The key component of any successful automated testing process is test automation frameworks. Reduced maintenance costs, testing efforts and a higher return on investment (ROI) for QA teams are just some of the key benefits the offer while optimizing Agile processes. Executives in the software development domain have fostered an extensive understanding of how implementing an automation framework benefits their business and many in this space have started using the term "framework" quite often, knowing how it can become key to the success of software automation project. But still, to many, the question remains – what exactly is a test automation framework and automation script? How does it work and what advantages can the framework bring to the testing process?
Artificial Intelligence and Machine Learning, fondly known as AI & ML respectively, are the hottest buzzwords in the Software Industry today. The Testing community, Service-organisations, and Testing Product / Tools companies have also leaped on this bandwagon. While some interesting work is happening in the Software Testing space, there does seem to be a lot of hype as well. It is unfortunately not very easy to figure out the core interesting work / research / solutions from the fluff around. See my blog post - "ODSC - Data Science, AI, ML - Hype, or Reality?" as a reference.
Nornir, an automation framework that uses Python directly, provides an alternative to other automation frameworks that use their own domain-specific language (DSL). The framework can dispatch tasks to devices and nodes, deal with inventory when the user has host information, and support the writing of plugins. For troubleshooting, users can use existing debug tools directly from Python. Cisco systems engineer Dmitry Figol, a Nornir contributor, says Nornir is more flexible than Red Hat's Ansible, an IT automation language that uses YAML running on top of Python. Nornir can run as a standalone script and print results to the console.
From a developer's standpoint, deep learning is usually a hands-on exercise conducted within a particular modeling framework. Typically, a developer has needed to adapt their own manual coding style to interfaces provided by a specific framework, such as TensorFlow, Apache MXNet, Microsoft Cognitive Toolkit (CNTK), Caffe, Caffe2, Torch and Keras.
Contextual deep learning allows artificial intelligence machines to react in a more natural and intelligent way to the real-world auditory, visual or other type of data. According to Tech Spot, the concept of having a machine capable of reacting in an intelligent way has been until very recently a matter of science fiction. However, this concept is certainly very compelling and scientists were working on transform this into reality. We are now on the verge of creating this new reality. The general public, however, is not yet informed of what concepts such as neural networks, artificial intelligence and deep learning represent.