of interest and encourage future research.

This paper presents a framework aimed at supporting knowledge transferring inside and outside an organization for innovation purposes. For this goal, the authors propose a Knowledge Experience Base KEB, which collects Knowledge Experience Packages KEP, to support the formalization and packaging of knowledge and experience of innovation stakeholders, encouraging gradual explanation of tacit information of bearers of knowledge to facilitate the transfer, minimizing costs and risks.

The easy access to training and content in the digital age has greatly accelerated the level of competitiveness of individuals and organizations. The e-learning space is outside the context of academic and corporate. It supports lifelong learning which has become central to the development of competitive advantage and for the affirmation individual. This implies that training is paid to individuals are characterized by requirements training extremely varied. The designers of e-learning systems have, in this context, the task of developing systems with adaptive and flexible functions in order to adapt the path training, the pedagogical models and the interactions between attending to their needs and preferences. In literature, these systems are called adaptive e-learning systems. Adaptive e-learning systems adopt a system of rules with complex logic. This may involve a high number of anomalies which correct can be difficult and expensive. In this study presents the conceptual models and technology of adaptive elearning more accredited in the literature and a framework based on the technique of decision table for the representation and validation of complex business logic in the rules

In software production process, a lot of knowledge is created and remain silent. Therefore, it cannot be reused to improve the effectiveness and the efficiency of these processes. This problem is amplified in the case of a distributed production. In fact, distributed software development requires complex context specific knowledge regarding the particularities of different technologies, the potential of existing software, the needs and expectations of the users. This knowledge, which is gained during the project execution, is usually tacit and is completely lost by the company when the production is completed. Moreover, each time a new production unit is hired, despite the diversity of culture and capacity of people, it is necessary to standardize the working skills and methods of the different teams if the company wants to keep the quality level of processes and products. In this context, we used the concept of Knowledge Experience Package (KEP), already specified in previous works and the tool realized to support KEP approach. In this work, we have carried out an experiment in an industrial context in which we compared the software development supported by KEPs with the development achieved without it.

Many technological solutions, especially in the fields of computer science and software engineering, are poorly supported by empirical evidences of their effectiveness and by the experience of acquiring the application in different industrial contexts. The lack of empirical evidences makes managers less confident in applying technological solutions proposed by the research community. Moreover, the lack of experience in the acquisition of technological solutions in different industrial contexts makes acquisition of the technological solution highly risky. These two issues represent a barrier to the diffusion of innovative technological solutions. This paper presents a Knowledge Management System (KMS), called PROMETHEUS, which consists of a platform that manages the Knowledge Experience Base (KEB), which collects Knowledge Experience Packages (KEP). The KMS thus formed supports the formalization and packaging of knowledge and experience of producers and innovation transferors encouraging gradual elicitation of tacit information of bearers of knowledge to facilitate the transfer. The KMS enables the cooperative production and evolution of KEP between different authors and users

Si presenta un framework per il supporto cooperativo della catena dell’innovazione nella prospettiva dell’Open In- novation (OI). Viene proposto un Knowledge Management System (KMS) che consiste in un insieme di processi che formano l’Experience Factory (EF) e una piattaforma che è una Knowledge Experience Base (KEB), che colleziona Knowledge Experience Packages (KEP). Il KMS così formato supporta la formalizzazione e l’impacchettamento delle conoscenze ed esperienze da parte dei produttori e dei cedenti l’innovazione incoraggiando una graduale esplicitazione di informazione tacita nei portatori di conoscenze. Si facilita in tal modo per facilitare il trasferimento riducendo al minimo costi e rischi. Il KMS consente la produzione cooperativa di KEP tra i diversi autori che contribuiscono alla produzione di KEP e gli utenti di quest’ultimo. Il documento descrive l’approccio delineato nel Progetto Prometheus e le precauzioni adottate nella progettazione dei KEP per garantire che l’esperienza in esso contenuta, anche se raccolta attraverso progetti eseguiti durante molti anni-persona, possa essere rapidamente acquisita dall’utente e contenga gli strumenti per agevolare l’acquisizione di conoscenze di supporto all’innovazione.

This paper presents a Knowledge Management System (KMS), called PROMETHEUS, which consists of a set of processes that constitute the Experience Factory (EF) and a platform that is the Knowledge Experience Base (KEB), which collects Knowledge Experience Packages (KEP). The KMS thus formed supports the formalization and packaging of knowledge and experience of producers and innovation transferors encouraging gradual explanation of tacit information of bearers of knowledge to facilitate the transfer. The KMS enables the cooperative production of KEP between different authors contributing to the production of KEP and users of the latter. The paper describes the approach outlined in the PROMETHEUS Project and the precautions taken in the design of KEP to ensure that: the experience contained in it, even when collected through projects executed by many person-years, can be quickly acquired by the user, contains the tools to facilitate the acquisition of knowledge innovation support to transfer.

The competitive pressure for organizations and countries has moved the focus of economy from material to immaterial assets. The recognition that knowledge is the fundamental driver of sustainable competitive and collaborative advantage has been a major breakthrough in management thinking. The movement from internal R&D to external connect and develop opens the door for large and small companies to reach beyond their core competencies to remain competitive in an increasingly complex, uncertain and changing environment. This phenomenon, called Open Innovation, alone has opened completely new perspectives about how to manage human, physical and financial resources. It has also influenced organisations to start recognising that to thrive they need to find new ways of accessing the knowledge they need exactly when they need it, in order to adapt to an ever-changing and increasingly complex and uncertain environment. To this end we propose a platform that is the Knowledge Base Experience (KEB), which collects Knowledge Experience Packages (KEP). This framework is able to support a cooperative “innovation chain” from an Open Innovation (OI) perspective because it contains the tools needed to facilitate the acquisition of knowledge that support the innovation to transfer with particular emphasis on supporting, in particular, Small and Medium companies to survive the current turbulence of the markets

Practitioners must continually update their skills to align their professional profile to market needs and social organizations in which they live, both characterized by extreme variability and volatility. In this scenario, Universities, the traditional Institution for the knowledge transferring, assume the role of an institution dedicated to lifelong learning. However the lifelong learning highlights several issues that make it unsuitable to the university instructional models. In order to face this problem the authors propose to use a Learning Network model integrating a Knowledge Base Experience (Prometheus) to support distribution of contents and to the enhancement knowledge transferring. The results of an empirical experimentation encourage their adoption in real contexts

All projects involve risk; a zero risk project is not worth pursuing. Furthermore, due to software project uniqueness, uncertainty about final results will always accompany software development. While risks cannot be removed from software development, software engineers instead, should learn to manage them better (Arshad et al., 2009; Batista Webster et al., 2005; Gilliam, 2004). Risk Management and Planning requires organization experience, as it is strongly centred in both experience and knowledge acquired in former projects. The larger experience of the project manager improves his ability in identifying risks, estimating their occurrence likelihood and impact, and defining appropriate risk response plan. Thus risk knowledge cannot remain in an individual dimension, rather it must be made available for the organization that needs it to learn and enhance its performances in facing risks. If this does not occur, project managers can inadvertently repeat past mistakes simply because they do not know or do not remember the mitigation actions successfully applied in the past or they are unable to foresee the risks caused by certain project restrictions and characteristics. Risk knowledge has to be packaged and stored over time throughout project execution for future reuse. Risk management methodologies are usually based on the use of questionnaires for risk identification and templates for investigating critical issues. Such artefacts are not often related each other and thus usually there is no documented cause-effect relation between issues, risks and mitigation actions. Furthermore today methodologies do not explicitly take in to account the need to collect experience systematically in order to reuse it in future projects. To convey these problems, this work proposes a framework based on the Experience Factory Organization (EFO) model (Basili et al., 1994; Basili et al., 2007; Schneider & Hunnius, 2003) and then use of Quality Improvement Paradigm (QIP) (Basili, 1989). The framework is also specialized within one of the largest firms of current Italian Software Market. For privacy reasons, and from here on, we will refer to it as “FIRM”. Finally in order to quantitatively evaluate the proposal, two empirical investigations were carried out: a post-mortem analysis and a case study. Both empirical investigations were carried out in the FIRM context and involve legacy systems transformation projects. The first empirical investigation involved 7 already executed projects while the second one 5 in itinere projects. The research questions we ask are: Does the proposed knowledge based framework lead to a more effective risk management than the one obtained without using it? Does the proposed knowledge based framework lead to a more precise risk management than the one obtained without using it? The rest of the paper is organized as follows: section 2 provides a brief overview of the main research activities presented in literature dealing with the same topics; section 3 presents the proposed framework, while section 4 its specialization in the FIRM context; section 5 describes empirical studies we executed, results and discussions are presented in section 6. Finally, conclusions are drawn in section 7.