Dirk Riehle's Industry and Research Publications

Category: 3.1 Research (General)

  • Making your research work practical for industry use

    Making your research work practical for industry use

    Most research articles are written for other researchers, leaving “popularization” to further publications (that for the most part never happen). What if you were able to capture and present your research work in such a way that it furthers your research and speaks to practitioners as well? After all, without application in practice, there will…

  • An empirical study of Jayvee, a domain-specific language for data engineering, on understanding data pipeline architectures [SP&E Journal]

    An empirical study of Jayvee, a domain-specific language for data engineering, on understanding data pipeline architectures [SP&E Journal]

    Abstract A large part of data science projects is spent on data engineering. Especially in open data contexts, data quality issues are prevalent and are often tackled by non-professional programmers. We introduce and evaluate Jayvee, a domain-specific language for data engineering aimed at reducing barriers to building data pipelines. We show that a structured DSL…

  • A systematic literature review of pre-requirements specification traceability [RE Journal]

    A systematic literature review of pre-requirements specification traceability [RE Journal]

    Abstract Requirements traceability (RT) is the ability to link requirements to other software development artifacts. In pre-requirements (pre-RS) traceability, requirements are linked to their origin, such as interviews with stakeholders, meeting protocols, or legacy systems. Compared with post-RS traceability, which links requirements to source code and other later artifacts, pre-RS traceability has seen much less…

  • A systematic analysis of problems in open collaborative data engineering [TSC Journal]

    A systematic analysis of problems in open collaborative data engineering [TSC Journal]

    Abstract Collaborative workflows are common in open-source software development. They reduce individual costs and improve the quality of work results. Open data shares many characteristics with open-source software as it can be used, modified, and redistributed by anyone, for free. However, in contrast to open-source software engineering, collaborative data engineering on open data lacks a…

  • Incentivizing German universities to capitalize on their intellectual property

    Incentivizing German universities to capitalize on their intellectual property

    In a recent position paper, SPRIN-D, an innovation agency of the German government, proposed that universities license their intellectual property (IP) to university startups in return for virtual shares. This approach is suggested as a practice to work around universities who stall startup licensing deals due to unrealistic assumptions (e.g. lump-sum upfront payment). The problem…

  • Course on commercial open source startups at UC Santa Cruz

    Course on commercial open source startups at UC Santa Cruz

    In September 2020, I will be teaching a workshop series on commercial open source startups at UC Santa Cruz (and starting November, as a course, at FAU). The series at UCSC is being faciliated by CROSS, the Center for Research in Open Source Software, and I’m getting help from Thomas Otter (@vendorprisey). If you would…