Prioritize Technical Debt in Large-Scale Systems Using CodeScene

摘要

Large-scale systems often contain considerable amounts of code that is overly complicated, hard to understand, and hence expensive to change. An organization cannot address and refactor all of that code at once, nor should they. Ideally, actionable refactoring targets should be prioritized based on the technical debt interest rate to balance the trade-offs between improvements, risk, and new features. This paper examines how CodeScene, a tool for predictive analyses and visualizations, can be used to prioritize technical debt in a large-scale codebase like the Linux Kernel based on the most likely return on code improvements.