Metrics

Vision

All Software Center companies have efficient product development, release and deployment processes.

Mission

We help the companies to design and develop modern measurement methods and tools by utilizing state-of-the-art analytics, AI and machine learning.

We use Action Research to increase the impact and adoption of the results (Action Research in Software Engineering), i.e., we work on-site of the companies.

Over the course of ten years of our collaboration, our theme has resulted in over 50 models and tools. We have also published over 200 papers and books that disseminate the results to the public domain.

Examples of the metrics designed and introduced to the companies:

Release readiness: measuring the number of weeks that the product development team needs to release the product (Agile): Release Readiness Indicator for Mature Agile and Lean Software Development Projects | SpringerLink
Change waves: measuring the impact of a change on software product: Identifying Implicit Architectural Dependencies Using Measures of Source Code Change Waves | IEEE Conference Publication | IEEE Xplore
Defect inflow: predicting the number of defects that the development team needs to handle in the coming weeks: Predicting weekly defect inflow in large software projects based on project planning and test status - ScienceDirect
Code quality: measuring and improving the impact of coding practices on software quality: Recognizing lines of code violating company-specific coding guidelines using machine learning | SpringerLink
Engineering level: measuring the quality of code in a git repository: PHANTOM: Curating GitHub for engineered software projects using time-series clustering (springer.com)
SimSAX project similarity: measuring the similarity of projects, for example to monitor the process evolution: LegacyPro—A DNA-Inspired Method for Identifying Process Legacies in Software Development Organizations | IEEE Journals & Magazine | IEEE Xplore, and Simsax: A measure of project similarity based on symbolic approximation method and software defect inflow - ScienceDirect
MeTEAM: measuring the maturity of software metric teams: MeTeaM—A method for characterizing mature software metrics teams - ScienceDirect
MESRAM: measuring the quality and quantity of measurement programs: MeSRAM – A method for assessing robustness of measurement programs in large software development organizations and its industrial evaluation - ScienceDirect

Projects

Continuous Product and Organizational Performance
Stakeholder Communication
Associated: MicroHRV
Associated: T4AI
Associated: Develop
Finished: Quasar@Car - Quantifying meta-model changes
Finished: VISEE - Verification and Validation of ISO 26262 requirements at the complete EE system level
Finished: Longitudinal Measurement of Agility and Group Development
Finished: Size and Quality between Software Development Approaches
Finished: RAWFP - Resource Aware Functional Programming

From Gutenberg to Google and on to AI May 11, 2026
Link to the book I’m often asked what invention I think is the biggest in human history. I do not have one that is the biggest, but I have a short list: 1) Writing – once we learned how to codify knowledge, our progress accelerated tremendously 2) Computing – once we learned how to make […]
Miroslaw Staron
VECS 2026 — The Era of the AI-Defined Vehicle May 5, 2026
The VECS 2026 conference in Gothenburg has made one thing clear: the transition to Software-Defined Vehicles (SDVs) is no longer a future prediction—it is accelerating rapidly toward total market dominance. I’ve been to both days and it seems that the best time for software is NOW! For a nerdy software engineer like me, this conference […]
Miroslaw Staron
Can You Trust GPT with Your System Design? Testing AI’s Architectural IQ March 30, 2026
Image by Vinson Tan ( 楊祖武 ) from Pixabay https://ieeexplore.ieee.org/document/10978937 We’ve all seen Large Language Models (LLMs) write impressive snippets of code or debug a tricky function. But can an AI actually understand the soul of a system? Can it explain the “why” behind a complex architectural decision? The paper, “Do Large Language Models Contain Software […]
Miroslaw Staron
GenAI: The Architect’s New Brainstorming Buddy, Not a Replacement March 23, 2026
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=11015085&casa_token=5hSfww3AlwIAAAAA:eTn9d1W-p95CJtxwAcvPft_bWZB9R8i6P-d1IZBln6MmSF-En1Q4vKdgbejF8w2klKZYeX1VZx4&tag=1 For years, software architects have operated in an “automation gap.” While developers enjoy robust CI/CD pipelines and automated testing, architects have largely relied on manual whiteboarding and expert intuition. With the rise of Generative AI (GenAI), many wonder: Is the gap finally closing? In this paper, researchers provide a reality check. Their verdict? GenAI […]
Miroslaw Staron
Is Your Microservice Architecture Causing Heartburn? The Cost of Static Chaos on Runtime Speed March 16, 2026
BIld av stux från Pixabay https://cs.gssi.it/catia.trubiani/download/2025-ICSA-Correlation-Architecture-Performance-Antipatterns.pdf In the world of microservices, we often chase the dream of independent deployment, rapid scaling, and resilient services. We focus on the dynamic—the Kubernetes pods autoscaling, the latency spikes, the load balancer metrics. We assume that if we have a robust runtime, our architecture is sound. But this study […]
Miroslaw Staron

Vision

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Projects

Metrics blog

Theme 3, Leader: Miroslaw Staron