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 evolutions 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.

Refactoring, the process of improving the internal code structure of a software system without altering its external behavior, is widely applied during software development. Understanding how developers refactor source code can help gain better understanding of the software development process and the relationship between various versions of a system. Refactoring detection tools have been developed for many popular programming languages, such as Java (e.g., REFACTORINGMINER and REF-FINDER) but, quite surprisingly, this is not the case for Python, a widely used programming language.Inspired by REFACTORING MINER, we present PYREF, a tool that automatically detects method-level refactoring operations in Python projects. We evaluated PYREF against a manually built oracle and compared it with a PYTHON-ADAPTED REFACTOR-INGMINER, which converts Python program to Java and detects refactoring operations with REFACTORING MINER. Our results indicate that PYREF can achieve satisfactory precision and detect more refactorings than the current state-of-the-art.