5 Common Types of Mobile App Bugs Found Using AI
Among all mobile apps, the current error rate is believed to be at 15 percent. With a thousand new apps launching daily and a constant increase of mobile devices, there’s a need for a scalable solution to create and maintain high-quality apps, without hassle. Thanks to artificial intelligence, exploratory testing is advancing and proving to detect mobile bugs at scale. Join Sandy Park as she examines the five most common types of errors found through more than ten thousand hours of AI-powered testing, with actual samples. She will introduce the challenges of each type and explain how the heuristic or rule-based approach was not able to address the issues efficiently. She will cover topics such as broken-element identification and Z-order detection for layered views. Finally, Sandy will share deep learning methods such as RCNN and LSTM, which enhance coverage and reliability. You will leave with a practical checklist of the hidden spot errors that could be prevented with the use of AI.
Sandy Park is a co-founder of Apptest.ai, an emerging startup in exploratory mobile testing located in the bay area and Seoul. She recently automated Facebook's fiber connectivity projects while partnering with MNOs in EMEA and APAC. Prior to that, she was a Director of mobile partnerships in Samsung USA. She also contributed to the acquisition of EdgeSpring to Salesforce, which is now called Salesforce Wave. She holds a Ph.D. of Energy Resources Engineering, and a Master's of Electrical Engineering both from Stanford University.