Consulting
Strategic support for engineering leaders adapting workflows, operating models, quality practices, and decision-making for AI-assisted software development.
Consultancy, training, and research for modern engineering organizations
Helping engineering organizations to keep up with the AI era.
Codalytics helps software engineering leaders redesign practices, code reviews, developer effectiveness, and collaboration models for AI-assisted software development.
Led by Michaela Greiler-Göritzer. Trusted by teams at Microsoft, BMW, Wikimedia, Wix, National Instruments, and others.
Background
Codalytics is built on research, industry experience, and hands-on work with engineering organizations.
Codalytics is founded by Michaela Greiler-Göritzer, a software engineering researcher, consultant, and educator specializing in developer effectiveness, code reviews, and modern engineering practice.
The business combines strategic advisory work, training, and applied research for organizations rethinking how engineering should operate in the AI era.
Michaela holds a PhD in Software Engineering from Delft University of Technology, worked at Microsoft and Microsoft Research, and later served as Head of Research for DX as a consultant from the earliest stage of the company.
Her research helped shape the ideas and products that made DX a category-defining force in software engineering, culminating in its acquisition by Atlassian in 2025 at a reported valuation of about $1 billion.
Offers
Three offers, one view of how engineering systems actually work.
Training
Workshops and leadership sessions that help teams improve code review culture, engineering effectiveness, and sustainable collaboration practices.
Research
Evidence-led studies, assessments, and thought leadership that connect serious research to the practical realities of engineering organizations.
Why now
AI-assisted development is a systems shift, not a tooling upgrade.
Engineering organizations are entering a transition period comparable to the introduction of distributed version control or cloud computing.
AI coding systems dramatically increase implementation speed, but they also change the operating conditions around quality, review, learning, and collaboration.
- Review dynamics
- Architectural consistency
- Knowledge distribution
- Onboarding
- Quality assurance
- Cognitive load
- Engineering collaboration
Traditional engineering practices are no longer sufficient. Organizations need approaches that combine empirical research, practical engineering experience, and organizational understanding.
How We Work
Flexible support for organizations, teams, and leadership groups.
Consulting
Advisory work for leaders navigating technical and organizational change.
- AI-assisted engineering workflows
- Engineering operating models
- Leadership advisory
- Technical organization design
Training
Workshops, leadership sessions, and enablement programs built around real engineering practice.
- Modern code reviews
- Developer effectiveness
- Engineering management sessions
- Team workshops
- Conference and internal talks
- Custom enablement programs
Research
Research collaborations, assessments, and evidence-based work for complex engineering questions.
- Research collaborations
- Organizational assessments
- Specialist partner support
- Keynotes and thought leadership
AI-assisted engineering
AI is not just changing implementation speed.
It changes how engineering teams collaborate, review code, share knowledge, maintain quality, onboard developers, and make architectural decisions.
Many organizations currently optimize only for faster code generation. The more difficult challenge is building engineering systems that remain understandable, maintainable, and sustainable as AI-generated code becomes dominant.
Code reviews
Ownership
Quality control
Developer workflows
Organizational learning
Engineering metrics
Cognitive load
Codalytics helps organizations navigate this transition pragmatically, with the right mix of strategy, training, and research.
Research areas
Empirical software engineering research that improves practices, processes, and developer experience.
Across these areas, the common thread is the same: understanding the human side of software engineering through empirical research, then using that evidence to improve how teams actually work.
Developer Experience
The developer experience work helped shape how the industry talks about DevEx today. It connects the day-to-day reality of developers with outcomes leaders care about, and it directly informed the research foundation behind DX.
DevEx in Action: A Study of Its Tangible Impacts
Research on how developer experience affects productivity, quality, innovation, retention, and technical debt.
An Actionable Framework for Understanding and Improving Developer Experience
Foundational work defining the factors, barriers, and strategies that shape developer experience.
Code Reviews
The code review studies focus heavily on Microsoft and helped reshape how code reviews are understood in practice: not just as a quality gate, but as a socio-technical workflow affecting speed, learning, coordination, and developer experience.
Characteristics of Useful Code Reviews
Large-scale Microsoft study on what makes review feedback genuinely useful to authors.
Code Reviews Do Not Find Bugs
Evidence-backed challenge to simplistic code review best practices and assumptions.
Code Reviewing in the Trenches
Study of review challenges, best practices, and tool needs based on how reviews are done at Microsoft.
Testing
The testing research also centers on how teams really work: complex systems, difficult tradeoffs, and evidence-based ways to improve testing practices without losing quality. This work informed better testing approaches at Microsoft and beyond.
The Art of Testing Less without Sacrificing Quality
Research on reducing large-scale test execution costs while maintaining product quality.
Test Confessions: A Study of Testing Practices for Plug-in Systems
ICSE study on real testing practices, barriers, and compensating strategies in complex systems.
Automated Detection of Test Fixture Strategies and Smells
IEEE work on detecting fixture-related test smells and improving test code quality in practice.
Selected Work
A few places to explore talks, podcast conversations, code review resources, and longer-form writing.
Talks & Keynotes
Selected conference talks, keynotes, and speaking topics.
awesomecodereviews.com
Curated resources for better reviews
SE Unlocked
Podcast conversations on software engineering practice.
Blog posts & Essays
Longer-form writing, research-backed essays, and reflections on software engineering practice.
About
Codalytics is led by Michaela Greiler-Göritzer.
Michaela is a software engineering researcher, consultant, and educator whose work focuses on code review, developer effectiveness, and the organizational side of software engineering.
She has worked at Microsoft and Microsoft Research, holds a PhD in Software Engineering from Delft University of Technology, and has spent years studying how engineering teams build, review, and improve software in practice.
At DX, Michaela served as Head of Research as a consultant from an early stage. The research she led informed the core ideas behind the product and contributed to a company that went on to shape the software engineering industry and was acquired by Atlassian in 2025 for about $1 billion.
Through Codalytics, she works directly with organizations and can also bring in trusted specialists when a project benefits from additional depth.
Work together
For engineering leaders who want a partner, not just a personality.
Codalytics offers consulting, training, research collaborations, and specialist support for organizations rethinking engineering practice in the AI era.
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