The Perception of Technical Debt in the Embedded Systems Domain: An Industrial Case Study
Paper i proceeding, 2016

Technical Debt Management (TDM) has drawn the attention of software industries during the last years, including embedded systems. However, we currently lack an overview of how practitioners from this application domain perceive technical debt. To this end, we conducted a multiple case study in the embedded systems industry, to investigate: (a) the expected lifetime of components that have TD, (b) the most frequently occurring types of TD in them, and (c) the significance of TD against run-time quality attributes. The case study was performed on seven embedded systems industries (telecommunications, printing, smart manufacturing, sensors, etc.) from five countries (Greece, Netherlands, Sweden, Austria, and Finland). The results of the case study suggest that: (a) maintainability is more seriously considered when the expected lifetime of components is larger than ten years; (b) the most frequent types of debt are test, architectural, and code debt; and (c) in embedded systems the run-time qualities are prioritized compared to design-time qualities that are usually associated with TD. The obtained results can be useful for both researchers and practitioners: the former can focus their research on the most industrially-relevant aspects of TD, whereas the latter can be informed about the most common types of TD and how to focus their TDM processes.

technical debt

embedded systems

industry

case study

Författare

Areti Ampatzoglou

Rijksuniversiteit Groningen

Apostolos Ampatzoglou

Rijksuniversiteit Groningen

A Chatzigeorgiou

University of Macedonia

Paris Avgeriou

Rijksuniversiteit Groningen

P Abrahamsson

Norges teknisk-naturvitenskapelige universitet

Antonio Martini

Chalmers, Data- och informationsteknik, Software Engineering

U Zdun

Universität Wien

K Systa

Tampereen Yliopisto

8th IEEE International Workshop on Managing Technical Debt (MTD)

2377-8571 (ISSN)

9-16
978-1-5090-3854-1 (ISBN)

Ämneskategorier

Data- och informationsvetenskap

DOI

10.1109/MTD.2016.8

ISBN

978-1-5090-3854-1

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2022-02-02