- Deep learning could be used for identifying buggy commits
- Accuracy of identification differs depending on the approach
- Open source dataset and example code are available to facilitate reproduction and building applications around the framework
Catalin Meirosu – Head of Systems Management 1 | Ericsson, Product Development Group Software-Defined InfrastructureCatalin is a results-oriented leader with over 13 years of experience at Ericsson. His work focuses on making telecommunication networks self-managing. He lead a team that focused on analytics and open-source contributions while successfully incubating the Cloud RAN product. Currently he leads the Systems Management team that is responsible for the architecture and technical evolution of the Software-Defined Infrastructure products at Ericsson.
Abgeiba Yaroslava Isunza Navarro – Machine Learning Engineer | Modulai ABAbgeiba is a machine learning engineer at Modulai. She has ML experience in NLP, finance and healthcare. Prior to joining Modulai she worked in ML projects at Ericsson and BBVA banking. Abgeiba holds a M.Sc. in Machine Learning from KTH Royal Institute of Technology, Stockholm and a B.Sc. in Telecommunications and Electronics from Tecnológico de Monterrey, Mexico. Outside of work she enjoys travelling, painting and learning new languages
Day 2 | M6 | Cloud Computing & Analytics Stage
Catalin Meirosu – Head of Systems Management 1 | Ericsson, Product Development Group Software-Defined Infrastructure & Abgeiba Yaroslava Isunza Navarro – Machine Learning Engineer | Modulai AB