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Register a new userCapabilities Engineering: Constructing Change-Tolerant Systems
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Added by
Ramya Ravichandar
Publication date
2006
fields
Informatics Engineering
and research's language is
English
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We propose a Capabilities-based approach for building long-lived, complex systems that have lengthy development cycles. User needs and technology evolve during these extended development periods, and thereby, inhibit a fixed requirements-oriented solution specification. In effect, for complex emergent systems, the traditional approach of baselining requirements results in an unsatisfactory system. Therefore, we present an alternative approach, Capabilities Engineering, which mathematically exploits the structural semantics of the Function Decomposition graph - a representation of user needs - to formulate Capabilities. For any given software system, the set of derived Capabilities embodies change-tolerant characteristics. More specifically, each individual Capability is a functional abstraction constructed to be highly cohesive and to be minimally coupled with its neighbors. Moreover, the Capability set is chosen to accommodate an incremental development approach, and to reflect the constraints of technology feasibility and implementation schedules. We discuss our validation activities to empirically prove that the Capabilities-based approach results in change-tolerant systems.
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AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image- and speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.
Model-based systems engineering (MBSE) provides an important capability for managing the complexities of system development. MBSE empowers the formalisms of system architectures for supporting model-based requirement elicitation, specification, design, development, testing, fielding, etc. However, the modeling languages and techniques are quite heterogeneous, even within the same enterprise system, which creates difficulties for data interoperability. The discrepancies among data structures and language syntaxes make information exchange among MBSE models even more difficult, resulting in considerable information deviations when connecting data flows across the enterprise. For this reason, this paper presents an ontology based upon graphs, objects, points, properties, roles, and relationships with entensions (GOPPRRE), providing meta models that support the various lifecycle stages of MBSE formalisms. In particular, knowledge-graph models are developed to support unified model representations to further implement ontological data integration based on GOPPRRE throughout the entire lifecycle. The applicability of the MBSE formalism is verified using quantitative and qualitative approaches. Moreover, the GOPPRRE ontologies are generated from the MBSE language formalisms in a domain-specific modeling tool, textit{MetaGraph} in order to evaluate its availiablity. The results demonstrate that the proposed ontology supports both formal structures and the descriptive logic of the systems engineering lifecycle.
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