Focusing on What Matters: Explaining Quality Tradeoffs in Software-Intensive Systems via Dimensionality Reduction

Journal article, 2024

Building and operating software-intensive systems often involves exploring decision spaces made up of large numbers of variables and complex relations among them.Understanding such spaces is often overwhelming to human decision makers, who have limited capacity to digest large amounts of information, making it difficult to distinguish the forest through the trees. In this article, we report on our experience in which we used dimensionality reduction techniques to enable decision makers in different domains (software architecture, smart manufacturing, automated planning for service robots) to focus on the elements of the decision space that explain most of the quality variation, filtering out noise, and thus reducing cognitive complexity.