Success in digital transformation is linked to Continuous Testing and automation has emerged as the disruptor, turning quality from a bottleneck into a competitive advantage for any organization. We are no longer being asked about reducing costs and delivering ROI. Instead, it is all about customer experience and business outcome.
Automated systems integrated with production testing, continuously and unattended, with deployment capabilities, achieves true digital transformation.
It enables agility for digital transformation and is embracing new technologies like Big Data, AI and Test Analytics and NLP based automation as well as self-healing of defects. Intelligent Automation, AI and deep learning-assistive technology solve specific challenges across the lifecycle, including test environment management, test data management, service virtualization and provide on demand data access for any project.
The preparation of the test data, the execution of tests and the analysis of the results in the age of Agile and DevOps is a shared responsibility and time is the key goal to achieve cycle times of weeks or days; test automation has to be part of a fully automated delivery chain including all the pre and post execution activities.
Test data management and fragmented test environments, continue to be a concern as teams struggle to find automated ways to create and maintain test data and fix environmental issues. Both have an impact on release timelines and test coverage, leading to defects and higher operational costs and eventually impacting the customer experience.
A test automation solution today needs to prepare its data, check the environment, run unattended and notify the result to the key stakeholders, fully integrated with CI/CD solutions.
The most important solution to overcome increasing QA and Testing Challenges will be the emerging use of AI and machine learning – based technology. AI-assistive technology ensures teams achieve maximum test coverage, prioritize the testing needs, increasing efficiency and productivity and enabling them to run more tests in less time with greater quality. AI brings new insights and helps testers make more informed and accurate decisions.
In order to move test automation forward we needed a new approach not just new tools. This new approach requires a cognitive automation strategy including tools supporting assistive technology, unattended automation across the enterprise, and self-healing of defects. Approaching test automation only from a tool perspective, will only create unrealistic expectations that will prevent its success.
Organizations must accelerate the speed of delivery while ensuring quality and testing efficiency of new digital products and services.
ValidatAI , the AI-powered process modelling and automated test creation product, provides deep analytics , allowing DevOps teams to understand why there are problems with a release rather than simply receiving failure notifications. Defect targeting driven by machine learning can cut costs by finding bugs and recommending ‘next best step’ for maximum coverage improving software quality and cutting time to fix.
ValidatAI expanded its capabilities for automated usability testing and enables continuous testing across the enterprise. By using AI and analytics, tests are being performed at every stage to ensure reliability and usability of the system under test.
Our ‘build for change’ technology is a central component of any testing process improvement strategy that increases business agility, flexibility and efficiency. It provides the single ‘brain’ built into your enterprise applications to gain insights in real-time and at scale across the entire customer journey.
This new approach to testing is integral to true test automation and delivers true digital experience and business success.