Minisymposium Presentation

Software performance evolves over time, yet traditional debugging and profiling remain reactive, costly, and disconnected from development workflows. Performance drift—gradual degradation in execution efficiency due to code modifications—often goes undetected until it causes significant slowdowns, forcing late-stage debugging and costly fixes. This talk presents a vision for AI/ML-driven proactive performance-drift detection, where models continuously monitor software evolution, identifying inefficiencies before they degrade execution. By combining static analysis (abstract syntax trees) with dynamic insights from nightly tests, this framework enables early detection of performance-impacting changes. Traditional ML approaches require full model retraining whenever code changes or new runtime data become available, making them impractical for fast-moving development cycles. Few-shot learning eliminates this overhead by allowing models to update incrementally with minimal new data. Attention-based representation learning further enhances interpretability by prioritizing performance-critical features, enabling more targeted interventions. This framework supports two key decision-making processes where (1) developers can receive automated feedback on whether a code change improves or degrades performance, enabling early intervention; (2) the insights can guide hardware configuration choices and runtime parameter tuning. This approach can be seamlessly integrated into CI/CD pipelines to achieve software that not only remains correct but also maintains efficiency as it evolves.