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Causal Mechanism Graph ─ A new notation for capturing cause-effect knowledge in software dependability

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  • Huang, Fuqun
  • Smidts, Carol

Abstract

Understanding cause-effect relations between concepts in software dependability engineering is fundamental to various research or industrial activities. Cognitive maps are traditionally used to elicit and represent such knowledge; however they seem incapable of accurately representing complex causal mechanisms in dependability engineering. This paper proposes a new notation called Causal Mechanism Graph (CMG) to elicit and represent the cause-effect domain knowledge embedded in experts’ minds or described in the literature. CMG contains a new set of symbols elicited from domain experts to capture the recurring interaction mechanisms between multiple concepts in software dependability engineering. Furthermore, compared to major existing graphic methods, CMG is particularly robust and suitable for mental knowledge elicitation: it allows one to represent the full range of cause-effect knowledge, accurately or fuzzily as one sees fit depending on the depth of knowledge he/she has. This feature combined with excellent reliability and validity poses CMG as a promising method that has the potential to be used in various areas, such as software dependability requirement elicitation, software dependability assessment and dependability risk control.

Suggested Citation

  • Huang, Fuqun & Smidts, Carol, 2017. "Causal Mechanism Graph ─ A new notation for capturing cause-effect knowledge in software dependability," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 196-212.
    • Handle: RePEc:eee:reensy:v:158:y:2017:i:c:p:196-212
    DOI: 10.1016/j.ress.2016.08.020
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    References listed on IDEAS

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