Authors:
Jorge López
1
;
Natalia Kushik
2
and
Nina Yevtushenko
3
Affiliations:
1
Université Paris-Saclay, France
;
2
Université Paris-Saclay and Tomsk State University, France
;
3
Tomsk State University and Institute for System Programming of the Russian Academy of Sciences, Russian Federation
Keyword(s):
Systems as Services, Machine Learning, Dynamic Code Analysis, Trust, Software Testing.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Cloud Computing
;
Data Engineering
;
Databases and Data Security
;
Formal Methods
;
Simulation and Modeling
;
Software Engineering
;
Software Engineering Methods and Techniques
Abstract:
The paper is devoted to the trust assessment problem for specific types of software/hardware systems,
namely Systems as Services. We assume that such systems are designed and utilized in all application
domains, and therefore the aspects of trust are becoming crucial. Moreover, these systems are mainly used
on-demand and are often represented by a composition of ‘smaller’ services. Thus, an effective method for
estimating/assessing the trust level of a given component service (or a system as a whole) needs to be
utilized. Most known methods and techniques for trust evaluation mainly rely on the passive testing and
system monitoring; in this paper, we propose a novel approach for this problem taking advantage of active
testing techniques. Test sequences to be applied to a system/service under test are derived based on
determining the critical values of non-functional service parameters. A set of these parameters can be
obtained via a static code analysis of the system/service or by ad
dressing available experts. Machine
learning techniques can be applied later on, for determining critical parameter values and thus, deriving
corresponding test sequences. The paper contains an illustrative example of RESTFul web service which
components are checked w.r.t. critical trust properties.
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