Abstract
Self-adaptive systems need to be designed with respect to threats within their operating conditions. Identifying such threats during the design phase can benefit from the involvement of stakeholders. Using a system model, the stakeholders, who may neither be IT experts nor security experts, can identify threats as a first step towards formulating security requirements. To support it, the modeling language might possess adequate features to support this task. This paper investigates how iconic signs as a feature of an informal modeling language can contribute to eliciting security requirements by non-experts. Taking urban grid as a case, we relate benefits and specifics of using iconic signs to the two modeling challenges: i) reducing the cognitive complexity required to understand and model a system by non-experts, and ii) facilitating the threat identification activity using a system model. Outputs of three experiments suggest that iconic signs do assists in addressing the challenges.
Original language | English |
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Title of host publication | CEUR Workshop Proceedings |
Place of Publication | RWTH Aachen |
Publisher | CEUR |
Pages | 1-15 |
Number of pages | 15 |
Volume | 1796 |
Publication status | Published - 2017 |
Event | 23rd International Working Conference on Requirements Engineering: Foundation for Software Quality 2017 - Essen, Germany Duration: 27 Feb 2017 → 2 Mar 2017 Conference number: 23 https://refsq.org/2017/welcome/ |
Publication series
Name | CEUR Workshop Proceedings |
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Publisher | RWTH Aachen |
Volume | 1796 |
ISSN (Print) | 1613-0073 |
Conference
Conference | 23rd International Working Conference on Requirements Engineering: Foundation for Software Quality 2017 |
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Abbreviated title | REFSQ 2017 |
Country | Germany |
City | Essen |
Period | 27/02/17 → 2/03/17 |
Internet address |
Keywords
- EC Grant Agreement nr.: FP7/318003
- EWI-27732
- SCS-Cybersecurity
- Experiments
- Security Requirements
- Cyber-physical networks
- Electrical network
- Requirements elicitation and analysis
- Smart Grid
- IR-103393