Modes for Software Architectures

Hirsch, Dan; Kramer, Jeff; Magee, Jeff; Uchitel, Sebastián

doi:10.1007/11966104_9

Cited by 47 publications

(38 citation statements)

References 8 publications

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“…The first problem pertains to how to establish a relation between belief staleness and its inaccuracy since it heavily depends on the nature of belief data domain (e.g., position may change much faster than ambient light level). The second problem is related to architectural adaptations; for instance, detecting that belief inaccuracy exceeded a given threshold may trigger architecture mode switching [2] resulting in a configuration guaranteeing a safe state.…”

Section: Introductionmentioning

confidence: 99%

Architecture Adaptation Based on Belief Inaccuracy Estimation

Ali

Bureš

Gerostathopoulos

et al. 2014

2014 IEEE/IFIP Conference on Software Architecture

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Abstract-Cyber-physical systems (CPS) are systems of cooperating autonomous components which closely interact with and control the physical environment. Being distributed and typically based on periodic activities, CPS have to cope with the problem that data capturing a distributed state of the system and its environment are inherently inaccurate (they represent belief on the state). In particular, this poses a problem when dependability is being pursued. In this paper we address this issue by modeling belief at the architecture level. In particular, we enhance the architecture by models describing belief inaccuracy over time. We exploit these models to quantify at runtime the impact of belief staleness on its inaccuracy. We then use this quantification to drive architectural adaptation with the aim to increase dependability of the running CPS system.

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Section: Introductionmentioning

confidence: 99%

Architecture Adaptation Based on Belief Inaccuracy Estimation

Ali

Bureš

Gerostathopoulos

et al. 2014

2014 IEEE/IFIP Conference on Software Architecture

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“…Many techniques for synthesizing formal models from MSCs have been proposed but, unlike ours, most (see, e.g., [19], [27], [15]) translate MSCs into software design models rather than system requirements models. Like us, Damas et al [9] synthesize formal requirements models and have techniques for detecting model incompleteness and generating invariants.…”

Section: A Formal Modeling and Analysismentioning

confidence: 99%

High assurance human-centric decision systems

Heitmeyer¹,

Pickett

Breslow

et al. 2013

2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE)

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Abstract-Many future decision support systems will be human-centric, i.e., require substantial human oversight and control. Because these systems often provide critical services, high assurance will be needed that they satisfy their requirements. How to develop "high assurance human-centric decision systems" is unknown: while significant research has been conducted in areas such as agents, cognitive science, and formal methods, how to apply and integrate the design principles and disparate models in each area is unclear. This paper proposes a novel process for developing human-centric decision systems where AI (artificial intelligence) methods-namely, cognitive models to predict human behavior and agents to assist the humanare used to achieve adequate system performance, and software engineering methods, namely, formal modeling and analysis, to obtain high assurance. To support this process, the paper introduces a software engineering technique-formal model synthesis from scenarios-and two AI techniques-a model for predicting human overload and user model synthesis from participant studies data. To illustrate the process and techniques, the paper describes a decision system controlling unmanned air vehicles.

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“…Finally, our idea of managing all context-and mobility-related aspects with statecharts is very close to the concept of modes. Modes has been proposed in [11] to extend the Darwin ADL for modeling Service Oriented Computing systems. Modes are also language primitives in the Architecture&Analysis Description Language (AADL) [1] for modeling Real-Time&Embedded Systems.…”

Section: Related Workmentioning

confidence: 99%

Performance Modeling and Analysis of Context-Aware Mobile Software Systems

Berardinelli

Cortellessa

Marco

2010

Fundamental Approaches to Software Engineering

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Abstract. Context-awareness is becoming a first class attribute of software systems. In fact, applications for mobile devices need to be aware of their context in order to adapt their structure and behavior and offer the best quality of service even in case the (software and hardware) resources are limited. Although performance is a key non-functional property for such applications, existing approaches for performance modeling and analysis fail to capture the characteristics related to the context, thus resulting not suited for this domain.In this paper we introduce a framework for modeling and analyzing the performance of context-aware mobile software systems. The framework allows to model: the software architecture, the context management, the adaptable behaviors and the performance parameters. Such models can then be transformed into performance models for analysis purposes. We tailor an integrated environment for modeling these elements in UML, and we show how to use it for performance analysis purposes. The modeling environment description and the performance analysis are driven by an example in the eHealth domain.

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Modes for Software Architectures

Cited by 47 publications

References 8 publications

Architecture Adaptation Based on Belief Inaccuracy Estimation

Architecture Adaptation Based on Belief Inaccuracy Estimation

High assurance human-centric decision systems

Performance Modeling and Analysis of Context-Aware Mobile Software Systems

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