The evolution of technology has correspondingly improved all its associated operations in its wake – and software development methodology is one of them. To keep pace with high-velocity deployments, organizations today turn to creating robust CI/CD pipelines that enable them to continuously integrate and test their source code.
It is a high-intensity operation that requires continuous monitoring and high visibility into every process involved. You need a set of key CI/CD metrics that sum up your entire pipeline in manageable numbers and give you a bird’s eye view of what’s going on in real time.
Through this blog, you will be able to list out the key CI/CD metrics that matter most.
What Is CI/CD?
CI/CD is an acronym for Continuous Integration/Continuous Deployment (or Delivery.) It is a software development practice that allows developers to continuously integrate code into the codebase, test it, and deploy incremental changes continuously in a well-engineered way.
Most of the CI/CD operations can be customized and automated, enabling coding professionals to:
- Automatically trigger CI pipelines through build-test-improve cycles
- Automatically initiate the CD process when the tests are successful
It empowers organizations to speed up the process of software development, improvement, and delivery. It sets up a continuous process of improvements over the elemental version of the codebase, enabling professionals to roll out updates frequently and swiftly.
CI/CD Metrics Overview
Companies aiming to boost their software development and delivery leverage CI/CD pipelines for improved developer efficiency. Various KPIs, such as deployment frequency and success rate, offer insights into the pipeline’s effectiveness.
The numbers reflect pipeline health, enabling you to identify stale or stuck processes and eliminate them. Using CI/CD metrics, you can accelerate your development cycle, improve software quality, and make data-driven decisions.
For an agile CI/CD pipeline, the initial step involves selecting suitable test management tools. The right tools seamlessly integrate with the pipeline, ensuring a robust testing framework that complements the overall software development and delivery strategy.
Top-Level Performance DevOps Metrics
The CI/CD process is a continuous, cyclical operation that requires real-time monitoring. Setting up automation testing to monitor the pipeline’s health is paramount for success, as it enables organizations to track the pipeline continuously and keep the CI/CD metrics in the desirable range.
Monitoring your KPIs helps you ensure that your automation testing processes remain effective and reliable and helps streamline the DevOps operations.
1. Lead Time
Lead time, also known as time-to-market, is the total time that a concept takes to reach the users. However, for the purpose of monitoring the CI/CD pipeline performance, lead time (as approached by DORA) is counted from the moment a code is committed to the CD pipeline.
Shorter lead times typically mean that your processes are efficient. Longer lead times indicate that your DevOps isn’t benefitting from user feedback, and there are fewer upgrades being rolled out. This is especially true for DevOps, which incorporate a lot of manual processes.
2. Deployment Frequency
Deployment frequency refers to the number of times you release a change through your CI/CD pipeline for a given timeline. Higher frequency indicates that your DevOps cycle is rolling out changes more frequently.
Typically, this is considered good because it reduces the risk of introducing too many erroneous variables into a release. Smaller changes gain feedback sooner, further providing impetus to the CI/CD momentum.
Lower frequencies, on the other hand, signify that your developers aren’t feeding regular code commits to the CI/CD pipeline for a variety of reasons, like task phasing or batching changes.
3. Change Failure Rate
Change failure rate is an efficiency CI/CD metric that lets you track how many of your code changes led to failure when compared against the volume of changes deployed in total. It is a reality check for the coding and testing efficiency of your CI/CD pipeline.
A low change failure rate is favorable because it indicates that fewer code changes led to failures for a given volume of changes deployed.
A high rate may indicate a problem with the testing mechanisms deployed early in the CI pipeline; they may be missing bugs frequently.
4. Mean Time to Recovery
Mean time to recovery is a key metric that measures the time your organization takes to resolve a production failure. It is inevitable for large-scale and complex projects to encounter some errors.
In this situation, it is favorable to release an almost perfect package rather than slowing down the pipeline to fix all the bugs.
The feedback loop can then be engaged to trigger proactive system monitoring, error alerts, and production alerts so that the failures that do return are resolved and released quickly.
CI and Operational Metrics
The CI pipeline is where all the action happens. It is important to continuously monitor the health of all associated operations using the CI/CD metrics below:
1. Code Coverage
Code coverage is a metric that refers to the proportion of the code that is covered by automated testing through unit testing.
Ideally, this metric should be as high as possible since a high number signifies that most of your code is being tested by the CI servers. This reduces the need for your engineers to explore random code units for possible bugs.
It can be improved by designing automated testing to perform unit testing as the first layer.
2. Build Duration
In order to accelerate the entire development process, it is essential to streamline each stage of the process and accelerate the operations. Build duration is the metric that helps you identify which development stages are taking longer than the ideal time.
Build duration measures the time it takes your engineers to complete one stage of the CI/CD pipeline. Where the build duration is longer, it may indicate a bottleneck in the processes or some other factor that is slowing down the whole operation. It may cause downstream delays as well.
3. Test Pass Rate
The test pass rate is another metric similar to the change failure rate that tells you how many of the test cases of the total volume were successful. It is pivotal in understanding how many of the code changes made to the source lead to a failed test.
Automating the tests helps you address more code bits in less time, enabling the identification of failing code more efficiently. However, if the test pass rate is lower than ideal, it may indicate a problem with the quality of the code lined up for testing overall.
4. Time To Fix Tests
Time to fix test is a key metric that lets you see how efficiently your team is handling code that comes back after test failure. The metric highlights the time it takes between reporting a failed test and the moment the same unit passes the test after bug fixes or improvements.
The shorter the time to fix the test is, the better your team is at resolving issues identified with code. It indicates that your CI pipeline is healthy and is able to respond to errors and resolve issues quickly.
5. Failed Deployments
Failed deployment is a support metric that enables you to measure your change failure rates. A failed deployment is a release that needs to be rolled back or requires an urgent release of a fix for resolution.
Although having no failed deployments is desirable, it may have a negative impact on the CI/CD pipeline. This is because it inadvertently prioritizes flawless builds, potentially overlooking the importance of swift deployment.
Such an approach indirectly affects lead times and can pose challenges when handling larger, more complex deployments in the event of errors.
6. Defect Count
Defects are not counted as failures. Therefore, the defect count refers to the number of identified and open issues on the roster that are classified as bugs that need fixing. A low defect count is an indicator of high-quality code being circulated in the CI/CD pipeline.
You can monitor this metric to alert you for a rising trajectory that can indicate a fall in the code quality overall. It is crucial to maintain the defect count within the lower range to ensure there are fewer bugs per engineer to fix.
7. Deployment Size
Deployment size is used to measure the size of a build or release that a team commits. A larger deployment size indicates that a team is committing code less frequently and there are more story points involved in a release.
Deployment size should ideally be as small as possible for a project, which creates a quick deployment and feedback loop, helping the CI/CD pipeline with more iterations for improvement over the base.
CI/CD pipelines are a part of agile business methodology. Constant monitoring of pipeline health is pivotal to ensuring that it yields the results it is supposed to. By leveraging the key metrics discussed above, you can gain a holistic picture of your DevOps process in numbers, identify bottlenecks, and act on them quickly.
It can also indirectly shed light on the processes and practices that are working in a well-oiled manner, establishing a set of best practices to follow and improve on.
Remember: CI/CD pipeline works well when all the tools and human capital involved work in tandem to generate high-quality output more frequently.
What Is CI/CD Monitoring?
In CI/CD monitoring, organizations establish builds, tests, and automated analytics for pipeline visibility. It’s about pinpointing key metrics aligned with business goals and tracking them over time for process performance assessment.
DevOps keep an eye on metrics like lead time, deployment size, change failure rate, mean time to recovery, defect count, code coverage, and deployment frequency in their CI/CD workflows. It’s a strategic approach to refining processes and ensuring alignment with business objectives.
Contextual design DevOps Software development Metric (unit) Pipeline (software) Testing
Opinions expressed by MaximusDevs contributors are their own.