Understanding, Measuring, and Managing Software Ecosystems
Complex software systems behave less like machines and more like living ecosystems. They grow, age, and accumulate technical debt when technical, organizational, and social complexity increases without coordination. Volatile teams, aging dependencies, and the rapidly growing use of AI tools accelerate these effects. Traditional engineering approaches often reach their limits in dealing with these challenges.
Our training programs provide a different perspective. Participants learn to understand software systems as ecosystems and to assess and manage their quality, complexity, and risks throughout the entire software lifecycle.
The following topics are covered and can be tailored to your organization’s specific needs:
Software, Teams, and Processes
- How problematic code emerges. Identify the technical, organizational, and social causes of software quality issues before they evolve into structural problems.
- Dependency Analysis. Measure software aging and external dependencies, and learn how to strategically manage the associated risks.
- Maintainability and Resource Allocation. Analyze code and team dynamics to predict maintainability and risk, enabling targeted prioritization of resources.
- Foundations of AI in Software Development. Understand how AI models work, where their limitations lie, and how to use them without introducing new quality risks.
- Team Dynamics and Knowledge Distribution. Learn how knowledge sharing and collaboration patterns influence long-term software quality and sustainability.
Sustainable AI Adoption
- Predictive Maintenance with Machine Learning. Identify risk areas, maintenance needs, and defect patterns early by analyzing real-world software development data.
- Explainable AI for Quality Assessment. Generate transparent and interpretable quality indicators and understand the key factors influencing software quality.
- LLMs, Code Quality, and Complexity. Explore where AI coding assistants can provide value, where they introduce new risks, and how to use them responsibly.
- Quality Management for AI Applications. Learn how software quality assessment and assurance can be improved and streamlined in the age of AI.
Our experts:
Your contact person: Kevin Mallinger, kmallinger@sba-research.org
Photo credit: Niklas Schnaubelt