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At the same time, Simon Stewart at ThoughtWorks developed a superior browser automation tool called WebDriver. In 2009, after a meeting between the developers at the Google Test Automation Conference, it was decided to merge the two projects, and call the new project Selenium WebDriver, or Selenium 2.0. [7]
Open source framework for writing Integration and functional tests. It includes Arquillian graphene, Drone and Selenium to write tests to the visual layer too. AssertJ [295] Fluent assertions for java beanSpec [296] Behavior-driven development: BeanTest: No [297] A tiny Java web test framework built to use WebDriver/HTMLUnit within BeanShell ...
There are many approaches to test automation, however below are the general approaches used widely: Graphical user interface testing.A testing framework that generates user interface events such as keystrokes and mouse clicks, and observes the changes that result in the user interface, to validate that the observable behavior of the program is correct.
Data: By splitting a single sequential file into smaller data files to provide parallel access; Pipeline: allowing the simultaneous running of several components on the same data stream, e.g. looking up a value on record 1 at the same time as adding two fields on record 2
A traditional program is usually represented as a series of text instructions, which is reasonable for describing a serial system which pipes data between small, single-purpose tools that receive, process, and return. Dataflow programs start with an input, perhaps the command line parameters, and illustrate how that data is used and modified ...
In computing, a pipeline or data pipeline [1] is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements. Computer-related pipelines ...
Lambda architecture depends on a data model with an append-only, immutable data source that serves as a system of record. [2]: 32 It is intended for ingesting and processing timestamped events that are appended to existing events rather than overwriting them. State is determined from the natural time-based ordering of the data.
Google Cloud Dataflow was announced in June, 2014 [3] and released to the general public as an open beta in April, 2015. [4] In January, 2016 Google donated the underlying SDK, the implementation of a local runner, and a set of IOs (data connectors) to access Google Cloud Platform data services to the Apache Software Foundation. [5]