arXiv subjects

Gregory Mierzwinski

Publications and source records attributed to Gregory Mierzwinski.

4 recordsLinked to original sources

arXiv2026

Exploring Statistical Change Point Detection Techniques for Performance Anomaly Detection at Mozilla

Software performance regressions can have significant business consequences, making automated detection a critical component of modern continuous integration pipelines. At Mozilla, performance anomaly detection is handled by Perfherder, Mozilla's performance engineering management system that relies on a Student's T-test-based approach to flag regressions across hundreds of daily code changes. However, our preliminary analysis of one year of Mozilla performance data reveals that 12.5% of generated alert groups are false positives, while approximately 6.8% of them contain regressions missed by the automated system. This paper presents an empirical study evaluating 25 change-point detection (CPD) methods and 15 ensemble approaches as alternatives to Mozilla's current method. We construct a ground-truth dataset of 174 performance time series manually annotated by eleven Mozilla performance engineers, representing one of the first practitioner-annotated CPD benchmarks for performance engineering. Our results show that while offline and hybrid CPD methods improve recall over Mozilla's method, they do so at a high cost to precision. Ensemble voting strategies alleviate this trade-off and offer more consistent performance, resulting in 11% improvement in the F1-score. We validate the experimental results through a practitioner survey and report on lessons learned from integrating the best methods into Mozilla's performance engineering system.

cs.SE↗

arXiv2026

Risk-Aware Batch Testing for Performance Regression Detection

Performance regression testing is essential in large-scale continuous-integration (CI) systems, yet executing full performance suites for every commit is prohibitively expensive. Prior work on performance regression prediction and batch testing has shown independent benefits, but each faces practical limitations: predictive models are rarely integrated into CI decision-making, and conventional batching strategies ignore commit-level heterogeneity. We unify these strands by introducing a risk-aware framework that integrates machine-learned commit risk with adaptive batching. Using Mozilla Firefox as a case study, we construct a production-derived dataset of human-confirmed regressions aligned chronologically with Autoland, and fine-tune ModernBERT, CodeBERT, and LLaMA-3.1 variants to estimate commit-level performance regression risk, achieving up to 0.694 ROC-AUC with CodeBERT. The risk scores drive a family of risk-aware batching strategies, including Risk-Aged Priority Batching and Risk-Adaptive Stream Batching, evaluated through realistic CI simulations. Across thousands of historical Firefox commits, our best overall configuration, Risk-Aged Priority Batching with linear aggregation (RAPB-la), yields a Pareto improvement over Mozilla's production-inspired baseline. RAPB-la reduces total test executions by 32.4%, decreases mean feedback time by 3.8%, maintains mean time-to-culprit at approximately the baseline level, reduces maximum time-to-culprit by 26.2%, and corresponds to an estimated annual infrastructure cost savings of approximately $491K under our cost model. These results demonstrate that risk-aware batch testing can reduce CI resource consumption while improving diagnostic timeliness. To support reproducibility and future research, we release a complete replication package containing all datasets, fine-tuning pipelines, and implementations of our batching algorithms.

cs.SE↗

arXiv2025

A Dataset of Performance Measurements and Alerts from Mozilla (Data Artifact)

Performance regressions in software systems can lead to significant financial losses and degraded user satisfaction, making their early detection and mitigation critical. Despite the importance of practices that capture performance regressions early, there is a lack of publicly available datasets that comprehensively capture real-world performance measurements, expert-validated alerts, and associated metadata such as bugs and testing conditions. To address this gap, we introduce a unique dataset to support various research studies in performance engineering, anomaly detection, and machine learning. This dataset was collected from Mozilla Firefox's performance testing infrastructure and comprises 5,655 performance time series, 17,989 performance alerts, and detailed annotations of resulting bugs collected from May 2023 to May 2024. By publishing this dataset, we provide researchers with an invaluable resource for studying performance trends, developing novel change point detection methods, and advancing performance regression analysis across diverse platforms and testing environments. The dataset is available at https://doi.org/10.5281/zenodo.14642238

cs.PF↗

arXiv2016

Some Counterexamples for Compatible Triangulations

We consider the conjecture by Aichholzer, Aurenhammer, Hurtado, and Krasser that any two points sets with the same cardinality and the same size convex hull can be triangulated in the "same" way, more precisely via \emph{compatible triangulations}. We show counterexamples to various strengthened versions of this conjecture.

cs.CG↗