Enterprise Architecture Debt, EAD, has been defined as “the counterpart to Technical Debt” in the domain of Enterprise Architecture. Technical debt, TD, is a metaphor used in software development to describe various parts of the development lifecycle that causes higher costs and lower quality in the long run. As Seaman et al. describe it, technical debts can be seen as a type of debt that may speed up software development in the short run, but such benefit is achieved at the cost of extra work in the future.
Hacks et al. proposed a new metaphor for EAD to provide a more holistic view of TD, that is: “Enterprise Architecture Debt is a metric that depicts the deviation of the currently present state of an enterprise from a hypothetical ideal state.”
Furthermore, Salentin and Hacks suggested a catalog of measurements for EA Smells. Code smells are a component of TD. In short, code smells are parts of the source code that can seemingly be good but can indicate a deeper problem when implemented and impacting design quality. EA Smells is the counterpart of code smells in a holistic view.
Software architectural smells are also a component of TD and are indicators for bad practices or anti-patterns in a software system. In practice, software architecture is changed throughout its lifecycle. This may mean that new architectural design being added and existing one being modified. As the system grows, so will its TD. As Hacks et al. have suggested a catalog of measurements for EA Smells, I would in this research look for software architectural smells in the EAD domain.
What Software Architectural Smells can be adapted to the EAD domain?
EA Smells catalog: https://ba-ea-smells.pages.rwth-aachen.de/ea-smells/
EA Smells prototype: https://git.rwth-aachen.de/ba-ea-smells/program
In: 8th International Workshop on Quantitative Approaches to Software Quality in conjunction with the 27th Asia-Pacific Software Engineering Conference (APSEC 2020), pp. 47-54, CEUR-WS, 2020.
Towards a Catalog of Enterprise Architecture Smells Incollection
In: Gronau, Norbert; Heine, Moreen; Poustcchi, K; Krasnova, H (Ed.): WI2020 Community Tracks, pp. 276–290, GITO Verlag, 2020, ISBN: 9783955453367.