Cautious Adaptation of Defiant Components

Systems-of-systems are formed by the composition of independently created software components. These components are designed to satisfy their individual requirements, rather than the global requirements of the systems-of-systems. We refer to components that cannot be adapted to meet both individual and global requirements as defiant components. In this paper, we propose a cautious adaptation approach which supports changing the behaviour of such defiant components under exceptional conditions to satisfy global requirements, while continuing to guarantee the satisfaction of the components’ individual requirements. The approach represents both normal and exceptional conditions as scenarios; models the behaviour of exceptional conditions as wrappers implemented using an aspect-oriented technique; and deals with both single and multiple instances of defiant components with different precedence order at runtime. We evaluated an implementation of the approach using drones and boats for an organ delivery application conceived by our industrial partners, in which we assess how the proposed approach helps achieve the system-of-systems’ global requirements while accommodating increased complexity of hybrid aspects such as multiplicity, precedence ordering, openness, and heterogeneity

S.O.B (Save Our Budget) - A Simulation-Based Method for Prediction of Acquisition Costs of Constituents of a System-of-Systems

Software economics, acquisition, and pricing are important concerns for Systems-of-Systems (SoS). SoS are alliances of independent software-intensive systems combined to offer holistic functionalities as a result of the constituents interoperability. SoS engineering involves separately acquiring constituents and combining them to form the SoS. Despite the existence of cost prediction techniques, predicting SoS acquisition costs at design-time should also include the analysis of different suppliers of constituents, their respective prices and quality. However, known methods cover only two out of these three parameters.  The main contribution of this article is to present the S.O.B. (Save Our Budget) method, a novel simulation-based method to predict, at design-time, the acquisition cost of constituents, while still considering quality attributes and different suppliers. Results of a case study in the Smart Building domain revealed that S.O.B. method supports a precise prediction of acquisition cost of constituents to build a SoS for that domain. Furthermore, it also contributes to estimate the cost based on a pre-established quality attribute (functional suitability), as well as to support the selection of coalition that exhibits better results through the analysis of cost-benefit ratio.Software economics, acquisition, and pricing are important concerns for Systems-of-Systems (SoS). SoS are alliances of independent software-intensive systems combined to offer holistic functionalities as a result of the constituents interoperability. SoS engineering involves separately acquiring constituents and combining them to form the SoS. Despite the existence of cost prediction techniques, predicting SoS acquisition costs at design-time should also include the analysis of different suppliers of constituents, their respective prices and quality. However, known methods cover only two out of these three parameters. The main contribution of this article is to present the S.O.B. (Save Our Budget) method, a novel simulation-based method to predict, at design-time, the acquisition cost of constituents, while still considering quality attributes and different suppliers. Results of a case study in the Smart Building domain revealed that S.O.B. method supports a precise prediction of acquisition cost of constituents to build a SoS for that domain. Furthermore, it also contributes to estimate the cost based on a pre-established quality attribute (functional suitability), as well as to support the selection of coalition that exhibits better results through the analysis of cost-benefit ratio

A System Core Ontology for Capability Emergence Modeling

To properly understand organizational adaptation and innovation, it is critical to understand the emergence phenomenon, i.e., how the capabilities of a system emerge after changes. However, for this, we should be able to explain systems, their structure, behavior, and capabilities. In pursuit of an understanding of the emergence phenomenon and the nature of those new kinds of systems in organizations, we propose a well-founded system core ontology based on the Unified Foundational Ontology. The ontology is also grounded in system science definitions and disposition theories. For a more integrated explanation of emergence, the proposed ontology considers distinct perspectives of a system, such as its composition, structure, properties, and functions. In the end, we discuss the applications and implications of the proposed ontology on the enterprise architecture area and emergence modeling