A formal framework for design and analysis of human-machine interaction

Combéfis, Sébastien; Giannakopoulou, Dimitra; Pecheur, Charles; Feary, Michael

doi:10.1109/icsmc.2011.6083933

Cited by 22 publications

(18 citation statements)

References 20 publications

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“…We model HMI systems with an enriched version of labeled transition systems called HMI-LTSs [8]. An HMI-LTS is a directed graph whose edges are labeled with actions.…”

Section: A Human-machine Interaction Labeled Transition Systemsmentioning

confidence: 99%

“…A formal framework to analyze system models has been developed based on the full-control property [8]. The main analysis proposed in the framework consists in the automatic generation of a minimal full-control mental model for a given system model.…”

Section: Detection Of Automation Surprisesmentioning

confidence: 99%

“…The considered method is used for the automatic detection of potential automation surprises and is implemented as a prototype command-line tool. The method is based on models described with enriched labeled transition systems called HMI-LTS [8]. The considered tool with which the formal-methodbased analysis is integrated is Automatic Design and Evaluation Prototyping Toolset (ADEPT) [9].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Detection of Potential Automation Surprises for ADEPT Models

Combéfis¹,

Giannakopoulou

Pecheur

2016

IEEE Trans. Human-Mach. Syst.

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This paper describes how to automatically detect potential automation surprises in interactive systems, within a rapid automation interface design tool named ADEPT. The proposed analysis method in this paper is based on a conformance relation, called full-control, between the model of the actual system and a mental model of it, that is, its behavior as perceived by the operator. The method can, among other things, automatically generate a so-called minimal full-control mental model for a given system. Systems are well designed if they can be described by relatively simple mental models for their operators, which can be assessed with the minimal full-control mental model generation algorithms. During the generation, potential automation surprises are detected and highlighted with execution examples that may lead to confusion. The analysis methods are based on an enriched version of labeled transition systems to describe the system and mental models. In order to be able to integrate the analysis method within ADEPT, a semantics for ADEPT models makes it possible to translate them into enriched LTSs. The proposed translation is automated for a specified class of ADEPT models that are characterized and defined in this paper. A case study demonstrates the proposed analysis framework and informs how the integration with ADEPT can be improved.Index Terms-ADEPT toolset, formal methods, human factors, human-machine interaction.

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“…We model HMI systems with an enriched version of labeled transition systems called HMI-LTSs [8]. An HMI-LTS is a directed graph whose edges are labeled with actions.…”

Section: A Human-machine Interaction Labeled Transition Systemsmentioning

confidence: 99%

Section: Detection Of Automation Surprisesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Detection of Potential Automation Surprises for ADEPT Models

Combéfis¹,

Giannakopoulou

Pecheur

2016

IEEE Trans. Human-Mach. Syst.

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“…Some automated requirement derivation approaches (e.g., [11]) take modes into account. Combefis et al [11] investigate a requirement derivation approach that can track the modes to help identify mode errors.…”

Section: Related Workmentioning

confidence: 99%

“…Combefis et al [11] investigate a requirement derivation approach that can track the modes to help identify mode errors. They do not create views that take advantage of the modes as our modal abstraction view does.…”

Section: Related Workmentioning

confidence: 99%

Modal abstraction view of requirements for medical devices used in healthcare processes

Conboy¹,

Avrunin²,

Clarke³

2013

2013 5th International Workshop on Software Engineering in Health Care (SEHC)

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Abstract-Medical device requirements often depend on the healthcare processes in which the device is to be used. Since such processes may be complex, critical requirements may be specified inaccurately, or even missed altogether. We are investigating an automated requirement derivation approach that takes as input a model of the healthcare process along with a model of the device and tries to derive the requirements for that device. Our initial experience with this approach has shown that when the process and device involve complex behaviors, the derived requirements are also often complex and difficult to understand. In this paper, we describe an approach for creating a modal abstraction view of the derived requirements that decomposes each requirement based on its modes, and thus appears to improve understandability.

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