Open Collaborative Data -- using OSS principles to share data in SW engineering

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


Reliance on data for software systems engineering is increasing, e.g., to train machine learning applications. We foresee increasing costs for data collection and maintenance, leading to the risk of development budgets eaten up by commodity features, thus leaving little resources for differentiation and innovation. We therefore propose Open Collaborative Data (OCD) - a concept analogous to Open Source Software (OSS) - as a means to share data. In contrast to Open Data (OD), which e.g., governmental agencies provide to catalyze innovation, OCD is shared in open collaboration between commercial organizations, similar to OSS. To achieve this, there is a need for technical infrastructure (e.g., tools for version and access control), licence models, and governance models, all of which have to be tailored for data. However, as data may be sensitive for privacy, anonymization and obfuscation of data is also a research challenge. In this paper, we define the concept of Open Collaborative Data, demonstrate it by map data and image recognition examples, and outline a research agenda for OCD in software engineering as a basis for more efficient evolution of software systems.


Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Software Engineering
Original languageEnglish
Title of host publicationProceedings of the International Conference on Software Engineering
Subtitle of host publicationNew Ideas and Emerging Research track
PublisherIEEE Computer Society
Publication statusAccepted/In press - 2019 May 25
Publication categoryResearch
Event41st International Conference on Software Engineering (ICSE), 2019 - Montreal, Canada
Duration: 2019 May 252019 May 31


Conference41st International Conference on Software Engineering (ICSE), 2019

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Per Runeson


Project: ResearchCollaboration with industry

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