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An Empirical Evaluation about Using Models to Improve Preliminary Safety Analysis

Authors: Vilela, Jéssyka;

An Empirical Evaluation about Using Models to Improve Preliminary Safety Analysis

Abstract

Video presentation of the paper "An Empirical Evaluation about Using Models to Improve Preliminary Safety Analysis" to appear at II Workshop de Modelagem e Simulação de sistemas intensivos em Software (II MSSiS) 2020 co-located with CBSOFT 2020. Abstract. Context: Safety analysis is an activity of fundamental importance in the development of safety-critical systems (SCS) to ensure that hazardous situations are properly found and mitigated. Such analysis is performed after a system requirements specification is available. Therefore, it is then worthwhile to investigate specification techniques to detect their strengths and weaknesses with respect to discovering hazards early in the development process. Objective: In this paper, we investigate similarities and differences in the results of a preliminary safety analysis from requirements specified using models in Business Process Modeling Notation (BPMN) and Textual Use Cases (TUC). Method: We adopted a controlled experiment as research method using computer engineering students as subjects. Results: The subjects of BPMN group found more accidents, hazards as well as more causes of hazards. Moreover, they have a higher preference for the template used for safety analysis documentation. Conclusions: The use of BPMN to represent the interactions among actors in a system probably lead to the discovery of more accidents and hazards, but more experiments are necessary to test this hypothesis since the results are not statistically significant.

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Keywords

Safety Analysis, BPMN, Textual Use cases, Safety-Critical Systems, Controlled Experiment

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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