In the era of the Internet of Things (IoT), every object can be made smart with embedded sensors, and connected to the internet through wireless technologies. The term "smart" was introduced first for the mobile phone, and the term smartphone was used for the first time in 1999. After 2012, smart watches and other wearable devices became popular. The massive data collected with smart phones and wearable devices offer unprecedented opportunities for human behavior modeling, real-time health monitoring, and personalised services. When people think of IoT, phones, watches, and other small devices often spring to mind. However, automobile manufacturers are now embedding into their vehicles Wi-Fi, global positioning system (GPS) and a bunch of sensors that collect data about the vehicle and the driving behavior. Soon, every car will be connected to its manufacturer, to service companies, to insurance carriers, to its drivers, and to the world around it. Gartner predicts that there will be a quarter of a billion connected vehicles by 2020 [1]. Most cars now have over 400 sensors built into them, capturing data every few milliseconds about steering wheel movement, tire pressure, driver actions, speed, GPS position, car wear and tear, and more. Autonomous cars generate dozens of operational data
Testing conceptually consists of three activities: test case generation, test case execution and verdict assignment. Using online testing, test cases are generated and simultaneously executed. This paper presents a framework that automatically generates and executes tests "online" for conformance testing of the Web service composition described in BPEL. The proposed framework considers unit testing and it is based on a timed modeling of BPEL specification, a distributed testing architecture and an online testing algorithm that generates, executes and assigns verdicts to every generated state in the test case.
This paper proposes an approach to test (actively and passively) Web services composition described in BPEL using TGSE (Test Generation, Simulation and Emulation), that is a tool for generating test cases for Communicating Systems (CS). TGSE implements a generic generation algorithm allowing either test cases derivation or traces checking. It supports the description of one or several components with data and temporal constraints. First, in order to model the BPEL behaviors, the timing constraints, and data variables, the BPEL specification is transformed into the Timed Extended Finite State Machines (TEFSM) model. As our framework can handle both active and passive testing, on the one hand test cases are obtained by stimulating the CS. In this case, the exploration is guided by the use of test purposes modeled by TEFSM (a test purpose is considered as a part of the CS). On the other hand, TGSE can check whether a trace is valid according the specification or not. Finally, the Loan Web Service is used as a case study.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.