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

  • Who and when needs automated code reviews… November 20, 2020
    https://rdcu.be/caKsW Having worked with code reviews for a while, I strongly sympathize with the thesis put forward by the authors of this paper – code review tools are still far from being supporting for software developers. Yes, they do automate the process and organize it. Yes, they help in assuring that all code is reviewed […]
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  • Data analytics in SE November 10, 2020
    https://www.sciencedirect.com/science/article/abs/pii/S0950584920301981 A few years ago, data analytics and big data were super popular in software engineering. In fact, they were a bit too popular, as many authors quoted big data because they had a diagram in the paper. Fast forward to today and the situation is a bit different. We are more mature in using […]
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  • Is confusion a factor when reviewing a code? November 3, 2020
    https://www.win.tue.nl/~aserebre/EMSE2020Felipe.pdf Reviewing the code is an art. After working with the topic for a few years, we’ve realized that this is like reading a chat – one person responds to a piece of message sent by another person. The message often being the code and the response being the review comment. What we’ve discovered is […]
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  • Is noise important in SE? October 27, 2020
    https://www.researchgate.net/profile/Khaled_Al-Sabbagh/publication/344190831_Improving_Data_Quality_for_Regression_Test_Selection_by_Reducing_Annotation_Noise/links/5f5a167aa6fdcc116404d72b/Improving-Data-Quality-for-Regression-Test-Selection-by-Reducing-Annotation-Noise.pdf Image by F. Muhammad from Pixabay Machine learning and deep learning are only as good as the data used to train them. However, even the best data sources can lead to data of non-optimal quality. Noise is one of the exampes of the data problems. Our research team has studied the impact of noise […]
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  • What’s going on in Deep Learning – a literature review (paper review) October 20, 2020
    https://arxiv.org/pdf/2009.06520.pdf Image by Free-Photos from Pixabay Deep learning in software engineering has been used extensively and there is a significant body of research about this topic. In this post, I would like to share my review of the recent systematic review on the use of DL in SE. The interesting finding is the list of […]
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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