Markus Buschle

A Tool for Enterprise Architecture Analysis Using the PRM Formalism

Enterprise architecture advocates for model-based decision-making on enterprise-wide information system issues. In order to provide decision-making support, enterprise architecture models should not only be descriptive but also enable analysis. This paper presents a software tool, currently under development, for the evaluation of enterprise architecture models. In particular, the paper focuses on how to encode scientific theories so that they can be used for model-based analysis and reasoning under uncertainty. The tool architecture is described, and a case study shows how the tool supports the process of enterprise architecture analysis.

Enterprise architecture availability analysis using fault trees and stakeholder interviews

The availability of enterprise information systems is a key concern for many organisations. This article describes a method for availability analysis based on Fault Tree Analysis and constructs from the ArchiMate enterprise architecture (EA) language. To test the quality of the method, several case-studies within the banking and electrical utility industries were performed. Input data were collected through stakeholder interviews. The results from the case studies were compared with availability of log data to determine the accuracy of the methods predictions. In the five cases where accurate log data were available, the yearly downtime estimates were within eight hours from the actual downtimes. The cost of performing the analysis was low; no case study required more than 20 man-hours of work, making the method ideal for practitioners with an interest in obtaining rapid availability estimates of their enterprise information systems.

A Tool for automatic Enterprise Architecture modeling

Enterprise Architecture is an approach which aims to provide decision support based on organization-wide models. The creation of these models is however cumbersome as multiple aspects of an organization need to be considered. The Enterprise Architecture approach would be significantly less demanding if data used to create the models could be collected automatically.

P

This paper presents the Predictive, Probabilistic Cyber Security Modeling Language (P

^{2}

^{2}

CySeMoL: Predictive, Probabilistic Cyber Security Modeling Language

CySeMoL), an attack graph tool that can be used to estimate the cyber security of enterprise architectures. P

P2AMF: Predictive, Probabilistic Architecture Modeling Framework

^{2}

Automatic data collection for enterprise architecture models

CySeMoL includes theory on how attacks and defenses relate quantitatively; thus, users must only model their assets and how these are connected in order to enable calculations. The performance of PCySeMoL enables quick calculations of large object models. It has been validated on both a component level and a system level using literature, domain experts, surveys, observations, experiments and case studies.

A Tool for Interoperability Analysis of Enterprise Architecture Models using Pi-OCL

In the design phase of business and software system development, it is desirable to predict the properties of the system-to-be. Existing prediction systems do, however, not allow the modeler to express uncertainty with respect to the design of the considered system. In this paper, we propose a formalism, the Predictive, Probabilistic Architecture Modeling Framework (P2AMF), capable of advanced and probabilistically sound reasoning about architecture models given in the form of UML class and object diagrams. The proposed formalism is based on the Object Constraint Language (OCL). To OCL, P2AMF adds a probabilistic inference mechanism. The paper introduces P2AMF, describes its use for system property prediction and assessment, and proposes an algorithm for probabilistic inference.

A Tool for Enterprise Architecture Analysis of Maintainability

Enterprise Architecture (EA) is an approach used to provide decision support based on organization-wide models. The creation of such models is, however, cumbersome as multiple aspects of an organization need to be considered, making manual efforts time-consuming, and error prone. Thus, the EA approach would be significantly more promising if the data used when creating the models could be collected automatically—a topic not yet properly addressed by either academia or industry. This paper proposes network scanning for automatic data collection and uses an existing software tool for generating EA models (ArchiMate is employed as an example) based on the IT infrastructure of enterprises. While some manual effort is required to make the models fully useful to many practical scenarios (e.g., to detail the actual services provided by IT components), empirical results show that the methodology is accurate and (in its default state) require little effort to carry out.