Technical Debt Friction for Maintenance Prioritization: An Industrial Multi-Case Study

Abstract

Software-intensive organizations need effective ways to identify where maintenance and refactoring efforts will yield the greatest practical benefit. Although software analytics such as code health, hotspots, and coupling provide valuable signals, they do not always capture the experienced burden of change that slows software evolution in practice. This paper presents a multi-case industrial study of technical debt friction as a prioritization-oriented concept for identifying where technical debt most strongly affects maintenance and evolution. We investigate how practitioners interpret the concept, whether friction-related analysis aligns with perceived maintenance pain points and refactoring needs, and what broader maintenance and evolution insights friction can provide beyond individual refactoring candidates. To this end, we conducted structured walkthrough sessions with practitioners across multiple industrial cases using analysis artifacts including code health, hotspots, coupling, refactoring targets, and socio-technical views. Our findings show that practitioners generally considered technical debt friction useful for reasoning about maintenance burden, especially when interpreted together with complementary technical and socio-technical views. At the file level, friction often aligned with known problematic areas and, in several cases, with files that later received maintenance attention, although its practical relevance depended strongly on context. In addition, our exploratory project-level analysis suggests that friction distributions may reveal broader maintenance and evolution patterns. These results indicate that technical debt friction is promising as a decision-support concept, but most effective when used with contextual knowledge and supporting evidence.