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

Summer 2026 – where are we going now? July 13, 2026
Image by Manueldesign20 from Pixabay It’s hard to believe that 2026 is already halfway and the summer is upon us. When I look back at what happens now, I still think that the best time to be a software engineer is now. We get so many cool tools to work with that we do not […]
Miroslaw Staron
Levels of automated code development… July 10, 2026
Image generated by Gemini based on this blow post https://www.mdpi.com/2076-3417/16/10/4788 The practical meaning of automated code generation is shifting rapidly. What was recently categorized as simple “autocomplete” has expanded into complex workflows involving multi-file modifications, test execution, and repository navigation. However, as Zhenhan Chen et al. argue in a recently published article in Applied Sciences, […]
Miroslaw Staron
What are you talking about – one agent asked another… July 3, 2026
Image taken directly from the paper https://arxiv.org/pdf/2605.24138 The Software Engineering (SE) landscape is shifting from LLM-assisted workflows, like copilots, toward Autonomous SE, where multiple specialized AI agents cooperate without a human in the loop. The premise is exciting: a ‘Designer’ agent creates the plan, and a ‘Programmer’ agent implements it. Yet, simply letting agents talk […]
Miroslaw Staron
Can we force LLMs to generate the code we really want? June 18, 2026
Experiment design – from the paper Large Language Models (LLMs) are revolutionary for programming productivity, producing functional code snippets in seconds. However, as software engineers, my co-authors and I know that “functional” is not the same as “well-designed.” LLMs are generally “bottom-up” thinkers; they excel at local syntax but struggle to adhere to higher-level architectural […]
Miroslaw Staron
My prompt is better than your prompt – how to optimize your prompts in the age of agentic AI June 12, 2026
Image generated by Gemini based on the content of this post https://arxiv.org/pdf/2605.19102 Getting Large Language Models (LLMs) to write functional code often feels like casting spells; a slight misphrasing in your prompt can result in a buggy output. This is even more important now that we have agents which work for days on our tasks. […]
Miroslaw Staron

Vision

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Metrics blog

Theme 3, Leader: Miroslaw Staron