TY - GEN
T1 - Managing Technical Debt in Automation
T2 - 21st IEEE International Conference on Industrial Informatics, INDIN 2023
AU - Bi, Fandi
AU - Vogel-Heuser, Birgit
AU - Huang, Ziyi
AU - Land, Kathrin
AU - Ocker, Felix
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - TD subtype
KW - TD type
KW - automation
KW - correlation analysis
KW - design
KW - industrial best practice
KW - life cycle
KW - requirements
KW - technical debt
KW - technical debt management
KW - testing
UR - http://www.scopus.com/inward/record.url?scp=85171132784&partnerID=8YFLogxK
U2 - 10.1109/INDIN51400.2023.10218034
DO - 10.1109/INDIN51400.2023.10218034
M3 - Conference contribution
AN - SCOPUS:85171132784
T3 - IEEE International Conference on Industrial Informatics (INDIN)
BT - 2023 IEEE 21st International Conference on Industrial Informatics, INDIN 2023
A2 - Dorksen, Helene
A2 - Scanzio, Stefano
A2 - Jasperneite, Jurgen
A2 - Wisniewski, Lukasz
A2 - Man, Kim Fung
A2 - Sauter, Thilo
A2 - Seno, Lucia
A2 - Trsek, Henning
A2 - Vyatkin, Valeriy
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 July 2023 through 20 July 2023
ER -