Complex, end-to-end processes can be easily automated by combining the Robotic Process Automation (RPA) and Artificial Intelligence (AI) platforms. This will in turn help humans to work more efficiently. Companies striving for scaling their digital transformation activities can rely on these two (RPA and AI) most sought after technological platforms. In this article, you will get to know the significance of making AI and RPA work in a combined manner. What is Artificial Intelligence (AI)? It is a technological platform through which human intelligence can be simulated by computer systems. Cognitive functions of the human mind such as self-correction, problem-solving etc., are mimicked by the AI platform. A few examples of AI include voice recognition software (Amazon’s ‘Alexa’ and Apple’s ‘Siri’), personalization of social media news feeds, spam email filters etc. Machine Learning (ML) is a crucial component of AI, through which specific programs are developed that help robots to lea

  The test automation framework provides a platform for conducting all the test automation activities in an optimal manner and thus improving the efficiency of the testing process. Every software development project has its own specific requirements and scope and hence it becomes important to select the right test automation framework that can truly justify the project needs. In this article, you will get to know the steps required to select the right test automation framework. Following are the six steps required to select the right test automation framework: 1. Analyze the project requirements: The expectations need to be clearly understood before any specific tool is applied in the project. This means specific business and project issues should be solved by the tool. This depends upon the software development methodology and the project scope. Features like reporting and key aspects such as test coverage, project scope and level of software testing (performance testing, unit

Functional testing is a black box type of testing and includes many important types of functional testing such as Smoke testing, sanity testing, system testing, unit testing, regression testing etc. It is mainly concerned and focused on ensuring that all the functions of a software application are strategically verified. User Acceptance Testing is focused on testing the product from a user’s viewpoint, as ultimately users would be using the software product or application post release of the product.  User experience, usability, functionality and performance are the key parameters that are thoroughly checked in UAT. In this article, you will get to know about the differences between functional testing and UAT. What is functional testing? It is a testing method where the application’s basic functionalities are tested against a predetermined set of specifications. Functional tests measure whether the desired output is returned by the given input, regardless of any other details. Thro

The platform of Artificial Intelligence (AI) and Machine Learning (ML) is evolving at a rapid pace and is gaining huge momentum in the IT industry. There are already many industries that are getting immensely benefitted through the strategic implementation of AI and ML. When it comes to utilizing AI in software testing, efficiency, speed of execution and greater accuracy is where AI stands out. In this article, you will get to know how AI and ML are going to impact the future of software testing. Following are the five factors that will justify how Artificial Intelligence (AI) and Machine Learning (ML) will constructively impact the software testing domain: 1. Improved reliability and accuracy: There will be scenarios where QA engineers will not be able to detect certain bugs. From a QA engineer’s perspective, it is generally accepted, as manual testing may contain a certain amount of errors/defects. But, the brand’s image can be at stake, if a consumer notices these bugs/defects/