Human Interaction in Learning Ecosystems based on Open Source Solutions

Technological ecosystems are software solutions based on the integration of heterogeneous software components through information flows in order to provide a set of services that each component separately does not offer, as well as to improve the user experience. In particular, the learning ecosystems are technological ecosystems focused on learning and knowledge management in different contexts such as educational institutions or companies. The ecosystem metaphor comes from biology field and it has transferred to technology field to highlight the evolving component of software. Taking into account the definitions of natural ecosystems, a technological ecosystem is a set of people and software components that play the role of organisms; a series of elements that allow the ecosystem works (hardware, networks, etc.); and a set of information flows that establish the relationships between the software components, and between these and the people involved in the ecosystem. Human factor has a main role in the definition and development of this kind of solutions. In previous works, a metamodel has been defined and validated to support Model-Driven Development of learning ecosystems based on Open Source software, but the interaction in the learning ecosystem should be defined in order to complete the proposal to improve the development process of technological ecosystems. This paper presents the definition and modelling of the human interaction in learning ecosystem

Eight Observations and 24 Research Questions About Open Source Projects: Illuminating New Realities

The rapid acceleration of corporate engagement with open source projects is drawing out new ways for CSCW researchers to consider the dynamics of these projects. Research must now consider the complex ecosystems within which open source projects are situated, including issues of for-profit motivations, brokering foundations, and corporate collaboration. Localized project considerations cannot reveal broader workings of an open source ecosystem, yet much empirical work is constrained to a local context. In response, we present eight observations from our eight-year engaged field study about the changing nature of open source projects. We ground these observations through 24 research questions that serve as primers to spark research ideas in this new reality of open source projects. This paper contributes to CSCW in social and crowd computing by delivering a rich and fresh look at corporately-engaged open source projects with a call for renewed focus and research into newly emergent areas of interest

Dimensions of the University Digital Ecosystem: Validation of the Instrument «University Digital Ecosystem» (UN-DIGECO)

El presente estudio presenta el diseño y validación del instrumento UN-DIGECO (University Digital Ecosystem), orientado a conocer el Ecosistema Digital Universitario, identificando las dimensiones asociadas al uso que hacen los estudiantes de los medios sociales (MMSS) y/o aplicaciones digitales. La validación de contenido y comprensión se efectuó en tres fases: construcción, refinado y validación psicométrica, mediante el análisis factorial confirmatorio, apoyado en el constructo teórico inicial. Se diseñaron ítems –recabando información de instrumentos similares-, acordes a indicadores adscritos a seis dimensiones: aprendizaje, interacción social, creación de contenidos, consumo online, ocio y prácticas lúdicas. La versión preliminar del cuestionario se testó con 25 estudiantes para depurar y redefinir algunos ítems para facilitar su comprensión. Finalmente, el cuestionario constó de 44 ítems y se aplicó a 484 universitarios españoles y colombianos. Se analizó la estructura latente de los ítems mediante un análisis factorial exploratorio y tras analizar la estructura factorial de los indicadores se excluyeron los no pertinentes. Los resultados confirmaron la existencia de seis dimensiones definidas teóricamente a partir de 37 ítems, obteniendo un alfa de Cronbach mayor de 0,8. Concluyendo, se puede afirmar que el instrumento y los indicadores de sus dimensiones presentan adecuadas propiedades psicométricas de validez y confiabilidad para aplicarse en otros contextos. La originalidad de UN-DIGECO radica en la estructuración de la información, al contemplar las seis dimensiones de uso de los MMSS que hacen los universitarios.The purpose of the study was to design and validate the UN-DIGECO (University Digital Ecosystem) instrument, aimed at learning about the use that undergraduates make of social media and/or digital applications. The content and comprehension validation was carried out in three phases: construction, refining and psychometric validation, based on confirmatory factor analysis, supported by the initial theoretical construct. The items, designed by experts, are in line with indicators assigned to six theoretically defined dimensions: Learning, Social Interaction, Content Creation, Online Consumption, Leisure, and Recreational. Own indicators were integrated together with others adapted from similar published instruments. The preliminary version of the questionnaire was tested by a group of 25 students, some items were refined and redefined to facilitate their understanding. The pilot version included 44 items and involved 484 students from Spanish and Colombian universities. The authors analysed the latent structure of the items by applying exploratory factor analysis and, after examining the composition of the factor structure of the indicators, excluded the non-relevant ones. The results confirmed the existence of these six dimensions, which include 37 items, with a Cronbach's alpha greater than 0.8. The instrument and its components show appropriate psychometric properties of validity and reliability, being applicable to other contexts. The originality of the designed questionnaire lies in the structuring of the information, establishing and integrating six dimensions related to the use university students make of SM.peerReviewe

QuESo V2.0 a quality model for open source software ecosystems: List of measures

This technical report is part of QuESo-V2.0 a quality model for open source software ecosystems (OSSECOs). Our prior efforts have focused on providing a detailed list of the quality measures found during the execution of a systematic mapping on OSSECOs. In this new version of the model, we addressing some of the issues that were highlighted in the QuESo V1.0 such as: the unbalanced distribution of measures and the ambiguity of some measures names. The measures listed in this report are not intended to be an exhaustive and complete set. However, this list provides a representative collection of OSSECOs measures. It is a small step in the direction of developing a platform for support the analysis of OSSECO.Postprint (published version

How Do Developers React to API Evolution? The Pharo Ecosystem Case

International audienceSoftware engineering research now considers that no system is an island, but it is part of an ecosystem involving other systems, developers, users, hardware,. .. When one system (e.g., a framework) evolves, its clients often need to adapt. Client developers might need to adapt to functionalities, client systems might need to be adapted to a new API, client users might need to adapt to a new User Interface. The consequences of such changes are yet unclear, what proportion of the ecosystem might be expected to react, how long might it take for a change to diffuse in the ecosystem, do all clients react in the same way? This paper reports on an exploratory study aimed at observing API evolution and its impact on a large-scale software ecosystem, Pharo, which has about 3,600 distinct systems, more than 2,800 contributors, and six years of evolution. We analyze 118 API changes and answer research questions regarding the magnitude, duration, extension, and consistency of such changes in the ecosystem. The results of this study help to characterize the impact of API evolution in large software ecosystems, and provide the basis to better understand how such impact can be alleviated

Open source software ecosystems : a systematic mapping

Context: Open source software (OSS) and software ecosystems (SECOs) are two consolidated research areas in software engineering. OSS influences the way organizations develop, acquire, use and commercialize software. SECOs have emerged as a paradigm to understand dynamics and heterogeneity in collaborative software development. For this reason, SECOs appear as a valid instrument to analyze OSS systems. However, there are few studies that blend both topics together. Objective: The purpose of this study is to evaluate the current state of the art in OSS ecosystems (OSSECOs) research, specifically: (a) what the most relevant definitions related to OSSECOs are; (b) what the particularities of this type of SECO are; and (c) how the knowledge about OSSECO is represented. Method: We conducted a systematic mapping following recommended practices. We applied automatic and manual searches on different sources and used a rigorous method to elicit the keywords from the research questions and selection criteria to retrieve the final papers. As a result, 82 papers were selected and evaluated. Threats to validity were identified and mitigated whenever possible. Results: The analysis allowed us to answer the research questions. Most notably, we did the following: (a) identified 64 terms related to the OSSECO and arranged them into a taxonomy; (b) built a genealogical tree to understand the genesis of the OSSECO term from related definitions; (c) analyzed the available definitions of SECO in the context of OSS; and (d) classified the existing modelling and analysis techniques of OSSECOs. Conclusion: As a summary of the systematic mapping, we conclude that existing research on several topics related to OSSECOs is still scarce (e.g., modelling and analysis techniques, quality models, standard definitions, etc.). This situation calls for further investigation efforts on how organizations and OSS communities actually understand OSSECOs.Peer ReviewedPostprint (author's final draft

Open source software GitHub ecosystem: a SEM approach

Open source software (OSS) is a collaborative effort. Getting affordable high-quality software with less probability of errors or fails is not far away. Thousands of open-source projects (termed repos) are alternatives to proprietary software development. More than two-thirds of companies are contributing to open source. Open source technologies like OpenStack, Docker and KVM are being used to build the next generation of digital infrastructure. An iconic example of OSS is 'GitHub' - a successful social site. GitHub is a hosting platform that host repositories (repos) based on the Git version control system. GitHub is a knowledge-based workspace. It has several features that facilitate user communication and work integration. Through this thesis I employ data extracted from GitHub, and seek to better understand the OSS ecosystem, and to what extent each of its deployed elements affects the successful development of the OSS ecosystem. In addition, I investigate a repo's growth over different time periods to test the changing behavior of the repo. From our observations developers do not follow one development methodology when developing, and growing their project, and such developers tend to cherry-pick from differing available software methodologies. GitHub API remains the main OSS location engaged to extract the metadata for this thesis's research. This extraction process is time-consuming - due to restrictive access limitations (even with authentication). I apply Structure Equation Modelling (termed SEM) to investigate the relative path relationships between the GitHub- deployed OSS elements, and I determine the path strength contributions of each element to determine the OSS repo's activity level. SEM is a multivariate statistical analysis technique used to analyze structural relationships. This technique is the combination of factor analysis and multiple regression analysis. It is used to analyze the structural relationship between measured variables and/or latent constructs. This thesis bridges the research gap around longitude OSS studies. It engages large sample-size OSS repo metadata sets, data-quality control, and multiple programming language comparisons. Querying GitHub is not direct (nor simple) yet querying for all valid repos remains important - as sometimes illegal, or unrepresentative outlier repos (which may even be quite popular) do arise, and these then need to be removed from each initial OSS's language-specific metadata set. Eight top GitHub programming languages, (selected as the most forked repos) are separately engaged in this thesis's research. This thesis observes these eight metadata sets of GitHub repos. Over time, it measures the different repo contributions of the deployed elements of each metadata set. The number of stars-provided to the repo delivers a weaker contribution to its software development processes. Sometimes forks work against the repo's progress by generating very minor negative total effects into its commit (activity) level, and by sometimes diluting the focus of the repo's software development strategies. Here, a fork may generate new ideas, create a new repo, and then draw some original repo developers off into this new software development direction, thus retarding the original repo's commit (activity) level progression. Multiple intermittent and minor version releases exert lesser GitHub JavaScript repo commit (or activity) changes because they often involve only slight OSS improvements, and because they only require minimal commit/commits contributions. More commit(s) also bring more changes to documentation, and again the GitHub OSS repo's commit (activity) level rises. There are both direct and indirect drivers of the repo's OSS activity. Pulls and commits are the strongest drivers. This suggests creating higher levels of pull requests is likely a preferred prime target consideration for the repo creator's core team of developers. This study offers a big data direction for future work. It allows for the deployment of more sophisticated statistical comparison techniques. It offers further indications around the internal and broad relationships that likely exist between GitHub's OSS big data. Its data extraction ideas suggest a link through to business/consumer consumption, and possibly how these may be connected using improved repo search algorithms that release individual business value components