A Case Study Investigation of a Lightweight, Systematic Elicitation Approach for Enterprise Architecture Requirements

Rosasco, Nicholas; Dehlinger, Josh

doi:10.1007/978-3-319-27218-4_3

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Similarly, Pulkkinen et al 44 provides a method for establishing collaboration between different stakeholders, both from the IT and business groups, in order to improve EA modelling projects. Rosasco et al 45 defined a systematic method “for eliciting required EA knowledge from stakeholders” which is based on queries that follows the approach of the Vision‐Mission‐Objectives‐Strategy‐Tactics (VMOST). This method is able to document and analyse the elicited EA requirements in a semiautomatic way.…”

Section: Related Workmentioning

confidence: 99%

ArchiRev—Reverse engineering of information systems toward ArchiMate models. An industrial case study

Pérez‐Castillo

Caivano

Ruíz

et al. 2020

J Software Evolu Process

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In the context of digital transformation, companies are increasingly forced to develop, manage and continually align IT and business. Companies have found in Enterprise Architecture (EA) a valuable tool to represent and manage IT and business in a holistic way by establishing connections among technology concerns and business/strategical/motivational ones. EA modelling is one of the most critical tasks in this context to represent accurate models that make the difference in the decision-making and governance processes of the company. In most of the cases, EA models are manually defined by experts which is usually error-prone and time-consuming. Thus, the constant business and IT realignment through manual modelling becomes complex and expensive. This research presents ArchiRev, an extensible reverse engineering method that automate the extraction of EA models (using ArchiMate language) by analysing different information systems artefacts. This proposal has been validated through an industrial case study in the context of a C#-based system of an Italian ship refurbishment company. The study has demonstrated the effectiveness (based in the opinion of some business experts) and efficiency of the method. The main implication is that EA modelling can be accelerated, whereas subjectivity and errors introduced by experts might be reduced.

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Section: Related Workmentioning

confidence: 99%