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A likelihood-free Bayesian derivation method for service variants

Rune Rasmussen,A. Barros,Fuguo Wei

Published 2018 in Journal of Systems and Software

ABSTRACT

Abstract Application programming interfaces (API), allowing systems to be accessed by the services they expose, have proliferated on the Internet and gained strategic interest in the IT industry. However, integration opportunities for larger, enterprise systems are hampered by complex and overloaded operations of their interfaces, having hundreds of parameters and multiple levels of nesting, corresponding to multiple business entities. Static (code) analysis techniques have been proposed to analyse service interfaces of enterprise systems. They support the derivation of business entities and relationships from the parameters of interface operations, allowing the restructure of operations, based on individual entities. In this paper, we extend the repertoire of static interface analysis to derive service variants, whereby subsets of operation parameters correspond to multiple nested business entity subtypes of variants. Specifically, we apply a Monte Carlo sampling method, based on likelihood-free Bayesian sampling, to traverse large parameter spaces, based on higher probabilistic tree search, to efficiently find subsets of parameters related to prospective subtypes. The results demonstrate a method with significant success rates in massive search spaces, as applied to the FedEx Shipment interface whose operations have in excess of 1000 parameters.

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