Background
Technical debt is a metaphor that describes the tradeoff often made between short-term solutions in a software system and the long-term development of it. These kinds of trade-offs can create issues that makes it harder to modify and develop a software system further. This aspect becomes increasingly important as businesses must stay competitive in a changing environment, while aligning IT and business is something that must be addressed continuously. While doing so, businesses that build up on technical debt will meet negative consequences such as increased software development cost, low product quality, decreased maintainability, and slower development.
Since technical debt so far only has been limited to technical aspects, researchers at the Division of Network and Systems Engineering at KTH, have recently proposed a definition to the metaphor “Enterprise Architecture debt (EAD)” to give a more holistic view when addressing technical debt from an enterprise perspective.
Researchers have now taken the firsts steps towards measuring EADs and this degree project will be carried out to elaborate further on such measurements to create a set of measurements for EAD. These could be derived from mapping existing measurements for bad behavior in different domains of Enterprise Architecture (business, data, application, and technical layer).
An example of such bad behavior measurements are code smells and anti-patterns, which aim to describe bad practices such that they can get recognized and dealt with. This project aims to focus on anti-patterns that have been defined to discover data-flow errors. The plan is to map these definitions to suit a more holistic view of EA and further contribute to the management of EADs.
Research Question
What corresponding EA anti-patterns can be defined on the existing data-flow anti-patterns?
Contact Persons
Further Information
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
Related Work
Lehmann, Barry-Detlef; Alexander, Peter; Lichter, Horst; Hacks, Simon
Towards the Identification of Process Anti-Patterns in Enterprise Architecture Models Proceedings Article
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.
@inproceedings{Lehmann2020,
title = {Towards the Identification of Process Anti-Patterns in Enterprise Architecture Models},
author = {Barry-Detlef Lehmann and Peter Alexander and Horst Lichter and Simon Hacks},
url = {http://ceur-ws.org/Vol-2767/06-QuASoQ-2020.pdf},
year = {2020},
date = {2020-12-11},
booktitle = {8th International Workshop on Quantitative Approaches to Software Quality in conjunction with the 27th Asia-Pacific Software Engineering Conference (APSEC 2020)},
volume = {2767},
pages = {47-54},
publisher = {CEUR-WS},
abstract = {IT processes constitute the backbone of an integrated enterprise architecture (EA). The model thereof sustains the development and management of the EA. Nevertheless, the quality of such models tends to degrade over time due to, e.g. improper modeling practices or ineffective evaluation. In this regard, the knowledge of relevant modeling anti-patterns can help identify, mitigate, and prevent the occurrence of sub-optimal or adverse constructs in the model. In the field of business process modeling (BPM), a plethora of BPM anti-patterns has been defined and compiled in various taxonomies. However, these BPM anti-patterns mostly focus on technical issues, which thus are applicable for evaluating workflows but not EA-level processes. We strongly argue that the concept of process anti-pattern in EA domain can facilitate EA analyses on process-related issues. To address this gap, this paper presents a catalogue of 18 EA process modeling anti-patterns, which we derived from the existing BPM anti-patterns. Our result should serve as food for thought and motivation for future research in this context.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
IT processes constitute the backbone of an integrated enterprise architecture (EA). The model thereof sustains the development and management of the EA. Nevertheless, the quality of such models tends to degrade over time due to, e.g. improper modeling practices or ineffective evaluation. In this regard, the knowledge of relevant modeling anti-patterns can help identify, mitigate, and prevent the occurrence of sub-optimal or adverse constructs in the model. In the field of business process modeling (BPM), a plethora of BPM anti-patterns has been defined and compiled in various taxonomies. However, these BPM anti-patterns mostly focus on technical issues, which thus are applicable for evaluating workflows but not EA-level processes. We strongly argue that the concept of process anti-pattern in EA domain can facilitate EA analyses on process-related issues. To address this gap, this paper presents a catalogue of 18 EA process modeling anti-patterns, which we derived from the existing BPM anti-patterns. Our result should serve as food for thought and motivation for future research in this context.
Salentin, Johannes; Hacks, Simon
Towards a Catalog of Enterprise Architecture Smells Book Section
In: Gronau, Norbert; Heine, Moreen; Poustcchi, K; Krasnova, H (Ed.): WI2020 Community Tracks, pp. 276–290, GITO Verlag, 2020, ISBN: 9783955453367.
@incollection{Salentin.2020,
title = {Towards a Catalog of Enterprise Architecture Smells},
author = {Johannes Salentin and Simon Hacks},
editor = {Norbert Gronau and Moreen Heine and K Poustcchi and H Krasnova},
url = {https://dx.doi.org/10.30844/wi_2020_y1-salentin},
doi = {10.30844/wi_2020_y1-salentin},
isbn = {9783955453367},
year = {2020},
date = {2020-03-23},
booktitle = {WI2020 Community Tracks},
pages = {276--290},
publisher = {GITO Verlag},
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.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
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.
