83 research outputs found

    Sustainability of Open Source Software Projects: On the Influence of Technical Interdependencies in Software Ecosystems on Developer Participation

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    In the community-based model of open source software (OSS) development, OSS projects are built and maintained by developers that voluntarily contribute their skills, knowledge, and time, thus making them dependent on their continued participation. Therefore, the question of how projects can attract and retain developers is of major concern for their sustainability. OSS projects are embedded into a complex network of technical interdependent projects that emerges from building upon and reusing existing software components. In these so-called software ecosystems, the issue of sustained participation is not only a concern of a single project but also other dependent projects. However, the role and influence of these interdependencies between projects have so far been neglected by Information Systems researchers. This dissertation thus asks: _How do technical interdependencies in software ecosystems influence the sustainability of open source software projects?_ To answer this question, this dissertation consists of three independent empirical studies that focus on three aspects of how technical interdependencies influence developer participation and thus contribute to the sustainability of open source projects: (1) the ability to attract developers, (2) the influences on developers' participation decision, and (3) the retention of developers in a project. This dissertation finds that OSS projects attract more developers when depending on other projects and their ability to retain developers increases with the number of shared developers with other technical interrelated projects. Furthermore, the participation decisions of developers are also positively influenced by these technical relations. Together, these studies contribute to the body of knowledge on developer participation by highlighting the role of technical interdependencies for the overall sustainability of open source projects

    eLuna : A Co-Design Framework for Mixed Reality Narrative Game- Based Learning

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    De siste tiårs utvidede fokus på læring utenfor skolen har bidratt til økt anvendelse av vitensentre som læringsarena for barn i grunnskole og videregående utdanning. En læringsløype er en type integrert læringsmiljø der de lærende, fysiske installasjoner, og digitale hjelpemidler bidrar til å fremme læringsinnhold og mål. På vitensentre brukes læringsløyper som pedagogisk støtte innen et bredt spekter av pensumplaner og programmer, gjennom å kombinere forskjellige sett av installasjoner og ved å vektlegge forskjellige aspekter av installasjonenes innhold. Siden de er sammensatt av både fysiske installasjoner og digitale hjelpemidler, er læringsløyper blandet virkelighet systemer, der de lærende interagerer med elementer i både den fysiske og virtuelle virkeligheten. Forskning har vist at både narrativ og spillmekanikker er blant de mest effektive komponentene som kan ligge til grunn for at læringsløyper skal kunne oppnå økt fokus på læringsinnhold, og for å engasjere de lærende ved å sette dem i en tilstand av flyt (av engelsk flow). Forskningen som presenteres i denne avhandlingen har som hovedmål å forbedre læring på vitensentre, gjennom å bidra med et co-design-rammeverk for blandet virkelighet narrative spillbaserte læringsløyper som underbygger positive effekter på engasjement, motivasjon, og læring. Narrativ har vært brukt til læring og instruksjon siden forhistorisk tid, og spill for læring har vært teoretisert og anvendt i mennesker i århundrer, i enda større grad etter oppfinnelsen av datamaskiner, og mulighetene bragt på banen gjennom digitale spill. Selv om bade narrative og spill har vært vist å kunne ha positive effekter når anvendt for læring, har forskning på effekter fra narrative spillbasert læring vist variable og motstridende resultater. Mangelen av en felles modell for kategorisering av narrative spill medfører manglende kunnskap relatert til hvordan og under hvilke forutsetninger narrative spill har effekt på læring. På tross av at de fleste studier av narrativ spillbasert læring unnlater å nevne narratologiske modeller, og de som gjør det primært refererer til modeller lånt fra andre media som mangler de nødvendige egenskapene til å kategorisere hendelsesflyten som benyttes i mange spill, finnes det en ludo narrativ variabel modell (LNVM), som er en narratologisk modell som kategorisere alle spill som narrativ. Denne forskningen videreutvikler LNVM, og presenterer en felles modell for kategorisering av narrativ spillbasert læring; eLNVM (fra engelsk: The extended LNVM). Narrative spillbaserte læringsløyper består av interaktive installasjoner og digitale hjelpemidler som belyser læringsmål innenfor pensumprogrammer. Det er derfor nødvendig med deltakelse både fra pedagoger og utviklere når slike læringsløyper skal designes og presenteres til lærende. Forskning viser at det er mangel av modeller, metoder, og rammeverk som myndiggjør pedagoger og utvikleres felles design av spillbasert læring, noe som enten resulterer i tapt fokus på læringsinnhold til fordel for engasjerende spillmekanikk, eller i at underholdningspotensialet i spill blir underordnet læringsmålene. Slike rammeverk må videre kunne skille mellom fysiske og virtuelle elementer for å være anvendbare i blandet virkelighet omgivelser. Forskningen presentert i denne avhandlingen benytter et rammeverk for informasjonssystemer som vitenskapelig metode til å utvikle eLuna co-design-rammeverket for blandet virkelighet narrative spillbaserte læringsløyper som underbygger positive effekter på engasjement, motivasjon, og læring. En systematisk litteraturstudie identifiserte 15 studier som rapporterte effekter fra digitale spillbaserte læringssystemer på engasjement, motivasjon, og læring. Disse systemene ble kategorisert med bruk av eLNVM og sortert basert på deres rapportering for å identifisere karakteristikker av narrative digital spillbasert læring som har positive effekter på engasjement, motivasjon, og læring. Denne forskningen benytter en iterativ design-basert forskningsprosess der karakteristikkene assosiert med de positive effektene legges til grunn for et co-design-rammeverk bestående av en metode og et visuelt språk. Co-design-rammeverket blir deretter utvidet med kapasitet til å separere mellom fysiske og virtuelle elementer i blandet virkelighet omgivelser. Rammeverket blir gjennom prosessen testet i deltakende co-design workshops og evaluert med bruk av varierte metoder, inkludert fokus grupper, intervjuer, spørreskjemaer, tematisk analyse, og heuristisk evaluering. Forskningen som blir presentert i denne doktoravhandlingen resulterer i eLuna co-design-rammeverket for narrative spillbasert læring, som kan bli brukt av pedagoger og utviklere til å lage både narrative digitale spillbaserte læringssystemer, og blandet virkelighet narrative spillbaserte læringsløyper som optimaliserer potensiale for positive effekter på engasjement, motivasjon, og læring.Increased focus on out of school learning over the last decades has led to extended use of science centres as learning arenas for pupils in primary and secondary education. A learning trail is a form of embedded learning environment in which the learners themselves, physical exhibits, and digital companions are elements that promote learning content and goals. When used in science centres, learning trails can combine different sets of exhibits and emphasize various aspects of their content to support learning goals inside a broad range of curricular plans and programs. Being comprised of physical exhibits and digital companions, science centre learning trails are mixed reality systems in which learner interaction occurs in both the physical and virtual domains. Research has shown that narratives and game mechanics are among the most effective components for science centre learning trails to achieve increased focus on the learning content, and to induce flow and engagement in learners. With an aim to contribute to improving science centre learning, the main objective of this research is to develop a co-design framework for mixed reality narrative game-based learning trails that enforce positive effects on engagement, motivation, and learning. Narratives have been used in learning and instruction since prehistoric times, and games for learning have been theorized and applied in human culture for centuries, increasingly so with the advent of the computer, and opportunities provided by digital games. While both narratives and games are shown to have the ability to positively affect learning, research on the effects from narrative game-based learning has shown mixed and contradictory results. The lack of a common model to categorize narrative games has led to a knowledge gap regarding how and under which conditions narrative games have effects on learning. Whereas most studies of narrative game-based learning neglect mentioning a narratological model at all, the ones that do mainly refer to models adapted from different media that lack the capabilities to properly categorize the event flow of many digital games. An exception is the ludo narrative variable model (LNVM), a narratological model that can properly categorize all games as narratives. Building on the LNVM, this research fills this gap with the development of the extended LNVM (eLNVM), a common model to categorize and isolate narratives in digital game-based learning. Narrative game-based learning trails comprise interactive exhibits and digital companions and promote learning goals inside curricular programs. Therefore, they require participation from educator and developer stakeholders to be properly designed and brought to learners. Research has shown that there is a lack of models, methods, or frameworks that empower educators and developers to co-design game-based learning, something which results in either the learning content being lost in the engaging mechanics of the game, or the fun of the games becoming inferior to the learning goals. Furthermore, to be applicable in science centres, such a co-design framework must also distinguish between physical and digital elements in mixed reality environments. Applying an information system research framework as a design science methodology, the eLuna co-design framework for mixed reality narrative game-based learning trails that enforce positive effects on engagement, motivation, and learning was developed. A systematic literature review identified 15 studies that self-reported effects of digital game-based learning systems on engagement, motivation, and learning. These were categorized on the eLNVM and sorted by their self-reported effects to identify what characterizes narrative digital game-based learning systems that positively affect engagement, motivation, and learning. Using an iterative design-based research process these characteristics associated with positive effects were then applied in a co-design framework comprising a method and a visual language, which was later extended with the capabilities to distinguish between physical and virtual elements in mixed reality learning trails. Throughout the process the framework was tested in co-design workshops with stakeholders and evaluated through mixed methods, including focus groups, semi-structured interviews, questionnaires, thematic analysis, and heuristic usability inspection. The research presented in this PhD dissertation contributes the eLuna co-design framework for narrative game-based learning, which empowers educators and developers in the creation of both narrative digital game-based learning and mixed reality narrative game-based learning trails that optimize the potential to induce positive effects on engagement, motivation, and learning.Doktorgradsavhandlin

    Methods for improving entity linking and exploiting social media messages across crises

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    Entity Linking (EL) is the task of automatically identifying entity mentions in texts and resolving them to a corresponding entity in a reference knowledge base (KB). There is a large number of tools available for different types of documents and domains, however the literature in entity linking has shown the quality of a tool varies across different corpus and depends on specific characteristics of the corpus it is applied to. Moreover the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real world applications. In the first part of this thesis I explore an approximation of the difficulty to link entity mentions and frame it as a supervised classification task. Classifying difficult to disambiguate entity mentions can facilitate identifying critical cases as part of a semi-automated system, while detecting latent corpus characteristics that affect the entity linking performance. Moreover, despiteless the large number of entity linking tools that have been proposed throughout the past years, some tools work better on short mentions while others perform better when there is more contextual information. To this end, I proposed a solution by exploiting results from distinct entity linking tools on the same corpus by leveraging their individual strengths on a per-mention basis. The proposed solution demonstrated to be effective and outperformed the individual entity systems employed in a series of experiments. An important component in the majority of the entity linking tools is the probability that a mentions links to one entity in a reference knowledge base, and the computation of this probability is usually done over a static snapshot of a reference KB. However, an entity’s popularity is temporally sensitive and may change due to short term events. Moreover, these changes might be then reflected in a KB and EL tools can produce different results for a given mention at different times. I investigated the prior probability change over time and the overall disambiguation performance using different KB from different time periods. The second part of this thesis is mainly concerned with short texts. Social media has become an integral part of the modern society. Twitter, for instance, is one of the most popular social media platforms around the world that enables people to share their opinions and post short messages about any subject on a daily basis. At first I presented one approach to identifying informative messages during catastrophic events using deep learning techniques. By automatically detecting informative messages posted by users during major events, it can enable professionals involved in crisis management to better estimate damages with only relevant information posted on social media channels, as well as to act immediately. Moreover I have also performed an analysis study on Twitter messages posted during the Covid-19 pandemic. Initially I collected 4 million tweets posted in Portuguese since the begining of the pandemic and provided an analysis of the debate aroud the pandemic. I used topic modeling, sentiment analysis and hashtags recomendation techniques to provide isights around the online discussion of the Covid-19 pandemic

    A Design-Science-Research Approach

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    Neue Organisationsformen, wie evolutionäre Organisationen, bilden in vielen Kooperationsszenarien sozio-technische Konstrukte mit modernen CSCW Anwendungen aus. Daher erfordern Veränderungen dieser sozialen Systeme eine kontinuierliche Anpassung der technischen Tools an die neuen sozialen Konfigurationen. Diese Dissertation ist als Design Science Research (DSR) Projekt konzipiert und addressiert die folgende Forschungsfrage (RQ): “Wie können soziotechnische, evolutionäre Organisationen die Herausforderungen der joint optimization und des organizational choice während ihrer autopoietischen Veränderungsprozesse addressieren?” Die Fallstudie Viva con Agua de St. Pauli e.V. wurde mittels qualitativer und ethnographischer Methoden im Rahmen der entsprechenden DSR Zyklen untersucht. Das Forschungsprojekt fokussiert die Entwicklung von Artefakten indem sowohl eine technische, als auch eine soziale Perspektive eingenommen wird. Aus der technische Perspektive wird die RQ durch eine Microservice-Plattform adressiert. Die Architektur dient der Verteilung von Verantwortlichkeit für die Software in einem heterogenen Netzwerk von Entwickler:innen. Dabei müssen diverse neue Herausforderungen beachtet werden, wie etwa die Verteilung des User Interface. Durch die Betrachtung der RQ aus der sozialen Perspektive wird der USMU Workshop entwickelt. Dieses Artefakt dient der Verbindung der Charakteristika evolutionärer Organisationen mit agiler Software Entwicklung und mit Methoden des partizipativen Designs. Die Studien zeigen, dass beide Artefakte die RQ adressieren. Zudem konnte ich für beide Artefakte wertvolle Verbesserungsmöglichkeiten aufzeigen. Somit motivieren die Ergebnisse den nächsten Schritt des Projekts und die vorliegende Thesis wird Bestandteil des zyklischen Ablaufs eines DSR Projekts.The emergence of new types of organizational structures, such as evolutionary-teal organizations, almost always leads to the development of socio-technical constructs when it comes to working in collaboration with modern CSCW applications. A consequence of this is that the social system’s autopoietic change processes create challenges that compel one to adjust the implementation of the technical tool to the social system’s new configuration. This thesis is structured according to the design science research (DSR) approach and focuses on the research question (RQ): “How can socio-technical evolutionary-teal organizations address the challenges of joint optimization and organizational choice during their autopoietic processes?” For this purpose, the case study Viva con Agua de St. Pauli e.V. is investigated using a qualitative ethnographical approach during the DSR cycles. Addressing the RQ, two artifacts are designed from a technical as well as a social perspective. While the technical perspective primarily investigates the adjustments of technology, the social perspective focuses on the management of change in socio-technical evolutionary-teal organizations. I propose a microservice platform as an artifact that addresses the RQ from a technical perspective. The microservice architecture aims at spreading the responsibility for the software through a heterogeneous ecosystem of developers. The newly designed USMU workshop is addressing the RQ from the social perspective. It strives to intertwine the characteristics of evolutionary-teal organizations with agile software development and participatory design methods. In my studies, I examine the fact that both artifacts can be used to address the RQ. Additionally, I was able to identify valuable improvements for both of my artifacts. Hence, the project follows the lifecycle of a DSR project by reasoning through the results presented here for its next iteration

    Bayesian network analysis of software logs for data-driven software maintenance

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    Software organisations aim to develop and maintain high-quality software systems. Due to large amounts of behaviour data available, software organisations can conduct data-driven software maintenance. Indeed, software quality assurance and improvement programs have attracted many researchers' attention. Bayesian Networks (BNs) are proposed as a log analysis technique to discover poor performance indicators in a system and to explore usage patterns that usually require temporal analysis. For this, an action research study is designed and conducted to improve the software quality and the user experience of a web application using BNs as a technique to analyse software logs. To this aim, three models with BNs are created. As a result, multiple enhancement points have been identified within the application ranging from performance issues and errors to recurring user usage patterns. These enhancement points enable the creation of cards in the Scrum process of the web application, contributing to its data-driven software maintenance. Finally, the authors consider that BNs within quality-aware and data-driven software maintenance have great potential as a software log analysis technique and encourage the community to deepen its possible applications. For this, the applied methodology and a replication package are shared.Junta de Andalucía, Grant/Award Number: P20‐00091; AEI, Grant/Award Number: PID2019‐106758GB‐32/AEI/10.13039/501100011033; Spanish project, Grant/Award Number: PDC2021‐121195‐I00; Spanish Program, Grant/Award Number: BEAGAL18/00064Peer ReviewedPostprint (published version

    Pentti Malaska: Ennalta näkijä, edellä kulkija

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    VisuaLizations As Intermediate Representations (VLAIR) : an approach for applying deep learning-based computer vision to non-image-based data

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    We thank the China Scholarship Council (CSC) for financially supporting my PhD study at University of St Andrews, UK, and NSERC Discovery Grant 2020-04401 (Miguel Nacenta).Deep learning algorithms increasingly support automated systems in areas such as human activity recognition and purchase recommendation. We identify a current trend in which data is transformed first into abstract visualizations and then processed by a computer vision deep learning pipeline. We call this VisuaLization As Intermediate Representation (VLAIR) and believe that it can be instrumental to support accurate recognition in a number of fields while also enhancing humans’ ability to interpret deep learning models for debugging purposes or in personal use. In this paper we describe the potential advantages of this approach and explore various visualization mappings and deep learning architectures. We evaluate several VLAIR alternatives for a specific problem (human activity recognition in an apartment) and show that VLAIR attains classification accuracy above classical machine learning algorithms and several other non-image-based deep learning algorithms with several data representations.Publisher PDFPeer reviewe

    Pentti Malaska: ennalta näkijä, edellä kulkija

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    Pentti Malaska: ennalta näkijä, edellä kulkija

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