Code Quality
Technical Debt Metrics: Quantifying and Managing Code Quality
Technical debt, the cost of shortcuts and compromises made during software development, poses a significant challenge for engineering managers and development teams seeking to maintain high-quality code and streamline productivity. Accumulating technical debt impacts developer productivity metrics, slows down cycle time, deteriorates code quality, and ultimately hampers business agility. For organizations striving to align engineering team metrics with strategic goals, effectively measuring and managing technical debt is a crucial competency.
Engineering Velocity: Beyond Story Points and Sprints
Engineering velocity has become a central concern for software teams and technology leaders seeking to maximize business impact. Yet, velocity is too often misunderstood, reduced to simple tallies of story points completed or sprints run. While these agile metrics offer surface-level indicators of progress, they fail to capture the holistic picture required for effective decision-making and continuous improvement.
In this comprehensive post, we’ll explore how engineering velocity should be measured, why legacy metrics like story points and sprint counts fall short, and which modern, actionable metrics—powered by platforms such as Gitrolysis—can provide deeper insights into product engineering effectiveness. Whether you’re a CTO, engineering manager, team lead, or developer, understanding the full landscape of engineering velocity will enable smarter strategies, faster feedback cycles, and higher performing teams.
Pull Request Metrics That Actually Improve Code Quality
Modern software development is driven by collaboration, speed, and constant iteration. As engineering teams scale, keeping track of code quality becomes increasingly challenging—especially with distributed teams, hybrid work environments, and rapidly shifting priorities. One of the most actionable ways to drive higher code quality and developer productivity is through systematic tracking and analysis of pull request (PR) metrics. Git analytics platforms like Gitrolysis are designed to surface these metrics, providing clear insights that inform better engineering practices and effective team performance monitoring.