The pace of software delivery has increased dramatically over the past decade. Agile development, DevOps, and continuous delivery have shortened release cycles from months to weeks, or even days. In this fast-moving environment, traditional testing methods often struggle to keep up, leading to higher costs, missed defects, and delayed releases. Artificial Intelligence (AI) is emerging as a powerful enabler in this space. By applying machine learning, natural language processing, and predictive analytics, AI testing is transforming how organizations approach software quality. What is AI Testing? AI testing refers to the use of artificial intelligence techniques to improve different aspects of the software testing lifecycle. Instead of relying only on static, rule-based scripts, AI testing leverages algorithms that learn from data, adapt to application changes, and optimize test execution . This makes testing smarter, faster, and more resilient compared to convent...
Organizations rely on scalable, efficient and robust testing practices that can ensure the timely delivery of high quality software products or applications. Software quality assurance ensures that stringent quality standards are met by the application or product that is being built. With the technological domain scaling new heights, the integration of Artificial intelligence (AI) in software testing is considered to be a strategic and rewarding move. In this article, you will get to know how to select the right AI-driven automation tools for QA. The following are the key factors to consider while selecting the right AI-driven test automation tool: Assessing the return on investment and cost efficiency: There is a higher initial price tag involved with advanced automated software testing tools, but, they can provide significant long-term savings by reducing maintenance costs and manual effort. The total cost of ownership needs to be evaluated, including t...