Introduction: Static Analysis to Predict Performance Bugs (2nd Offering)
Presenter: Wardah Saleh
Date: Monday, October 27th, 2025
Time: 11:30 am
Location: Workshop Space, 4th Floor - 300 Ouellette Ave., School of Computer Science, Advanced Computing Hub
This workshop offers an introduction to using static code analysis and machine learning to predict performance bugs before deployment. Participants will learn how performance issues differ from functional bugs and how tools like SZZ Unleashed can automatically label code changes for analysis. The session will cover extracting key static and process metrics, training models such as Random Forest and XGBoost, and interpreting results to improve early bug detection. Attendees will also explore challenges like data imbalance and discuss future directions using advanced models such as CodeBERT and graph neural networks (GNNs) for more accurate, proactive performance assurance.
1. Introduction to Performance Bugs
2. Static Analysis Basics
3. Using SZZ Unleashed for Data Labelling
4. Feature Extraction & ML Models
5. Results and Key Insights
Basic understanding of programming and familiarity with machine learning concepts or software development tools
Wardah Saleh is currently a Ph.D. student in the School of Computer Science at the University of Windsor and an Assistant Professor in the Department of Computer Science at the American International University-Bangladesh (AIUB), where she is on study leave. She earned both her B.Sc. in Computer Science & Engineering and M.Sc. in Computer Science (Computer Network and Architecture) from AIUB, graduating with the highest academic distinctions. Her research interests include VANET (Vehicular Ad hoc Networks), 5G technologies, IoT (Internet of Things), network security and AI (Artificial Intelligence). She also holds professional certifications such as CCNA (Cisco Certified Network Associate) and CCNP (Cisco Certified Network Professional).