Identifying and Visualizing Architectural Debt and Its Efficiency Interest in the Automotive Domain: A Case Study
Paper i proceeding, 2015

Architectural Technical Debt has recently received the attention of the scientific community, as a suitable metaphor for describing sub-optimal architectural solutions having short-term benefits but causing a long-term negative impact. We study such phenomenon in the context of Volvo Car Group, where the development of modern cars includes complex systems with mechanical components, electronics and software working together in a complicated network to perform an increasing number of functions and meet the demands of many customers. This puts high requirements on having an architecture and design that can handle these demands. Therefore, it is of utmost importance to manage Architecture Technical Debt, in order to make sure that the advantages of sub-optimal solutions do not lead to the payment of a large interest. We conducted a case study at Volvo Car Group and we discovered that architectural violations in the detailed design had an impact on the efficiency of the communication between components, which is an essential quality in cars and other embedded systems. Such interest is not studied in literature, which usually focuses on the maintainability aspects of Technical Debt. To explore how this Architectural Technical Debt and its interest could be communicated to stakeholders, we developed a visual tool. We found that not only was the Architectural Debt highly interesting for the architects and other stakeholders at VCG, but the proposed visualization was useful in increasing the awareness of the impact that Architectural Technical Debt had on efficiency.

Computer Science

Författare

Antonio Martini

Chalmers, Data- och informationsteknik, Software Engineering

Robert Kaufmann

Chalmers, Data- och informationsteknik

Sam Odeh

Chalmers, Data- och informationsteknik

2015 Ieee 7th International Workshop on Managing Technical Debt (Mtd) Proceedings

33-40

Ämneskategorier

Data- och informationsvetenskap

ISBN

978-1-4673-7378-4

Mer information

Skapat

2017-10-08