Introduction to Static Analysis for Predicting Performance Bugs (1st Offering)
Presenter: Younes Jahandideh
Date: Friday, October 10, 2025
Time: 3:00 pm
Location: Workshop Space, 4th Floor - 300 Ouellette Ave., School of Computer Science, Advanced Computing Hub
This workshop introduces how static analysis and machine learning can be combined to detect performance bugs before they slow down or weaken software systems. Performance bugs don’t stop a program from running, but they make it inefficient, using too much time or memory. The session explains how project history in Git can reveal where such bugs start, using automated tools like SZZ Unleashed to label buggy and clean files. Participants will see how models such as Random Forest and XGBoost learn from this data to predict problem areas in code with high accuracy. By the end, attendees will understand how these techniques can help developers improve code quality, save debugging time, and build faster, more reliable software.
- What static analysis is and why it matters
- Understanding performance bugs and their effects
- Collecting and labelling data from code history
- Extracting useful features from software projects
- Training models like Random Forest and XGBoost
- Reviewing results and accuracy
- How to make the system smarter and more reliable
- Simple real-world example
Basic understanding of programming, software engineering, and machine learning.
Younes is a Ph.D. student and research assistant who began his program at the School of Computer Science in Fall 2023. His primary research focuses on learning-based optimization, a type of self-optimizing system in cloud computing.