The Experts below are selected from a list of 36 Experts worldwide ranked by ideXlab platform

Jin Maozhong - One of the best experts on this subject based on the ideXlab platform.

  • research of software Dependability Requirement specification method based on goal
    Computer Engineering, 2007
    Co-Authors: Jin Maozhong
    Abstract:

    Analyzing and defining the consistent Requirements of software Dependability is critical to the development of complex dependable software systems.How to solve this problem directly influences the quality of Requirements specification,as well as the final software production.Based on a widely-recognized management framework of NFR,this paper points out one Requirement specification method of software Dependability,which utilized the B AMN and thinking of goal-oriented theory.Such method can be named as the software profile of Dependability.It may be applied in the UML area and also provides the formal input for the B proof though the B AMN.

Carol Smidts - One of the best experts on this subject based on the ideXlab platform.

  • Causal Mechanism Graph ─ A new notation for capturing cause-effect knowledge in software Dependability
    Reliability Engineering & System Safety, 2017
    Co-Authors: Fuqun Huang, Carol Smidts
    Abstract:

    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.

Fuqun Huang - One of the best experts on this subject based on the ideXlab platform.

  • Causal Mechanism Graph ─ A new notation for capturing cause-effect knowledge in software Dependability
    Reliability Engineering & System Safety, 2017
    Co-Authors: Fuqun Huang, Carol Smidts
    Abstract:

    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.

Zhendong Niu - One of the best experts on this subject based on the ideXlab platform.

  • Automatically Tracing Dependability Requirements via Term-Based Relevance Feedback
    IEEE Transactions on Industrial Informatics, 2018
    Co-Authors: Wentao Wang, Nan Niu, Jing-ru C. Cheng, Arushi Gupta, Zhendong Niu
    Abstract:

    In many critical industrial information systems, tracking a Dependability Requirement is instrumental to the verification and validation (V&V) of security, privacy, and other Dependability concerns. Automated traceability tools employ information retrieval methods to recover candidate links, which saves much manual effort. Integrating relevance feedback (RF) could potentially improve the retrieval effectiveness by soliciting the relevance judgments on a subset of the retrieval results and then incorporating the feedback into subsequent retrieval. However, little is known about how to use RF to trace Dependability Requirements. In this paper, we propose a novel term-based RF algorithm that leverages the term usage context to recommend positive and negative feedback. Experiments on two software datasets show that our algorithm significantly outperforms the contemporary link-based RF tracing method. Our work not only contributes a new solution to Dependability Requirements' V&V, but also enables further automation to reduce the manual effort in the development life cycle of dependable industrial systems.

Tomasz Janowski - One of the best experts on this subject based on the ideXlab platform.

  • ARES - Building a Dependable Messaging Infrastructure for Electronic Government
    The Second International Conference on Availability Reliability and Security (ARES'07), 2007
    Co-Authors: Elsa Estevez, Tomasz Janowski
    Abstract:

    The paper presents the development of a dependable messaging infrastructure for electronic government. Based on a few simple concepts like messages, members and channels, the infrastructure was developed to facilitate the exchange of messages by government agencies in a dependable and automated way. The Dependability Requirement was addressed on various levels: design, development and application. Considering design, the infrastructure comprises a small core offering plain messaging services, a repository of extensions to provide additional services, and a development framework to rigorously specify, implement and verify messaging extensions. Considering development, the infrastructure was build through rigorous use of modeling and analysis in various development stages. Considering applications, government agencies can use the infrastructure to exchange messages through carefully managed logical communication channels and the prudent use of necessary extensions, including extensions to implement required security measures. The paper presents the development and explains why the outcome satisfies the Dependability Requirement