1. Artificial Intelligence allows the forecasting of defective areas
2. This process can be fully automated with a low cost using open source technology
3. The above process is a different approach from static code analysis.

Jarek is a leader of research and development projects in the area of artificial intelligence and quality assurance as well as being experienced trainer. He is well known to software testing community in Poland, being nominated as one of the ten “People of Testing” for year 2018. His research interests revolve around applied artificial intelligence, especially for quality assurance purposes. Jarek received master degree and has strong background in management, including commanding of military units. He also studied cybernetics, electronic engineering and nuclear energetics. Jarek is an author and co-author of series of books, publications and presentations concerning nuclear and computer systems safety, artificial intelligence and quality assurance in computer science, including "Software Testing in Practice", “Requirements Engineering in Practice”, “Software Engineering: Challenges and Solutions” and “Software Engineering: Improving Practice through Research”. Since 2012, as a part of his PhD studies, he conducts research on artificial intelligence for quality assurance in IT systems. He is a well-known conference speaker, presenting on over 20 scientific and professional conferences, including TestCon in Moscow and STF in Milan. He held a position of global artificial intelligence technology specialist in one of the world’s leading automotive companies, working as an advisor for top executive management. Also he led of global implementation of artificial intelligence-driven software quality assurance methodology and supporting it centralized, automated tools. He is an advisor to Polish Testing Board and a member of Internet of Things working group created by Polish Ministry of Digital Affairs. He also co-created Polish nuclear energy popularization programme.

For some years, I’ve worked on using machine learning to predict software defects. About this topic and the success of this technology in the company, I spoke on several conferences, including Testwarez in Poland and TestCon in Moskow. In recent years, inspired by endless laziness in projects, I have decided to fully automate process of defect prediction in an easy and enjoyable way – by using open source. I invited to this initiative my friend and after some time we ended with success! We do not want to keep the way we have succeeded in secret: Would you like to be automatically notified, which software components the user will not like in the future? You are more than welcome to see my presentation!