Frequency and Impact of Technical Debt Characteristics in Companies Producing Mechatronic Products

TL;DR

This study investigates the frequency and impact of various types, causes, and symptoms of technical debt in mechatronic companies through expert surveys, revealing discipline-specific patterns and potential for automated identification.

Contribution

It provides the first detailed survey-based analysis of technical debt characteristics across disciplines in mechatronics, highlighting key types and causes with implications for automation.

Findings

Most frequent TD types and causes identified

Discipline-specific differences in TD characteristics

Potential for automated TD detection in mechatronics

Abstract

Complexity of products, volatility in global markets, and the increasingly rapid pace of innovations may make it difficult to know how to approach challenging situations in mechatronic design and production. Technical Debt (TD) is a metaphor that describes the practical bargain of exchanging short-term benefits for long-term negative consequences. Oftentimes, the scope and impact of TD, as well as the cost of corrective measures, are underestimated. Especially for mechatronic teams in the mechanical, electrical, and software disciplines, the adverse interdisciplinary ripple effects of TD incidents are passed on throughout the life cycle. The analysis of the first comprehensive survey showed that not only do the TD types differ in cross-disciplinary comparisons, but different characteristics can also be observed depending on whether a discipline is studied in isolation or in combination with others. To validate the study results and to report on a general consciousness of TD in the disciplines, this follow-up study involves 15 of the 50 experts of the predecessor study and reflects the frequency and impact of technical debt in industrial experts' daily work using a questionnaire. These experts rate 14 TD types, 47 TD causes, and 33 TD symptoms in terms of their frequency and impact. Detailed analyses reveal consistent results for the most frequent TD types and causes, yet they show divergent characteristics in a profound exploration of discipline-specific phenomena. Thus, this study has the potential to set the foundations for future automated TD identification analyses in mechatronics.