Maximizing Software Quality With Artificial Intelligence - AI Summary

But considering that an estimated 85% of AI projects fail to deliver on their goals, it's clear that many software development organizations are struggling to understand what skills actually help their teams harness the power of intelligent technologies. Today AI and ML are helping quality teams by ensuring that tests are only run when the application reaches the correct state, making sure that developers and testers can dedicate more time to fixing defects rather than investigating accidental failures. These advanced reporting features help QA teams efficiently identify small changes or errors –and ensure that anomalies are addressed before they lead to more severe issues. But as important as artificial intelligence and machine learning are to the future of software development and quality engineering, most QA professionals are too busy to become AI experts. To maximize their time, effort, and skillset, QA teams are better served by mastering key artificial intelligence and machine learning fundamentals that will enable them to start embracing advanced testing techniques and AI-based solutions as quickly as possible. But considering that an estimated 85% of AI projects fail to deliver on their goals, it's clear that many software development organizations are struggling to understand what skills actually help their teams harness the power of intelligent technologies.