To work Agile is to work in a timebox style. Testers must work within a smaller scope during development cycles of two to four weeks (called Sprints), and address changes within each Sprint while acknowledging the impacts of those changes for the next Sprint.
When thinking of Agile development and artificial intelligence, the potential for a symbiotic relationship that increases testing speed and accuracy will likely inspire more project managers to embrace machine learning for higher testing efficiency.
Today’s project managers must find a method for deploying quality software that is error free and satisfies the needs of consumers without exhausting their testing team and quality assurance engineers. Artificial intelligence and Machine Learning could remedy the challenges of agile testing because these tools offer automation.
Testing Challenges in Agile Environments
Agile methodology is now one of the most popular methods for project managers during software development and testing, but there are difficulties that management should acknowledge within agile environments.
Continue reading the most common issues agile testers face at aithority.com