What are the most important metrics to measure?
The following are the metrics and key performance Indicators that every software development organization should consider using to improve visibility into their software development projects and processes and to measure the performance of their engineering teams. These will not apply to all organizations, and the specific metrics and KPIs that you choose should align with your goals and be specific to your team and organization. Regardless of the selected metrics, It's important to regularly review and adjust them based on your team's needs and objectives.
Cycle Time: This metric measures the time between the first commit in the code repository and when that code is merged into the production branch.. It can help identify bottlenecks and inefficiencies in your coding duration, your code review process and help you improve overall productivity.
Deployment Frequency: This metric measures how often your team is able to deploy new changes to your production environment. It can help you identify how quickly your team is able to iterate and respond to changes.
Bug Duration: This metric measures how long it takes to fix bugs and other issues. It can help you identify areas where your team can improve their response time and identify any systemic problems that need to be addressed.
Bug Resolution Ratio: This measures if the team is resolving more metrics than it creates or vice-versa. Specifically, it is the ratio of bugs resolved to bugs created.
Active Contributor %: This key performance indicator measures the percentage of potential contributors (developers) that were active during the selected period.
Review Coverage: This metric measures the percentage of code that goes through a code review. It can be a good indicator of code quality and helps ensure that all code changes are tested properly.
Review Time: This metric measures the time it takes for code changes to be reviewed and approved. It helps you understand how efficient your code review process is and identify any areas for improvement.
Lead Time/Issue Active Duration: This metric measures the time it takes to complete a specific task from the moment that work is started until it is resolved. It can help identify bottlenecks and inefficiencies in your development process.
Issue Duration: Similar to Issue Active Duration (Lead Time), this metric measures the total time an issue was worked on, but it also includes the time the issue exists in the backlog before work begins.
Technical Debt %: This KPI measures the percentage of completed work that focused on fixing technical issues or improving the overall quality of your code. It measures how well your team is prioritizing and planning for technical improvements.
Coding Efficiency: measures code quality by dividing each review by the number of rounds of feedback (request for changes, approvals, etc.) required to gain approval. The ideal number is 1, the lower it is the more issues found during review.
Sprint Completion %: Measures the amount of the work in a sprint that was completed during the sprint. It is important to also measure the quality of the work completed to ensure that the work that was done was done to satisfaction.
Additional metrics that enhance visibility and monitor effectiveness include:
Sprint Burndown: This KPI measures how much work is remaining in a sprint and helps you identify if you are on track to meet your goals.
Mean Time to Recovery (MTTR): This metric measures the average time it takes to recover from a failure or incident. It can help identify areas of your system that need improvement and help your team focus on reducing downtime.
Customer Satisfaction: This KPI measures how satisfied your customers are with your product or service. It can be measured through surveys, reviews, or other feedback mechanisms. This is a critical KPI as it will help you understand how well your product meets the customer's expectations.
Defect Density: Defect density is the number of defects (bugs or issues) per unit of code or functionality. You can measure defect density by tracking the number of defects that are identified during testing or in production and dividing it by the size of the code base or functionality.
Application Performance: Performance measures how well your software performs in terms of speed, responsiveness, and resource usage. You can measure performance by using tools that generate reports on metrics such as response time, throughput, and resource usage.