Here at SourceLevel, we help software engineering teams measure their software life cycle with Pull Request Metrics, Collaboration Metrics, DORA Metrics and more.
We also like to hear battle tested ideas on metrics and how to scale teams. That's the idea for this collection.
Code Quality Metrics
Measures the level of difficulty in understanding and maintaining a piece of software code. Higher code complexity can lead to longer debugging and testing time, lower code quality, and decreased maintainability. Common measures of code complexity include lines of code, number of nested loops and conditional statements, and code density.
Code duplication refers to instances of the same code appearing in multiple places within a codebase. Code duplication can increase the size of the codebase, making it more difficult to maintain, and can lead to inconsistencies if the duplicate code is changed in one place but not in another.
Code coverage is a measure of how much of the codebase is executed during testing. High code coverage indicates that a significant portion of the code has been tested and is less likely to contain bugs or security vulnerabilities. Code coverage is often measured in terms of percentage of lines of code executed or percentage of functions executed.
Measures the amount of code that is added, deleted, or modified over a specific time period. This metric can be used to assess the stability and maintainability of a software product, as well as the development velocity of a team. High Code Churn may indicate a rapidly changing codebase, which can make it difficult to maintain and troubleshoot the software. On the other hand, low Code Churn may indicate a more stable and consistent codebase, but also may suggest a lack of new features or development velocity. Monitoring Code Churn can help development teams identify areas where improvements can be made to the development process, and ensure that the codebase remains maintainable and stable over time.
Pull Request Metrics
Pull Request Engagement Time is a metric that measures the amount of time that elapses between the creation of a pull request and its final approval or rejection. This metric can help development teams understand the efficiency of their code review process and identify bottlenecks or areas for improvement. High pull request engagement times can indicate a slow and inefficient code review process, while low engagement times can indicate a fast and streamlined process. Additionally, Pull Request Engagement Time can be used to measure the level of collaboration and engagement within a development team, as well as the effectiveness of communication and decision-making processes.
Pull Request Approval Rate is a metric that measures the percentage of pull requests that are approved and merged into the codebase. This metric can be used to assess the efficiency and effectiveness of the code review process, as well as the overall quality of the software product. A high Pull Request Approval Rate may indicate a fast and efficient code review process, while a low approval rate may indicate a slow or overly strict code review process, or a lack of attention to pull requests. Monitoring Pull Request Approval Rate can help development teams identify bottlenecks in their code review process and make improvements to increase the speed and quality of their software development.
Pull Request Review Time is a metric that measures the amount of time it takes for a pull request to be reviewed and processed. This metric can help development teams evaluate the efficiency of their code review process and identify bottlenecks or areas for improvement. High Pull Request Review Time may indicate a slow and inefficient code review process, while low review time can indicate a fast and streamlined process. Additionally, Pull Request Review Time can be used to measure the workload and availability of development team members, and to ensure that code quality is maintained. Monitoring Pull Request Review Time can help development teams prioritize resources and make improvements to the code review process.
Deployment, Release or Flow Metrics
Refers to the number of times a software product is deployed to production in a given period of time. High deployment frequency can indicate a fast-paced and efficient development process, while low deployment frequency may indicate a slower and less agile process.
Lead Time for changes
Lead Time for Changes is the amount of time it takes for a change request to be implemented and deployed to production. Short lead times for changes can indicate a fast and efficient development process, while long lead times may indicate a slow and bureaucratic process.
Throughput is the number of items processed in a given period of time. In software engineering, throughput can refer to the number of software features developed, the number of bug fixes completed, or the number of change requests processed. High throughput can indicate an efficient and productive development process, while low throughput may indicate a slow and unproductive process.
Rollback Rate is a metric that measures the frequency at which changes to a software product are rolled back or undone due to issues such as bugs, compatibility problems, or unexpected consequences. High rollback rates can indicate a lack of thorough testing and quality assurance processes, and can result in decreased customer confidence and a negative impact on user experience. Rollback rate can be used as an indicator of the overall stability and reliability of a software product, and can help development teams identify areas for improvement in their processes.
Measures the amount of time that a system or service is available and functioning as intended. This metric is commonly used in the context of software development to evaluate the reliability and performance of a software product or service. High uptime indicates that a system or service is available and functioning smoothly, while low uptime suggests that the system or service is unavailable or experiencing performance issues. Monitoring uptime can help development teams identify and resolve performance issues, ensure high availability for users, and improve the overall reliability of the software product or service.
SourceLevel brings Engineering Ops Metrics to your workflow, including Pull Requests Metrics, DORA Metrics, Collaboration Metrics and more.