{"id":14883,"date":"2020-08-27T20:51:41","date_gmt":"2020-08-27T15:21:41","guid":{"rendered":"https:\/\/coforge.site\/cigniti\/blog\/?p=14883"},"modified":"2022-07-28T15:34:48","modified_gmt":"2022-07-28T10:04:48","slug":"intelligent-test-automation-ai-ml","status":"publish","type":"post","link":"https:\/\/coforge.site\/cigniti\/blog\/intelligent-test-automation-ai-ml\/","title":{"rendered":"Intelligent Test Automation: The core engine of smart processes"},"content":{"rendered":"

Automation. Artificial Intelligence. Machine Learning.<\/span>\u00a0<\/span><\/p>\n

We are not just randomly stating the buzzwords of the era.<\/span>\u00a0<\/span><\/p>\n

Certainly, these trends have caused uproar in the IT industry but as they evolved and advanced, their adoption has become more widespread.<\/span>\u00a0<\/span><\/p>\n

Considering the modern IT landscape, accelerated releases and high customer satisfaction while achieving cost efficiency are the most urgent demands, and yet the most challenging.<\/span>\u00a0<\/span><\/p>\n

With automation, IT organizations have been able to somewhat meet the requirements of speed and quality. However, the automation processes are still rendered with numerous loopholes that leave them inefficient, expensive, and ineffective.<\/span>\u00a0<\/span><\/p>\n

Intelligent Process Automation is a marriage between AI & ML-led processes and the traditional automation practices. This matrimony results in smarter processes with low to no error rate and high efficiency.<\/span>\u00a0<\/span><\/p>\n

As digital transformation adoption and scaling efforts are at a full swing across the global IT scenario, it is critical to consider and embrace future-facing technologies that prepare an organization for whatever is coming next.<\/span>\u00a0<\/span><\/p>\n

McKinsey says \u2013\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cIn essence,\u00a0<\/span>IPA takes the robot out of the human<\/span><\/i>. At its core, IPA is an emerging set of new technologies that combines fundamental process redesign with robotic process automation and machine learning. It is a suite of business-process improvements and next-generation tools that assists the knowledge worker by removing repetitive, replicable, and routine tasks. And it can radically improve customer journeys by simplifying interactions and speeding up processes.<\/span>\u00a0<\/span><\/p>\n

IPA mimics activities carried out by humans and, over time, learns to do them even better. Traditional levers of rule-based automation are augmented with decision-making capabilities thanks to advances in deep learning and cognitive technology. The promise of IPA is radically enhanced efficiency, increased worker performance, reduction of operational risks, and improved response times and customer journey experiences.<\/span>\u201d<\/span>\u00a0<\/span><\/p>\n

By applying the concept of intelligent automation in the software testing lifecycle to achieve intelligent test automation, the digital-first organizations can build a process that is embedded with next-generation technologies and is robust enough to tackle continuous changes.<\/span>\u00a0<\/span><\/p>\n

Driving agility and quality with Intelligent Test Automation<\/span><\/b>\u00a0<\/span><\/p>\n

In an effort<\/span>\u00a0to accelerate digital transformation, organizations are adopting the modern software development methodologies of DevOps and Quality Engineering, while shifting Quality Assurance to the left.\u00a0<\/span>\u00a0<\/span><\/p>\n

With the help of test automation, it has been possible to perform software testing of a code in parallel to the development, since the beginning of an SDLC, while enabling a continuous feedback pipeline to promote early and continuous improvement.<\/span>\u00a0<\/span><\/p>\n

However, usually the test automation cases are high maintenance and not reusable. This makes test automation an expensive affair for the organization and they gradually seep back into the manual testing practices.<\/span>\u00a0<\/span><\/p>\n

With\u00a0<\/span>Intelligent Test Automation<\/span><\/a>, the software testing strategy involves a model-based testing<\/span>\u00a0approach<\/span>. In model-based testing, a TDD\/BDD approach is followed and the test cases are generated and maintained automatically.\u00a0<\/span>\u00a0<\/span><\/p>\n

Having a model-based testing approach allows organizations to implement an end-to-end testing practice across all enterprise systems. This results in h<\/span>igher test coverage, generation of efficient test cases, and lower maintenance costs.<\/span>\u00a0<\/span><\/p>\n

The smart algorithms driven by Artificial Intelligence and Machine Learning technologies integrate the analytical feature within the software testing lifecycle. This means that the outcomes can automatically be evaluated within an Intelligent Test Automation scenario, which further reduces involvement of manual resources.<\/span>\u00a0<\/span><\/p>\n

Key benefits of employing Intelligent Test Automation<\/span><\/b>\u00a0<\/span><\/p>\n

Some of the major benefits that organizations can reap from leveraging Intelligent Test Automation are:<\/span>\u00a0<\/span><\/p>\n