130,069 research outputs found

    Coopetition of software firms in Open source software ecosystems

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    Software firms participate in an ecosystem as a part of their innovation strategy to extend value creation beyond the firms boundary. Participation in an open and independent environment also implies the competition among firms with similar business models and targeted markets. Hence, firms need to consider potential opportunities and challenges upfront. This study explores how software firms interact with others in OSS ecosystems from a coopetition perspective. We performed a quantitative and qualitative analysis of three OSS projects. Finding shows that software firms emphasize the co-creation of common value and partly react to the potential competitiveness on OSS ecosystems. Six themes about coopetition were identified, including spanning gatekeepers, securing communication, open-core sourcing and filtering shared code. Our work contributes to software engineering research with a rich description of coopetition in OSS ecosystems. Moreover, we also come up with several implications for software firms in pursing a harmony participation in OSS ecosystems.Comment: This is the author's version of the work. Copyright owner's version can be accessed at https://link.springer.com/chapter/10.1007/978-3-319-69191-6_10, Coopetition of software firms in Open source software ecosystems, 8th ICSOB 2017, Essen, Germany (2017

    Making Sense Of Software Ecosystems: A Critical Review

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    Visualizing software as ecosystems has been an emergent phenomenon. The objective of this paper is to analyze the field of software ecosystems (SECO) and provide a critical review of the existing literature. This research identifies domains and peripheries of a SECO; highlights architectural challenges; examines design and control mechanisms and discusses some of the learning’s from other popular paradigms that can be applied to address the key challenges in the SECO paradigm. This paper also aims to recommend future research directions for software ecosystems and its role in the broader context of information systems research

    Software Startups -- A Research Agenda

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    Software startup companies develop innovative, software-intensive products within limited time frames and with few resources, searching for sustainable and scalable business models. Software startups are quite distinct from traditional mature software companies, but also from micro-, small-, and medium-sized enterprises, introducing new challenges relevant for software engineering research. This paper's research agenda focuses on software engineering in startups, identifying, in particular, 70+ research questions in the areas of supporting startup engineering activities, startup evolution models and patterns, ecosystems and innovation hubs, human aspects in software startups, applying startup concepts in non-startup environments, and methodologies and theories for startup research. We connect and motivate this research agenda with past studies in software startup research, while pointing out possible future directions. While all authors of this research agenda have their main background in Software Engineering or Computer Science, their interest in software startups broadens the perspective to the challenges, but also to the opportunities that emerge from multi-disciplinary research. Our audience is therefore primarily software engineering researchers, even though we aim at stimulating collaborations and research that crosses disciplinary boundaries. We believe that with this research agenda we cover a wide spectrum of the software startup industry current needs

    Novel approaches for managing platform-based ecosystems

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    Digitalization challenges existing organizations and industries. The associated advancement changes the way organizations and their customers interact. This has increasingly fostered the emergence of platforms to facilitate such interaction. Online platforms are software or hardware infrastructures that serve as a foundation and facilitate the interaction between multiple parties (e.g., between organizations and users). Organizations create platforms as part of a larger ecosystem. One major challenge concerns the design of platform-based ecosystems so all participants benefit from their participation. The management of associated relationships with other ecosystem participants is consequently a key challenge and demands according foresight. Platform-based ecosystems are subject to research in the field of information systems. Thus, scientific literature addresses many corresponding research questions and provides valuable insights for both research and practice. However, organizations face numerous challenges when engaging in ecosystems. Such challenges are, e.g., to develop new ecosystems, to incentivize participants to participate in the ecosystem, to cooperate with other participants, and to monitor the ecosystem. In this respect, this doctoral thesis provides a brief overview of platform-based ecosystems and the respective participants therein. Further, the thesis addresses four key challenges in the context of platform-based ecosystems, and proposes novel approaches in order to overcome the challenges. The basis for the novel approaches stems from five research papers. The first and second research paper address the challenge of determining design options when developing new ecosystems via blockchain-enabled initial coin offerings. The papers feature a taxonomy and derive predominant archetypes by drawing on real-world cases. The third research paper addresses the challenge of incentivizing users to participate in platform-based ecosystems. The paper proposes an approach to model financial incentives concerning platform adoption. The fourth research paper proposes an approach to analyze organizational cooperation patterns for the purpose of innovation integration. The developed approach incorporates taxonomy development and enables organizations to determine cooperation characteristics to align the cooperation decision with the cooperation objectives. The fifth research paper addresses the challenge of monitoring customer sentiment on online platforms. The proposed design science research artefact includes a detector of negative sentiment such that organizations are able to identify when a negative sentiment develops, and intervene before users spread the sentiment, e.g., through comments. Each research paper answers a stand-alone research question in the realm of platform-based ecosystems and derives a theoretically founded and separately evaluated research artefact. The artefacts draw on underlying, well-established research methods that allow answering the respective problem statements. Since the problem statements are motived in a practical context, this thesis bridges the gap between a practically oriented problem and a theoretically founded solution. As a result, the derived insights contain a contribution for both, research in the field of Information Systems and practice audience, and encourage the engagement of both domains

    An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry

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    Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems. In this work, we present the first empirical investigation of PTM reuse. We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse. From this data, we model the decision-making process for PTM reuse. Based on the identified practices, we describe useful attributes for model reuse, including provenance, reproducibility, and portability. Three challenges for PTM reuse are missing attributes, discrepancies between claimed and actual performance, and model risks. We substantiate these identified challenges with systematic measurements in the Hugging Face ecosystem. Our work informs future directions on optimizing deep learning ecosystems by automated measuring useful attributes and potential attacks, and envision future research on infrastructure and standardization for model registries

    Scaling Agile Beyond Organizational Boundaries: Coordination Challenges in Software Ecosystems

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    The shift from sequential to agile software development originates from relatively small and co-located teams but soon gained prominence in larger organizations. How to apply and scale agile practices to fit the needs of larger projects has been studied to quite an extent in previous research. However, scaling agile beyond organizational boundaries, for instance in a software ecosystem context, raises additional challenges that existing studies and approaches do not yet investigate or address in great detail. For that reason, we conducted a case study in two software ecosystems that comprise several agile actors from different organizations and, thereby, scale development across organizational boundaries, in order to elaborate and understand their coordination challenges. Our results indicate that most of the identified challenges are caused by long communication paths and a lack of established processes to facilitate these paths. As a result, the participants in our study, among others, experience insufficient responsivity, insufficient communication of prioritizations and deliverables, and alterations or loss of information. As a consequence, agile practices need to be extended to fit the identified needs

    Onboarding in Open Source Software Projects: A Preliminary Analysis

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    Nowadays, many software projects are partially or completely open-source based. There is an increasing need for companies to participate in open-source software (OSS) projects, e.g., in order to benefit from open source ecosystems. OSS projects introduce particular challenges that have to be understood in order to gain the benefits. One such challenge is getting newcom- ers onboard into the projects effectively. Similar challenges may be present in other self-organised, virtual team environments. In this paper we present preliminary observations and results of in-progress research that studies the process of onboarding into virtual OSS teams. The study is based on a program created and conceived at Stanford University in conjunction with Facebook’s Education Modernization program. It involves the collaboration of more than a dozen international universities and nine open source projects. More than 120 students participated in 2013. The students have been introduced to and supported by mentors experienced in the participating OSS projects. Our findings indicate that mentoring is an important factor for effective onboarding in OSS projects, promoting cohesion within distributed teams and maintaining an appropriate pace.Peer reviewe

    Sensemaking Practices in the Everyday Work of AI/ML Software Engineering

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    This paper considers sensemaking as it relates to everyday software engineering (SE) work practices and draws on a multi-year ethnographic study of SE projects at a large, global technology company building digital services infused with artificial intelligence (AI) and machine learning (ML) capabilities. Our findings highlight the breadth of sensemaking practices in AI/ML projects, noting developers' efforts to make sense of AI/ML environments (e.g., algorithms/methods and libraries), of AI/ML model ecosystems (e.g., pre-trained models and "upstream"models), and of business-AI relations (e.g., how the AI/ML service relates to the domain context and business problem at hand). This paper builds on recent scholarship drawing attention to the integral role of sensemaking in everyday SE practices by empirically investigating how and in what ways AI/ML projects present software teams with emergent sensemaking requirements and opportunities
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