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

  • Engineering AI systems – differences to engineering “other” software systems… July 3, 2020
    https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9121629 Being a software engineer working with AI for a while, I noticed that the engineering of AI systems is different. Well, maybe not building the actual system, but the way in which the knowledge about quality, testing and maintenance differ. In this article, IEEE Software’s Editor in Chief presents her view on the topic. […]
    metrics
  • Problems with engineering AI systems June 29, 2020
    https://link-springer-com.ezproxy.ub.gu.se/article/10.1007/s10994-020-05872-w Engineering machine learning systems is much more than train-evaluate cycles. It means that we need to systematically integrate these ML systems with the rest of the component. We need to build safety-cages to ensure that the decisions are not out-of-bounds and we need to make sure that we can maintain these systems. In this […]
    metrics
  • Actionable software metrics – an interesting new article June 26, 2020
    https://dl.acm.org/doi/pdf/10.1145/3383219.3383244 Working with metrics is a domain which calls for empirical data, which constantly changes. Software companies evolve and their metric programs evolve. I’ve always been interested how the metrics data is used in companies, especially in other geographical regions than the nordics. Although there are differences between companies in Sweden, Danmark or Finland, these […]
    metrics
  • Midsummer reading – stumbling on happiness June 21, 2020
    I picked up this book to get some new perspective on research, work-life balance and, eventually, happiness. Not that I’m miserable, but I got intrigued by the recent developments in psychology and I wanted to take this as a bedtime reading. Midsummer reading, to be exact. Well, the book is a great literature for that, […]
    metrics
  • What to discuss about Deep Learning? – an EMSE article review June 18, 2020
    A study about what the developers discuss regarding Deep Learning and whether this differs across different frameworks is an interesting summer discussion topic: https://doi-org.ezproxy.ub.gu.se/10.1007/s10664-020-09819-6 The paper reviews the comments of developers who comment and/or post questions about three deep learning frameworks: Theano, Tensorflow and PyTorch. I’ve got interested in the paper because I wanted to […]
    metrics

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