Revealing inputs causing web API performance latency using response-time-guided genetic algorithm fuzzing

Web API is the lightweight version to the SOAP (Simple Object Access Protocol) service and usually applies REST (Representational State Transfer) as the architectural style. Nowadays, the service-oriented computing paradigm is shifting from the SOAP services to the Web APIs, i.e., RESTful services. Rather than hard coding from scratch, the methodology of service-oriented computing emphasizes identifying the granularity services and composing services into executable applications. Quality of Service (QoS) is always an important concern in the selection of services. The QoS driven service selection problem for service composition has been proven as NP-Complete since there are nm combinations for a given composite service involving m tasks with n candidate services for each task. To address this issue, this study proposes a methodology based on Genetic Algorithms for the selection of Web APIs. The proposed approach in this research provide several features: 1) ε-Pareto dominance relations are introduced to help the discrimination of composite QoS, 2) the ε value for each dimension is determined by the AHP-based method (AHP: Analytic Hierarchy Process), and 3) the Genetic Algorithm is employed to find out the optimal combinations of Web APIs. Experimental results demonstrate that the proposed approach can effectively locate the combination of Web APIs based on the user-preferred QoS indicators without sacrificing other kinds of service quality.

Genetic algorithm hybridized with eXtreme gradient boosting to predict axial compressive capacity of CCFST columns