Abstract
This paper presents a congruence measurement method by partitions to apply software trustworthiness measures in dynamic behavior feature datasets. The datasets are generated at software running time. And the method compares the datasets with the static attribute feature datasets generated at software testing time. So this method can make recommendations for users in services selection time under the environment of SaaS. The measurement method is carried out in three stages: firstly, defining the concept of trust, software trustworthiness, static and dynamic feature datasets with fundamental calculating criteria; secondly, providing a group of formulas to illustrate congruence measurement approach for comparing the two types of feature datasets; lastly, giving an architecture supported by software trustworthiness measurement algorithm to evaluate conceptualized hierarchical software trustworthiness.
Highlights
Trust is essential to most human transactions[1] as well as for Internet based software applications
Testing[4] and data mining techniques[5] can be used to analyze different types of software engineering data to substantially assist in building software trustworthiness[6, 7, 8]
We propose a measurement method for software trustworthiness based on black box testing and data mining techniques to support trustworthiness measurement for Internet-based software[9, 10]
Summary
Trust is essential to most human transactions[1] as well as for Internet based software applications. Numerous research papers have addressed trust and software trustworthiness in recent years, but mainly from a security point of view. We consider trust as a subjective concept sourced from the human mind, and related to this, software trustworthiness as an objective concept, a comprehensive characteristic in Cloud Computing[2,3]. We propose a measurement method for software trustworthiness based on black box testing and data mining techniques to support trustworthiness measurement for Internet-based software[9, 10].
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