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.

Modern instant messaging applications (e.g., Gitter, Slack, Discord) provide users with real-time communication means. Developers use them for collaborative development, to ask for code reviews, and to have software-related discussions. In short, a (potential) treasure trove for program comprehension. However, as with any high-throughput "chat application", messages interleave, leading to concurrent conversations. Associating messages to conversations is called conversation disentanglement, a useful and necessary pre-processing step to analyze datasets of instant messages. Although various conversation disentanglement algorithms have been proposed, it is cumbersome to set up proper execution environments and hard to ensure input data format consistency, calling for better practices and tool support.We present CODI, a RESTful API micro-service and web interface for conversation disentanglement. It provides an easy way to disentangle conversation transcripts with pre-trained models or to train new ones on custom datasets, features, and hyper-parameters. CODI achieves state-of-the-art performances on transcripts of IRC, Slack, and Discord conversations. We show how CODI can provide a significant improvement to reusability (and replicability) of research results, while reducing the efforts and potential mistakes due to configuration, setup, and execution.CODI’s source code: https://github.com/USIREVEAL/CODI