Technical decisions that offer short-term gains but result in long-term disturbances and costs are often made due to the insufficient appreciation or underestimation of their scope, impact, and remedial actions. Technical Debt (TD) is a metaphor that embodies such phenomena and poses a particularly harmful threat when interdisciplinary teams interact and collaborate. The study presents new methods analyzing cross-company TD characteristics and positive TD best practice use cases gathered from 47 semi-structured expert interviews in the industrial automation domain. The three most important life cycle phases, the requirement, design, and testing phases, are addressed. The analysis demonstrates that, like adverse TD incidents, cross-life-cycle ripple effects can be advantageous or disadvantageous to the system. By implementing one measure, the system can benefit in multiple life-cycle phases and even disciplines. Additionally, the measures identified can prevent and eliminate numerous TD types and subtypes. The study elaborates on 31 measures that address 129 TD subtypes and proposes a systematic lessons-learned-based step for managing TD incidents in the automation sector.

Underlying and undiscovered technical debt (TD) that burdens the system and makes future changes more costly or impossible poses significant risks to mechatronic systems. Multi-disciplinary collaboration and cooperation lead to interdisciplinary interfaces and new life cycle phases that cause greater ripple effects to the TD distribution. When quantifying TD contagiousness in interdisciplinary engineering, only a few metrics, methods, or tools prove applicable. In this work, we propose a method containing two key metrics to quantify TD contagiousness across product life cycles and disciplines. Furthermore, we suggest a matrix multiplication method to quantify the adverse impact on each discipline and the system. By applying the methods to the data of three comparable companies in the industrial automation domain, the results enable us to measure and prioritize the TD incidents’ contagiousness. This method provides a first step towards the systematic quantification of TD in the interdisciplinary environment and provides metrics to compare systems based on objective factors.

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.

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.

Digitalization of products and production systems requires a fusion of mechatronic disciplines, where interfaces between mechanical, electrical, and software engineering are inevitable. The increasingly rapid pace of innovations in mechatronic systems triggers decisions being taken under time and cost pressure. At times, compromises in technical solutions are made, neglecting their long-term damage to the system. Technical debt (TD), a concept from software engineering, refers to short-term benefits that lead to long-term negative consequences, e.g., in the form of more difficult maintainability or evolvability. This also applies to mechatronic systems, yet the knowledge of TD characteristics and correlations in the interdisciplinary life cycle has only received little attention. This first comprehensive survey investigates TD in mechatronics systematically and across sectors. 50 experts, of whom 42% hold positions as department heads, from 21 renowned companies and 10 sectors in the German-speaking region supported this study with real scenarios where TD caused damage to their system. 94 informative TD incidents that were classified into twelve TD types were recorded, of which 2/3 have not yet been eliminated and posed a potential risk to the system. TD emerges most frequently in the first three stages of the life cycle, where the consequences rarely remain isolated at their source but are forwarded to later phases and disciplines in the life cycle. In contrast to the research focus in software engineering, the multi-domain analysis of mechatronic TD issues reveals that software engineers are most burdened by Requirements TD and Infrastructure TD in the interdisciplinary environment.