A Lightweight Architecture Analysis of a Monolithic Messaging Gateway

Brahneborg, Daniel; Afzal, Wasif

doi:10.1109/icsa-c50368.2020.00013

Cited by 1 publication

(1 citation statement)

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“…If it also affects one or more other quality attributes, particularly in a different direction, it is called a trade-off point. By letting the stakeholders prioritize the quality attributes involved in trade-off points, any architectural elements that may need to be changed are easily found [15]. The process is iterated as needed.…”

Section: A Saam Atam and Related Methodsmentioning

confidence: 99%

ALCEA: The Architecture Life-Cycle Effect Analysis Method

Axelsson,

Bilic,

Brahneborg

et al. 2024

IEEE Open J. Syst. Eng.

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This paper describes the Architecture Life Cycle Effect Analysis (ALCEA) method, a structured method for evaluating proposed new architectures for software-intensive systems. The method evaluates a proposed architecture by quantifying its effect on the performance of system life-cycle phases. The method is instantiated by identifying (i) the relevant life-cycle phases of the system under investigation and (ii) a set of evaluation functions that capture, in terms of basic factors, the effect of different architectural decisions on key life-cycle performance attributes, such as revenue, operating resources, and investments. The method results in a transparent cost and revenue structure, documented in a tabular form, based on quantifiable factors from the developing organization. The results of the method can be used directly as part of a business case, and their robustness can be estimated by sensitivity analysis. The ALCEA method is designed for system-level architectural analysis, covering both software and hardware aspects. In this paper, we introduce the ALCEA method and provide a detailed example of how to apply it in the evolution of embedded systems. Moreover, we share early experiences of using the method in large-scale industrial settings.

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Section: A Saam Atam and Related Methodsmentioning

confidence: 99%