Swedish Institute of Computer Science Publications Database
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What can we learn from enterprise architecture models? An experiment comparing models and documents for capability development
No full textEnterprise architecture (EA) has been established as a discipline to cope with the complex interactions of business operations and technology. Models, i.e., formal descriptions in terms of diagrams and views, are at the heart of the approach. Though it is widely thought that such architecture models can contribute to improved understanding and decision making, this proposition has not rigorously been tested. This article describes an experiment conducted with a real EA model and corresponding real traditional documents, investigating whether the model or the documents lead to better and faster understanding. Understanding is interesting to study, as it is a prerequisite to other EA uses. The subjects (N=98) were officer cadets, and the experiment was carried out using a comprehensive description of military Close Air Support capability either (1) in the form of a MODAF model or (2) in the form of traditional documents. Based on the results, the model seems to lead to better, though not faster, understanding
Modeling and analyzing systems-of-systems in the multi-attribute prediction language (MAPL)
No full textThe Multi-Attribute Prediction Language (MAPL), an analysis metamodel for non-functional qualities of systems-of-systems, is introduced. MAPL features analysis in five non-functional areas: service cost, service availability, data accuracy, application coupling, and application size. In addition, MAPL explicitly includes utility modeling to make trade-offs between the qualities. The paper introduces how each of the five non-functional qualities is modeled and quantitatively analyzed based on the ArchiMate standard for enterprise architecture modeling and the previously published Predictive, Probabilistic Architecture Modeling Framework, building on the well-known UML and OCL formalisms. The main contribution of MAPL lies in combining all five non-functional analyses into a single unified framework
Supporting Strategic Decision-Making for Selection of Software Assets
No full textCompanies developing software are constantly striving to gain or keep their competitive advantage on the market. To do so, they should balance what to develop themselves and what to get from elsewhere, which may be software components or software services. These strategic decisions need to be aligned with business objectives and the capabilities and constraints of possible options. These sourcing options include: in-house, COTS, open source and outsourcing. The objective of this paper is to present an approach to support decision-makers in selecting appropriate types of origins in a specific case that maximizes the benefits of the selected business strategy. The approach consists of three descriptive models, as well as a decision process and a knowledge repository. The three models are a decision model that comprises three cornerstones (stakeholders, origins and criteria) and is based on a taxonomy for formulating decision models in this context, and two supporting models (property models and context models)
Using cyber defense exercises to obtain additional data for attacker profiling
No full textIn order to be able to successfully defend an IT system it is useful to have an accurate appreciation of the cyber threat that goes beyond stereotypes. To effectively counter potentially decisive and skilled attackers it is necessary to understand, or at least model, their behavior. Although the real motives for untraceable anonymous attackers will remain a mystery, a thorough understanding of their observable actions can still help to create well-founded attacker profiles that can be used to design effective countermeasures and in other ways enhance cyber defense efforts. In recent work empirically founded attacker profiles, so-called attacker personas, have been used to assess the overall threat situation for an organization. In this paper we elaborate on 1) the use of attacker personas as a technique for attacker profiling, 2) the design of tailor-made cyber defense exercises for the purpose of obtaining the necessary empirical data for the construction of such attacker personas, and 3) how attacker personas can be used for enhancing the situational awareness within the cyber domain. The paper concludes by discussing the possibilities and limitations of using cyber defense exercises for data gathering, and what can and cannot be studied in such exercises
Decision-Making in Automotive Software Development - An Observational Study
No full textThis paper reports results from an independent observational study of an automotive software development research project. The study is carried out as a monitoring activity of the project, which is inexpensive but still representative of real automotive software development cases, thus providing the basis for more rigorous studies. The objective is to take initial steps to improve our understanding of architectural decision-making in the development of software in the automotive domain. The key findings summarize issues surfacing during the development process related to the problem articulation and formulation, the impact of participant experience, the definition of requirements, the decision process, and the effect of the decisions made on the system architecture evolution. The paper offers some insights that can be useful to gain understanding of how decisions are typically made in real settings, i.e., based on gut-feeling, which is important when designing decision support systems for architectural design decisions
IoT Protection Through Device to Cloud Synchronization
No full textThis paper addresses the problem of protecting distributed
IoT units from network based attacks while still having a
high level of availability. In particular we suggest a novel method
where the IoT device execution state is modeled with a suitable
high level application model and where the execution state of the
application of the IoT device is “mirrored” in a cloud executed
machine. This machine has very high availability and high attack
resistance. The IoT device will only communicate with the mirror
machine in the cloud using a dedicated synchronization protocol.
All essential IoT state information and state manipulations are
communicated through this synchronization protocol while all
end application communication directed towards the IoT units
is done towards the mirror machine in the cloud. This gives
a very robust and secure system with high availability at the
price of slower responses. However, for many non-real time IoT
application with high security demands this performance penalty
can be justified
Drifting Off Course – how Sports Technology Can Use Real-Time Data to Add New Dimensions to Sports
No full textWe have built Drift, an application that measures and provides feedback on how far from the ideal path an orienteer has deviated, to study how sports apps can draw on real-time data to enrich sports activities. Orienteering is an outdoor navigation sport requiring mental skills and fast running through the terrain. Participants appropriated deviation into their practice and found new ways it could integrate with common orienteering practice. Interaction around deviation provided possibilities for new forms of sporting practice and social interactions. Deviation as a measure allowed this because it was highly specific and well-grounded in a specific skill of the sport. We believe this use of data in real time has the possibility of supporting and renewing sports activity as well as offering new opportunities for design
A report on design and implementation of protected searchable data in IaaS
No full textIn the first part of this report we present a survey of the state of the art in searchable encryption and its relevance for cloud computing. In particular we focus on the OpenStack open-source cloud platform and investigate which searchable encryption schemes are more amenable for adoption in conjunction with platforms based on OpenStack. Based on that survey we chose one of the schemes to implement and test if it is practical enough to deploy in real systems. On the second part of this report we discuss the results of the implementation
Towards a CBSE Framework for Enhancing Software Reuse: Matching Component Properties Using Semi-Formal Specifications and Ontologies
No full textA novel Component-based Software Engineering (CBSE) framework is proposed in this work that focuses on enhancing the reuse process by offering support for locating appropriate components. The architecture of the framework comprises of five interrelated layers, namely Description, Location, Analysis, Recommendation and Build. The scope of this work is to describe in detail the first and third layers, and provide the means to evaluate the suitability of candidate software components for reuse. The overall aim is to facilitate components’ profiling and offer efficient matching of system and software requirements to increase the reusability potential of components. A specifications profile is created for each component using a semi-formal natural language that describes certain properties. A dedicated parser recognizes parts of the profile and translates them into in-stance values of a dedicated CBSE ontology that encodes these properties. Matching is then performed at the level of ontology instances between the available components and the components required by the developer. The framework recommends components based on a suitability ratio that calculates their distances from the desired properties