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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2503.16332 |
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| _version_ | 1866913748128628736 |
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| author | Besbes, Mohamed Bilel Costa, Diego Elias Mujahid, Suhaib Mierzwinski, Gregory Castelluccio, Marco |
| author_facet | Besbes, Mohamed Bilel Costa, Diego Elias Mujahid, Suhaib Mierzwinski, Gregory Castelluccio, Marco |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16332 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Dataset of Performance Measurements and Alerts from Mozilla (Data Artifact) Besbes, Mohamed Bilel Costa, Diego Elias Mujahid, Suhaib Mierzwinski, Gregory Castelluccio, Marco Performance 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 |
| title | A Dataset of Performance Measurements and Alerts from Mozilla (Data Artifact) |
| topic | Performance |
| url | https://arxiv.org/abs/2503.16332 |