This paper provides an artifact-pattern-matchingframework and mathematical model to analyze thedynamic behaviors of the SOA solution design in modeldrivenfashion and provide recommendations for optimalsolution pattern enablement for solution artifacts. Theartifact-pattern-matching system can be dynamicallytuned based on the practitioners’ final selections of therecommendations. Specifically, we propose a set ofSolution patterns to guide SOA solution architectsthrough the process of consuming and configuring SOAartifacts for composing SOA solutions. The resultingmulti-dimensional cascading flagging method is alsopresented in this paper. As an example, impact analysispatterns are used as solution patterns to supporttraceability enablement. We present some futuredirections of leveraging reinforcement learningalgorithms to enrich the design quality analytics of SOAsolution.
['Liang-Jie Zhang', 'Zhi-Hong Mao', 'Nianjun Zhou']
Design Quality Analytics of Traceability Enablement in Service-Oriented Solution Design Environment