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Auflistung nach Schlagwort "open source software"

1 - 5 von 5
  • Konferenzbeitrag
    Assessing the Usefulness of a Visual Programming IDE for Large-Scale Automation Software
    (Software Engineering 2022, 2022) Wiesmayr, Bianca; Zoitl, Alois; Rabiser, Rick
    This is a summary of a paper (with the same title) that we published at the ACM/IEEE 24th International Conference on Model-Driven Engineering Languages and Systems (MODELS 2021) describing a study centered on a visual programming IDE for large-scale automation software development and maintenance.
  • Zeitschriftenartikel
    Die Innovationstätigkeit der deutschen Softwareindustrie
    (Wirtschaftsinformatik: Vol. 44, No. 2, 2002) Friedewald, Michael; Blind, Knut; Edler, Jakob
    In this article we present the results of two empirical studies focusing on the structure and extent of the innovation activities of the German Software Industry and analyze distinctive features of Software Innovations. We distinguish the activities of the primary (core) Software Industry and secondary industries such as Mechanical and Electrical Engineering, Motor Industry and Telecommunications. A special focus is put on the question if innovations in Software are sequential, on the role of Open Source Software and the importance of interoperability.
  • Zeitschriftenartikel
    From FAIR research data toward FAIR and open research software
    (it - Information Technology: Vol. 62, No. 1, 2020) Hasselbring, Wilhelm; Carr, Leslie; Hettrick, Simon; Packer, Heather; Tiropanis, Thanassis
    The Open Science agenda holds that science advances faster when we can build on existing results. Therefore, research data must be FAIR (Findable, Accessible, Interoperable, and Reusable) in order to advance the findability, reproducibility and reuse of research results. Besides the research data, all the processing steps on these data – as basis of scientific publications – have to be available, too. For good scientific practice, the resulting research software should be both open and adhere to the FAIR principles to allow full repeatability, reproducibility, and reuse. As compared to research data, research software should be both archived for reproducibility and actively maintained for reusability. The FAIR data principles do not require openness, but research software should be open source software. Established open source software licenses provide sufficient licensing options, such that it should be the rare exception to keep research software closed. We review and analyze the current state in this area in order to give recommendations for making research software FAIR and open.
  • Zeitschriftenartikel
    Open Source aus ökonomischer Sicht — Zu den institutionellen Rahmenbedingungen einer spenderkompatiblen Rentensuche
    (Wirtschaftsinformatik: Vol. 45, No. 5, 2003) Franck, Egon
    The analysis focuses on the basic institutional mechanisms governing open source software development. It explains why these institutional mechanisms enable rent-seeking without crowding out donative behavior. The “symbiotic” institutional arrangements translate into an ability to attract contributors with heterogeneous motivations.
  • Konferenzbeitrag
    A Simple NLP-based Approach to Support Onboarding and Retention in Open Source Communities
    (Software Engineering and Software Management 2019, 2019) Stanik, Christoph; Montgomery, Lloyd; Martens, Daniel; Fucci, Davide; Maalej, Walid
    Successful open source communities are constantly looking for new members and helping them become active developers. A common approach for developer onboarding in open source projects is to let newcomers focus on relevant yet easy-to-solve issues to familiarize themselves with the code and the community. The goal of this research is twofold. First, we aim at automatically identifying issues that newcomers can resolve by analyzing the history of resolved issues by simply using the title and description of issues. Second, we aim at automatically identifying issues, that can be resolved by newcomers who later become active developers. We mined the issue trackers of three large open source projects and extracted natural language features from the title and description of resolved issues. In a series of experiments, we optimized and compared the accuracy of four supervised classifiers to address our research goals. Random Forest, achieved up to 91% precision (F1-score 72%) towards the first goal while for the second goal, Decision Tree achieved a precision of 92% (F1-score 91%). A qualitative evaluation gave insights on what information in the issue description is helpful for newcomers. Our approach can be used to automatically identify, label, and recommend issues for newcomers in open source software projects based only on the text of the issues.