Towards a Catalog of Enterprise Architecture Smells

Abstract

Code Smells are well known in the domain of Technical Debt (TD). They hint at common bad habits that impair the quality of the software system. By detecting those smells it is possible to suggest a better solution or, at least, make the developers aware of possible drawbacks. However, in terms of Enterprise Architecture (EA), which is a more holistic view of an enterprise including TD, there does not exist such a concept of EA Smells. Such EA Smells can be a component of EA Debt, working like a metric to rate the quality of data and estimate parts of the EA Debt in an EA Repository. The main goal of this work is to start the development of a catalog to facilitate future design and development of EAs. This catalog should be expanded and serve as food for thought to create a corresponding tool for the detection of smells.

Implementation of a continuous delivery pipeline for enterprise architecture model evolution

“…The EAM KPI Catalog [45] proposes a set of 52 KPIs that help to measure and keep track on EA-related goals. Salentin and Hacks [55] present different measures to assess quality flaws of EA models and develop a prototype that can detect 14 of them. Timm et al [63] sketch a framework that allows to assess the EA model quality in accordance with its purpose.…”

“…The term Bad (Code) Smell was introduced by Martin Fowler [21] and stands for bad habits when writing and designing code and software resulting in a decrease of the overall code quality. Hitherto, Salentin et al developed a catalog comprised by a total of 45 EA Smells, and a tool, which can detect 14 of the proposed 45 smells in an ArchiMate model [55]. We adapt the tool in our pipeline and embed it in this activity.…”

“…The pipeline user can setup thresholds for each smell. For further read toward EA smells, we refer the reader to the corresponding paper [55].…”

Using Knowledge Graphs to Detect Enterprise Architecture Smells

“…In particular, we want to automatically and efficiently analyze even large EA models with the aim to detect EA Smells. EA Smells have been recently proposed as a novel and promising research direction [33]. EA Smells are inspired by Code Smells, which are a common means to indicate possible Technical Debts [8].…”

“…While Code Smells analyze source code, EA Smells analyze an organization from a more holistic point of view and go beyond a technical scope. Hitherto, first EA Smells and tool prototypes have been proposed aiming to detect possible flaws in EA models [24,33,41].…”