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

翻译：软件系统中的性能退化可能导致重大经济损失和用户满意度下降，因此其早期检测与缓解至关重要。尽管尽早捕获性能退化的实践具有重要意义，但目前仍缺乏公开可用的数据集来全面捕获真实世界的性能测量数据、专家验证的警报以及相关元数据（如缺陷报告和测试条件）。为填补这一空白，我们引入了一个独特的数据集，以支持性能工程、异常检测和机器学习领域的各类研究。该数据集采集自Mozilla Firefox的性能测试基础设施，包含2023年5月至2024年5月期间收集的5,655条性能时间序列、17,989条性能警报以及相关缺陷的详细标注。通过发布此数据集，我们为研究人员提供了宝贵资源，可用于研究性能趋势、开发新颖的变化点检测方法，并推动跨不同平台和测试环境的性能退化分析。数据集可通过 https://doi.org/10.5281/zenodo.14642238 获取。