Stanislaw Swierc is a senior software engineer with 15 years of experience building cloud-native, data-driven systems at Microsoft and Facebook, currently based in Redmond. He blends machine learning and optimization expertise—having delivered energy load forecasting and distributed energy scheduling models—with production engineering skills across Azure DevOps, Pipelines, and large-scale microservices. His work has reduced manual toil (e.g., an automated build-diagnosis model with high precision) and enabled analytics at scale through integrations between Azure DevOps and Power BI that reached thousands of users. An advocate for reliability, he has contributed to open-source testing for log parsing (Drain3), improving cluster and threshold test coverage to harden streaming log templates. With graduate training in computer software engineering and a track record spanning research-grade models to pragmatic cloud services, he thrives at the intersection of data science and robust engineering.
14 years of coding experience
9 years of employment as a software developer
MSc, Computer Science, MSc, Computer Science at The Silesian University of Technology
Gæstestuderende, Computer Science, Gæstestuderende, Computer Science at Danmarks Tekniske Universitet / Technical University of Denmark
Doctor of Philosophy (Ph.D.), Computer Software Engineering, Doctor of Philosophy (Ph.D.), Computer Software Engineering at Politechnika Śląska w Gliwicach
A robust streaming log template miner based on the Drain algorithm
Role in this project:
QA Engineer / Test Automation Engineer
Contributions:15 reviews, 13 commits, 4 PRs in 6 months
Contributions summary:Stanislaw primarily contributed to testing the `drain3` project by adding and modifying unit tests. Their work included implementing test cases to validate the functionality of the log template miner, specifically focusing on scenarios involving different similarity thresholds and cluster management. The user also refactored the test suite, optimizing the existing code. These contributions help ensure the reliability and accuracy of the log parsing and clustering algorithms.
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.