CI/CD
Change Failure Rate: Balancing Speed with Stability in Software Development
Software development teams are under constant pressure to deliver features, fix bugs, and respond to market demands at an ever-increasing pace. As organizations race to improve developer productivity metrics and cycle times, engineering leaders must ensure that rapid delivery does not come at the expense of software stability and customer satisfaction. One essential metric for achieving this balance is Change Failure Rate (CFR), a core component of the DORA metrics widely accepted in modern DevOps and platform engineering practices.
Deployment Frequency: How Elite Teams Ship Multiple Times Per Day
Modern software development teams face mounting pressure to deliver value rapidly and consistently. Deployment frequency—how often engineering teams release code to production—is one of the cornerstone DORA metrics used to benchmark developer productivity and software delivery performance. Elite teams now reliably achieve multiple deployments per day, turning continuous delivery from an aspiration into a standard practice.
In this guide, we examine the critical drivers behind high deployment frequency, share proven strategies adopted by top-performing organizations, and highlight how leveraging git analytics and actionable engineering team metrics, via platforms like Gitrolysis, can transform how your team ships software and drives business outcomes.
Cycle Time in Software Development: How to Measure and Optimize
In today’s fast-paced software development environment, understanding and optimizing cycle time is crucial for engineering managers, team leads, and product leaders who want to deliver high-quality software efficiently. Cycle time—the elapsed time from when work begins on a task until it is delivered—serves as a fundamental metric for measuring developer productivity, identifying bottlenecks, and driving continuous improvement. This comprehensive guide examines the essentials of cycle time in software development, provides actionable steps for measuring it, and details proven strategies to optimize it, leveraging insights from git analytics platforms such as Gitrolysis.