Welcome to a new chapter of the metric of the month. This time, we want to recap three metrics we discussed and put them together to understand how active a project is regarding Contributors’ Behavior.
Continue reading “Contributors Behavior: Metric of the Month”7 metrics to evaluate risk in Open Source libraries
Metrics to assess risk in open source libraries are becoming more important. We need these tools and techniques to evaluate and mitigate the potential risks associated with using third-party open source software libraries in our own software applications. So, this blog post describes seven important metrics to evaluate this risk from an open source community perspective.
Continue reading “7 metrics to evaluate risk in Open Source libraries”Leaving Developers: Metric of the Month, February 2023
Welcome to the fourth chapter of the Metric of the Month: Leaving Developers. In the last chapter, we talked about Attracted Developers, a metric that helps us to understand the flow of new contributors joining a project or community. In this chapter, we will fully understand the contributors by studying the people leaving the project.
Continue reading “Leaving Developers: Metric of the Month, February 2023”Give Credit Where Credit Is Due: Identify Contributors From Commit Messages
Want to identify all contributors who helped with the source code of an open source project but find yourself limited by what is officially captured in the git-log? Maybe your project, like the Linux Kernel, keeps track of who helped with patches by adding their name to the commit message, but tools don’t usually understand how to analyze this properly.
Continue reading “Give Credit Where Credit Is Due: Identify Contributors From Commit Messages”Attracted Developers: Metric of the Month, January 2023
Welcome to the third chapter of the Metric of the Month, the first one for the new year! In the last chapter, we talked about the Pony Factor, a metric about code contributions to projects. In this chapter, we will study how many new contributors are attracted to the project with the Attracted Developers metric.
Continue reading “Attracted Developers: Metric of the Month, January 2023”Building and Supporting Open Source Communities Through Metrics
Each community is different and therefore requires other metrics for data-driven decisions about building and supporting it. In this blog post, we review this and share a summary of a 3 chapter series brought together by our friends at opensource.com. Take a look at this reading about Open Source Communities Metrics.
Continue reading “Building and Supporting Open Source Communities Through Metrics”The Pony Factor: Metric of the Month, November 2022
Welcome to the second chapter of the Metric of the Month! We’re very excited to continue this metrics series to show you a complete guide for different metrics each month so you can understand more about them.
Continue reading “The Pony Factor: Metric of the Month, November 2022”Metric of the Month – The Elephant Factor
Welcome to the first chapter of the Metric of the Month! We’re glad to start this series talking about metrics. We expect to show you a complete guide for a different metric each month so you can understand more about them. We start this series with our favorite one, the Elephant Factor.
Continue reading “Metric of the Month – The Elephant Factor”Bitergia has arrived in Latin America!
Last month, we were part of not one, but two Open Source events organized in Latin America! The OpenInfra Days and the Open Source Summit Latin America were broadcasted from Mexico and Brazil to the world.
Continue reading “Bitergia has arrived in Latin America!”Value-stream mapping using software development analytics
Modern software development pipelines are fractured with a lack of visibility across the entire product value-stream concealing process bottlenecks, constraints, and waste. Software development analytics can analyze value-streams within DevOps environments to provide full end-to-end visibility to the flow of work.
Continue reading “Value-stream mapping using software development analytics”