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Filters: Author is Mao, Bifei  [Clear All Filters]
2022-08-03
Deng, Yuxin, Chen, Zezhong, Du, Wenjie, Mao, Bifei, Liang, Zhizhang, Lin, Qiushi, Li, Jinghui.  2021.  Trustworthiness Derivation Tree: A Model of Evidence-Based Software Trustworthiness. 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C). :487—493.
In order to analyze the trustworthiness of complex software systems, we propose a model of evidence-based software trustworthiness called trustworthiness derivation tree (TDT). The basic idea of constructing a TDT is to refine main properties into key ingredients and continue the refinement until basic facts such as evidences are reached. The skeleton of a TDT can be specified by a set of rules, which is convenient for automated reasoning in Prolog. We develop a visualization tool that can construct the skeleton of a TDT by taking the rules as input, and allow a user to edit the TDT in a graphical user interface. In a software development life cycle, TDTs can serve as a communication means for different stakeholders to agree on the properties about a system in the requirement analysis phase, and they can be used for deductive reasoning so as to verify whether the system achieves trustworthiness in the product validation phase. We have piloted the approach of using TDTs in more than a dozen real scenarios of software development. Indeed, using TDTs helped us to discover and then resolve some subtle problems.