EVOSCAT: Exploring Software Change Dynamics in Large-Scale Historical Datasets
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915446819651584 |
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| author | Serbout, Souhaila Hurtado, Diana Carolina Muñoz Atwi, Hassan Riggio, Edoardo Pautasso, Cesare |
| author_facet | Serbout, Souhaila Hurtado, Diana Carolina Muñoz Atwi, Hassan Riggio, Edoardo Pautasso, Cesare |
| contents | Long lived software projects encompass a large number of artifacts, which undergo many revisions throughout their history. Empirical software engineering researchers studying software evolution gather and collect datasets with millions of events, representing changes introduced to specific artifacts. In this paper, we propose EvoScat, a tool that attempts addressing temporal scalability through the usage of interactive density scatterplot to provide a global overview of large historical datasets mined from open source repositories in a single visualization. EvoScat intents to provide researchers with a mean to produce scalable visualizations that can help them explore and characterize evolution datasets, as well as comparing the histories of individual artifacts, both in terms of 1) observing how rapidly different artifacts age over multiple-year-long time spans 2) how often metrics associated with each artifacts tend towards an improvement or worsening. The paper shows how the tool can be tailored to specific analysis needs (pace of change comparison, clone detection, freshness assessment) thanks to its support for flexible configuration of history scaling and alignment along the time axis, artifacts sorting and interactive color mapping, enabling the analysis of millions of events obtained by mining the histories of tens of thousands of software artifacts. We include in this paper a gallery showcasing datasets gathering specific artifacts (OpenAPI descriptions, GitHub workflow definitions) across multiple repositories, as well as diving into the history of specific popular open source projects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EVOSCAT: Exploring Software Change Dynamics in Large-Scale Historical Datasets Serbout, Souhaila Hurtado, Diana Carolina Muñoz Atwi, Hassan Riggio, Edoardo Pautasso, Cesare Software Engineering Long lived software projects encompass a large number of artifacts, which undergo many revisions throughout their history. Empirical software engineering researchers studying software evolution gather and collect datasets with millions of events, representing changes introduced to specific artifacts. In this paper, we propose EvoScat, a tool that attempts addressing temporal scalability through the usage of interactive density scatterplot to provide a global overview of large historical datasets mined from open source repositories in a single visualization. EvoScat intents to provide researchers with a mean to produce scalable visualizations that can help them explore and characterize evolution datasets, as well as comparing the histories of individual artifacts, both in terms of 1) observing how rapidly different artifacts age over multiple-year-long time spans 2) how often metrics associated with each artifacts tend towards an improvement or worsening. The paper shows how the tool can be tailored to specific analysis needs (pace of change comparison, clone detection, freshness assessment) thanks to its support for flexible configuration of history scaling and alignment along the time axis, artifacts sorting and interactive color mapping, enabling the analysis of millions of events obtained by mining the histories of tens of thousands of software artifacts. We include in this paper a gallery showcasing datasets gathering specific artifacts (OpenAPI descriptions, GitHub workflow definitions) across multiple repositories, as well as diving into the history of specific popular open source projects. |
| title | EVOSCAT: Exploring Software Change Dynamics in Large-Scale Historical Datasets |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2508.10852 |