Applying AI to static code analysis and unit testing
Register Now
2021-05-25
6:00 p.m.
Online Event - Zoom
Igor Kirilenko
Igor Kirilenko is VP of Development at Parasoft.
He received his Ph.D. in physics-mathematics in 1999, specializing in mathematical and program provisions of computers and systems. Dr. Kirilenko joined Parasoft in 2013, and brings many years of experience in leading engineering teams with an emphasis on establishing and promoting the best agile practices in software development environments. Currently, he oversees the rapid growth of multiple cutting-edge technologies at Parasoft, and is responsible for technical strategy, architecture, and development of all products delivered by the company.
For the past several years Igor Kirilenko has also been leading the R&D team of highly trained engineers at Parasoft who are focused on research of AI and Machine Learning technologies and creation of new approaches for improvement of accuracy in static analysis findings.
Speaker: Igor Kirilenko, Parasoft VP of Development
It’s a well-known fact you can achieve high software quality by starting your quality efforts early in the development lifecycle, aka “shift-left”. Still, it’s all too common for software to be promoted to QA environments with many easily preventable defects, often for alleged reasons such as “lack of time” or “it’s not our job to do QA”.
After thoroughly studying the issue, and its root causes, Parasoft applied carefully chosen algorithms and machine learning to maximize effective static code analysis, based on dynamically learned behaviour from each project team. We then addressed the perennial problem of low code coverage for unit tests, using AI to guide dev/QA professionals as they create effective and open source unit test code for their business critical applications.
Join us in this session as we share our journey with you, with a small peak behind the curtain, and a good old show and tell.
Register Now