Development metrics

Vision

Support the collaborating partners in increasing their market/technology leadership

Mission

Support the continuous improvement of product performance and organizational efficiency by rapid transfer of cutting edge research into novel ways of working

Stepping stones

  1. Quantitative reporting
  2. Infrastructure and language
  3. Efficiency measurement
  4. Pro-active measurements
  5. Product insight patterns
  6. Simulating new market/technology scenarios

Projects

  • Continuous Product and Organizational Performance
  • Quasar@Car - Quantifying meta-model changes
  • VISEE - Verification and Validation of ISO 26262 requirements at the complete EE system level
  • Longitudinal Measurement of Agility and Group Development
  • Size and Quality between Software Development Approaches
  • RAWFP - Resource Aware Functional Programming

RSS Metrics blog

  • Good storage and traceability in ML4SE October 16, 2019
    In the last post I’ve discussed the need to create good features from the data, and that this trumps the choice of the algorithm. Today, I’ve chosen to share my observations on the need of good data storage for machine learning. It’s a no brainer and everyone data scientist knows that this is important. However, […]
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  • AI Ethics – a programmer’s perspective August 22, 2019
    Image by Tumisu from Pixabay I’ve been working with machine learning for a while and observed the discussion about AI and ethics. From the philosophical perspective the discussion is very much problem-oriented; the discussion is about “paper cuts” from using AI. I’ve recently looked at the article from SDTimes (sdtimes.org) about AI Ethics (https://sdtimes.com/ai/ai-ethics-early-but-formative-days/) and its early days. […]
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  • How do software engineers work with ML? — an interesting paper from Microsoft June 14, 2019
    Machine learning is one of the current hot areas. As AI is believed to be the next big breakthrough, machine learning is the technology behind AI that makes it all possible. However, ML is also a technology, it’s a software algorithm and product that needs to be developed. It’s true that the development of ML […]
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  • Software analytics in the large scale – article review from IEEE Software in the light of our research on software development speed June 12, 2019
    In the latest IEEE Software issue we can find an interesting article from our colleagues in Spain, working on software analytics (https://doi-org.ezproxy.ub.gu.se/10.1109/MS.2018.290101357). Something that has caught my attention is the focus of the platform and visualizations on the code review process. The review speed and the review process are important for software development companies (see […]
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  • Software Analytics or Software Factfulness April 11, 2019
    I’ve recently read Hans Roslund’s book “Factfulness”, which is about the ability to recognize patterns and analyze data in the global scale. The book is about global trends like poverty, education, health, etc. Not much, if anything, about software engineering. However, when reading I constantly thought about its relation to software analytics. In software analytics […]
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Theme 3, Leader: Miroslaw Staron

Miroslaw Staron
Professor, Software Engineering division, Department of Computer Science and Engineering, University of Gothenburg

More information

Miroslaw.Staron@cse.gu.se

Phone: +46 31 772 10 81