Characteristics, causes, and consequences of technical debt in the automation domain

Abstract

Technical Debt (TD) is a significant concern in software development, particularly when interdisciplinary teams collaborate and interact. The goal of the study is to investigate TD causal chains and patterns in the industrial automation sector by analyzing 123 mechatronic TD incidents from 47 expert interviews across ten companies. Findings reveal that Requirements, Process, and Test TD are most common, while Build, Versioning, Manufacturing, Code, and Maintenance/Service TD are less frequent. Key causes include ”other priorities”, ”lack of time”, ”historically grown products”, ”lack of market analysis” and ”copy-paste-modify without revising tolerances.” The research identifies correlations between TD subtypes and causes/consequences in relation to company size, experts’ experience, and position, utilizing the Chi-square test and PrefixSpan algorithm. The study also maps the contagious character of TD using Neo4J graphical representation. This first in-depth analysis of TD causal chains in industrial automation contributes qualitatively to understanding TD patterns, helping researchers and practitioners assess TD contagiousness, comprehend its effects, prevent diffusion, and develop repayment strategies To the best of our knowledge, this study’s quantitative analysis approach provides the foundation that will enable future research identifying TD metrics and TD management in multidisciplinary engineering.