Starting out with DevSecOps requires a well-thought-out workflow, but that can sometimes seem like a daunting challenge. Luckily, there are two things that can help: GitLab Flow and GitLab Duo. GitLab Flow is a prescribed approach to help organizations successfully apply DevSecOps processes. GitLab Duo is a powerful set of AI-powered capabilities within the GitLab DevSecOps Platform that can help organizations develop code, improve operations, and secure software more efficiently. Combined, GitLab Flow and GitLab Duo can help organizations achieve significant improvements in end-to-end workflow efficiency, which can lead to even higher levels of productivity, deployment frequency, code quality and overall security, and production resiliency and availability. In this article, we delve into how GitLab Flow and GitLab Duo can be used together to help organizations be successful with DevSecOps.
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What is GitLab Flow?
GitLab Flow is a prescribed and opinionated end-to-end workflow for the development lifecycle of applications when using GitLab, an AI-powered DevSecOps platform with a single user interface and a single data model. GitLab Flow is based on best practices and lessons learned from customer feedback and our dogfooding. Furthermore, GitLab Flow spans across the stages of the DevSecOps lifecycle, forming an efficient workflow with an inner feedback loop for reviewing a specific update and an outer feedback loop for improving the entire application, as well as the development lifecycle itself.
As you can see by the many stages in GitLab Flow, there is much more to developing software than writing code. Below, we'll dive into each step of GitLab Flow and how GitLab Duo can help.
Planning
The first portion of GitLab Flow is planning, which sits on the outer feedback loop of GitLab Flow. It encompasses issues, merge requests, epics, milestones, iterations, release, release evidence, and more. Let’s cover what roles these components play in GitLab Flow and how GitLab Duo can help.
Issues
Issues are where product problems or new features are defined and where team members can collaborate. As an issue is created, you can populate its title and then leverage GitLab Duo Issue description generation capability to help enrich the description field, saving time and effort. Because many stakeholders can participate in comment threads on an issue, Discussion summary is an AI-powered capability in GitLab Duo that can summarize hundreds of comments on an issue into a concise paragraph so that a stakeholder can quickly get caught up with the conversation, jump into the discussion, and become productive right away.
Issues can be organized and visualized in issue boards, which are a software project management tool that can be used as kanban or Scrum boards. These boards help teams plan, organize, and visualize a workflow for a feature or product release. Different categories of boards can be created and issues can be moved from one board to another one with a simple drag and drop.
Merge requests
Merge requests are where solutions are developed. As release components, issues and merge requests provide the auditability and tracking of application changes done by stakeholders, such as DevOps and platform engineers, system and database administrators, security engineers, and developers. In addition, issues and merge requests are key inputs for the release planning process.
Merge requests can be individually created or created from an issue. Creating a merge request from an issue automatically relates it to that issue so when the merge request is merged its associated issue is automatically closed. Merge requests can also be manually related to an issue.
Like issues, merge requests can include a long list of updates to a feature branch by many stakeholders. Collaborators who need to familiarize themselves with or understand all of the updates included in a merge request can take advantage of the Merge request summary capability in GitLab Duo to quickly get caught up on the changes. In addition, collaborators can invoke GitLab Duo Code Merge request template population, which uses a pre-created merge request template and automatically fills in the content for sections in it. Description templates provide a way to standardize and optimize collaboration and communication across the development lifecycle and GitLab Duo speeds this up even more!
Issues with the same theme can be grouped together in an epic to organize the work to be done. Epics can have child issues and sub-epics and/or be linked to epics across the organization. Iterations can be used to track sprints of work, and can be manually scheduled or scheduled automatically using GitLab iteration cadences to streamline planning workflows. In addition, iterations include burndown and burnup charts. Burndown charts help track overall progress towards a project's total scope, while burnup charts track the daily total count and weight of issues added to and completed in a given timebox.
Milestones
Teams can use milestones to organize issues and merge requests into a cohesive group with an optional start date and an optional due date. Milestones are typically used to track releases and can track issues and merge requests at a project level or group level. Similar to iterations, milestones also provide burndown and burnup charts to show progress.
Milestones can be associated with a release, whose automated creation generates many artifacts, including the release evidence. The release evidence is an automatically collected snapshot of data that’s related to the release. In addition to test artifacts and linked milestones, job artifacts can optionally be included in the release evidence, which can facilitate internal processes such as external audits.
Epics, milestones, and iterations can be visualized via the Roadmaps page, which helps track release progress and streamline the release process.
Once the planning takes place, the work towards the resolution of a problem or a new feature can start. This happens in merge requests. Let’s delve deeper into how that happens in GitLab Flow.
Merge requests and pushing code
The second portion of GitLab Flow is related to merge requests and pushing code. As mentioned earlier, merge requests are where solutions are developed through collaboration among stakeholders across the organization. This collaboration can happen in a distributed manner and asynchronously. Participants can take advantage of collaborative capabilities, such as tagging, inline suggestions, inline comments, merge request comments, review threads, and review requests, which can help improve code quality, availability, reliability, and performance. Right after the creation of the merge request is the start of the GitLab Flow inner feedback loop, which is where code and fix pushes, test and scan runs, and collaboration and update reviews take place.
Pipelines
As updates are applied to a feature branch via merge requests, pipelines — if defined — are automatically executed. Pipelines can have multiple stages and jobs to build and test, and then deploy the application or microservice to a review environment. In that review environment, the updates can be dynamically verified before they are merged to the main branch. This automation helps streamline the application update and review processes.
In addition, as DevSecOps teams make updates to the application via merge requests, they have a variety of AI-powered capabilities at their disposal. As they write or update code, GitLab Duo Code Suggestions recommends code that should come next and the developer can choose to accept or ignore the recommendation. Code Suggestions support code generation via prompts as well as code completion as you type. Code Suggestions can help improve the programming experience by reducing errors and helping developers write code faster, which can help enhance production code quality. Code Suggestions also can lead to higher developer productivity and faster iterations and rollouts.
As different stakeholders within the organization participate in the development or review of applications, they may encounter code that is poorly documented, complex or difficult to understand, or is written in a programming language unfamiliar to them. The GitLab Duo Code explanation capability explains code in natural language so that everyone can understand the code and get up to speed quickly.
Moreover, when updates are committed to the feature branch, the GitLab Duo Suggested reviewers capability uses the changes in a merge request and a project’s contribution graph to suggest appropriate reviewers in the reviewer dropdown in the merge request sidebar. The list includes users that are knowledgeable about a specific aspect of the application and would be the best candidates to review the updates. Developers save time by not having to search and identify adequate reviewers, streamlining the review process and avoiding delays and low-quality reviews.
When developers make changes to the code, they often don't include a comment in the merge request about the specific changes they made. The GitLab Duo Merge request summary capability allows the author of merge request changes to use AI to generate a natural-language comment that summarizes the updates to the code. Reviewers then can better understand the changes and streamline the entire review process
As reviewers review updates to the code in a merge request, they can create a review block, which can consist of many comments spanning many source files. To help the original author of the updates better understand the feedback provided by the reviewer in a long review block, the GitLab Duo Code review summary capability generates a natural-language summary of the reviewer’s feedback. This enables better handoff between authors and reviewers, streamlining the review process.
Furthermore, when developers add new code via a merge request, they can leverage the GitLab Duo Test generation capability to use AI to generate unit tests for the new code. This can help to increase developer productivity, improve test coverage, and catch bugs early in the development lifecycle. Developers can also leverage GitLab Duo Chat, which is always accessible, to refactor code and generate in-line documentation, e.g. docstrings, for their source code.
While pipelines execute on branch updates, they can include automated tests and scans, which helps in shifting security left.
Shifting security left
The third portion of GitLab Flow is shifting security left, which is also part of the GitLab Flow inner feedback loop.
In addition to DevOps and platform engineers, system and database administrators, and developers, some of the stakeholders collaborating in a merge request may be concerned about security and compliance, which is where automated tests and security scans play a role. Scans can be simply included in a pipeline via readily available templates and/or can be automatically executed within a merge request pipeline. GitLab provides a broad set of built-in security scanners and analyzers that can be leveraged by GitLab Flow, but the DevSecOps platform can also accommodate third-party and custom scanners.
GitLab Flow shifts security left in the pipeline to detect and resolve defects as early as possible in the software development process. It is much simpler and cheaper to fix vulnerabilities early in the development cycle than once the application is in production, where an unscheduled outage can affect your users and revenue.
The built-in security scanners and analyzers provided by GitLab include: unit testing, infrastructure-as-code (IaC) scanning, static application security testing (SAST) scanners, dependency scanning, secret detection, container scanning, API security, web API fuzz testing, and coverage-guided fuzz testing. In addition, GitLab provides a variety of security dashboards and reports to manage and visualize vulnerabilities, such as the Dependencies list, Security dashboard, Vulnerability Report, and vulnerability pages.
To help developers and security engineers better understand and remediate vulnerabilities more efficiently, the GitLab Duo Vulnerability explanation capability provides an explanation about a specific vulnerability, how it can be exploited, and, most importantly, a recommendation on how to fix the vulnerability. Developers can also take advantage of GitLab Duo Vulnerability resolution, which automatically creates a merge request that includes code changes to fix the vulnerability. These AI-powered capabilities can help streamline and optimize the process of securing and hardening an application to prevent vulnerabilities that can be exploited by cyber attacks in production.
Besides SAST scanners, GitLab provides dynamic application security testing (DAST) scanners, which require a running application. When leveraging these scanners, GitLab is capable of automatically provisioning a DAST environment for the DAST scans and then performing a complete cleanup of all resources post-DAST testing. In addition, for running containers, GitLab provides operational container scanning, which scans container images in your cluster for security vulnerabilities.
The scans mentioned above can be executed automatically within a merge request pipeline or, in some cases, can be scheduled for execution via scan execution and merge request approval policies. These policies can be defined via the GitLab UI or YAML files and are configured in a separate project, allowing segregation of duties for reusability, maintenance, and management. Scan execution policies require that security scans be run on a specified schedule or with the project pipeline, and merge request approval policies take action based on scan results. Security engineers or teams can define these policies to enforce security processes across the organization and GitLab Flow may encounter or leverage these as it spans through its steps.
To enforce security and compliance across projects in your organization, you can use compliance labels and pipelines. Compliance labels and pipelines can be made mandatory to execute before a project’s own pipeline. With this approach, you can ensure that all teams within your organization meet your security and compliance standards. In addition, you can secure your applications against cyber attacks, conform to government compliance standards, and always be audit-ready.
The main goal of all of these GitLab Flow security prescriptions is to fix vulnerabilities early in the development cycle rather than once the application is in production, where remediating a vulnerability can prove to be very costly in reputation and revenue.
As vulnerabilities are mitigated within the GitLab Flow inner feedback loop and more updates are applied to the application in the feature branch, stakeholders need to re-review these updates to ensure that the updates have taken place and no regressions have inadvertently been introduced.
Continuous review
The next portion of GitLab Flow is reviewing features, which prescribes the continuous review of applications. Reviewing features involves the ability to stand up a review environment to which the interim application (feature branch) is deployed so that stakeholders can review it in real time and provide feedback. The interim application can then be continuously adjusted until it is ready to be merged to the main branch. GitLab Flow also prescribes the cleanup of all provisioned review environment resources at the moment when the merge request is merged to the main branch.
This iterative automated review process is part of the inner feedback loop in GitLab Flow. As mentioned above, within the inner feedback loop, GitLab Duo capabilities like Code explanation, Code Suggestions, Suggested reviewers, Merge request summary, Merge request template population, Code review summary, Vulnerability explanation, Vulnerability resolution, and Root cause analysis are prescribed by GitLab Flow to enable a better handoff between authors and reviewers and streamline the entire review process.
The GitLab Flow inner feedback loop terminates when all review items are addressed and the merge request is approved and merged to the main branch, which triggers the deployment of the application to production.
Deploying applications and infrastructure
Depending on an organization’s needs, either continuous delivery or continuous deployment is prescribed by GitLab Flow. Whereas continuous delivery is the frequent release of code by triggering the deployments manually (e.g., to production), continuous deployment is the automated release of code (e.g., to production) without human intervention. Let’s cover continuous delivery first.
As you release your software using continuous delivery, you have a few deployment options. You can establish a freeze window and then deploy using advanced deployment techniques, such as canary, blue/green, timed, and incremental rollouts. Incremental rollouts can lower the risk of production outages delivering a better user experience and customer satisfaction. Advanced deployment techniques can also improve development and delivery efficiency, streamlining the release process.
As you release your software using continuous deployment, all changes/updates go directly to production. Progressive delivery approaches like feature flags, which allow you to separate the delivery of specific features from a launch, are a good way to reduce risk and manage what functionality to make available to production users. Feature flags support multiple programming languages and allow developer experimentation and controlled testing. You can even use feature flags to roll out features to specific users.
Although GitLab supports all these deployment approaches, GitLab Flow allows for the adoption of the approach that best fits the organization and/or specific project needs.
Monitoring applications and DevSecOps processes
Once your application has been deployed to production, it needs to be continuously monitored to ensure its stability, performance, and availability. In addition, as the DevSecOps processes execute, they are measured, providing the opportunity to improve their performance and efficiency. The monitoring capabilities are provided by GitLab and, as such, can be leveraged by GitLab Flow.
For running containers, GitLab provides operational container scanning (OCS), which scans container images in your cluster for security vulnerabilities. These scans can be automated by scheduling them when to run and any found vulnerabilities are automatically displayed in a security dashboard. The OCS can help keep your cluster applications secure and preempt any cyber attacks that can lead to leaks of private data and even cause unexpected outages.
Error tracking allows developers to discover and view errors generated by their application. All errors generated by your application are displayed in the Error Tracking list in GitLab. Error tracking can help with availability and performance of your applications by detecting and resolving unexpected application conditions fast.
GitLab can accept alerts from any monitoring source, including Prometheus, via a webhook receiver. As alerts come in, they are displayed in the GitLab Alerts list, from which you can manually manage them. Alerts can also automatically trigger the creation of incidents, ChatOps, and email messages to appropriate individuals or groups. All these capabilities streamline the alert resolution and management process.
As incidents are created, due to production problems, they appear in the GitLab Incidents list for incident management. You can manage one or more incidents, sort them, search them, assign them, set their statuses, and even see their SLA preset countdown timer. Moreover, you can create on-call schedules and rotations, escalation policies, and set up paging and notifications to handle incidents. In addition, you can link an incident to an alert so that when the incident is closed, its associated alert is automatically resolved. Incident timelines are another capability for executives and external viewers to see what happened during an incident, and which steps were taken for it to be resolved. All these capabilities streamline the incident management process so that they can be resolved as quickly as possible.
Audit events track important events, including who performed the related action and when in GitLab. These events are displayed in the GitLab Audit Events list and provide, among others, the action that was taken on an object, who did it, and the date and time of its occurrence.
All the lists and dashboards mentioned above can help preempt out-of-compliance scenarios to avoid penalties as well as streamline audit processes. For your running applications, they generate the data and metrics that can be used in the GitLab Flow outer feedback loop to help improve and optimize your applications and lower the risk of unscheduled production outages.
Continuous improvement
When applying GitLab Flow, you also have the opportunity to use the insight that GitLab provides in the form of end-to-end process metrics dashboards to continuously improve not just your application but also your software delivery performance. These dashboards and their metrics are auto-generated by GitLab and are always available.
The Value Stream Analytics dashboard
You can track and monitor your application development lifecycle through the Value Stream Analytics Dashboard, where you can check project or group statistics over time. This dashboard is customizable but you can get started quickly by creating a value stream using a GitLab-provided default template. The default dashboard displays metrics for each of the pre-defined stages of your value stream analytics, namely Issue, Plan, Code, Test, Review, and Staging, as well as a graph with the average time to completion for each. It also shows the value stream analytics key metrics: lead time, cycle time, new issues, commits, and deploys. You can use these metrics to find areas of improvement in the stages of your value stream.
DORA metrics dashboard
To view the performance metrics that measure the effectiveness of your organization’s development and delivery practices, GitLab provides the DORA (DevOps Research and Assessment) metrics dashboard, which displays four key metrics: Deployment Frequency, Lead Time for Changes, Time to Restore Service, and Change Failure Rate. Deployment Frequency measures how often your organization deploys code to production or releases it to end users. Lead Time for Changes measures how long it takes to go from code committed to code successfully running in production. Time to Restore Service measures the time needed to restore services to the level they were previously, in case of an incident. Finally, Change Failure Rate is the percentage of changes to production or released to users that resulted in a degraded service (for example, a change that caused a service impairment or outage) and subsequently required remediation (required a hotfix, rollback, patch). These four key metrics are outcomes of your current processes and give you the opportunity to improve the factors and capabilities that drive them.
Customize your dashboard
Another dashboard is the Value Streams Dashboard, which is a customizable dashboard that enables decision-makers to identify trends, patterns, and opportunities for software development improvements. The metrics shown are the DORA metrics followed by the value stream analytics flow metrics and counts for critical and high vulnerabilities for the month to date, the two preceding months, and the past six months.
GitLab Duo can also help in your continuous improvement efforts. For example, the Value stream forecasting capability takes historical data and uses data trends across your development lifecycle to predict the future behavior of your value stream metrics. You can use these predictive analyses in your optimization initiatives.
All these dashboards and the metrics they report on are part of the GitLab Flow outer feedback loop to help you lower the risk of unscheduled production outages and improve and optimize your applications and DevSecOps workflows.
AI impact analytics
To better understand the impact of the use of GitLab Duo (or AI) along the entire development life cycle, you can check the AI Impact analytics, from where you can see how the adoption of GitLab Duo Code Suggestions impacts other performance, quality and security metrics. You can visualize the last six months of AI adoption and its impact on other metrics, such as cycle time, lead time, deployment frequency, change failure rate, and critical vulnerabilities over time.
AI impact analytics help to measure adoption, effectiveness and benefits that AI brings to teams and organizations and also to identify areas for improvement.
Why use GitLab Flow?
GitLab Flow is a prescribed approach, practiced by our customers and users worldwide, that can provide the following benefits:
- Higher productivity via the automation capabilities provided by GitLab and its single user interface and data model, all leveraged by GitLab Flow
- Accurate insights into the end-to-end DevSecOps lifecycle to support continuous improvement
- Built-in dashboards and metrics that can help you optimize your applications and DevSecOps processes
- Higher code quality and improved reliability and availability of your applications
- Better application security through built-in security scanners and capabilities
- Compliance- and audit-readiness via built-in compliance features
- Shorter cycle times that can help you increase deployment frequency
- Continuous review enabled by the GitLab Flow inner feedback loop
- The GitLab Flow inner feedback loop can help you optimize application updates leading to better code quality and higher reliability and availability of your applications
- The GitLab Flow outer feedback loop can help you improve your applications as well as the development lifecycle itself
- High levels of collaboration among stakeholders in your organization
- Shifting security left to help find vulnerabilities in applications before they make it to production to avoid costly, unscheduled outages
- Lower risk when deploying to production via the advanced deployment techniques and progressive delivery approaches supported by GitLab
- AI-powered capabilities that span across the entire development lifecycle and can boost productivity, code quality, continuous improvement, security and compliance, and more
- Support for cloud-native and non-cloud-native applications
- Multi-cloud support for hybrid/multi-cloud applications
- Shifting security left to help you find vulnerabilities in your applications before they make it to production so that you can avoid costly unscheduled outages
How can you get started with GitLab Flow? Leveraging GitLab Auto DevOps or parts of it is a good starting point for applying GitLab Flow principles to your application development lifecycle.
GitLab Flow and Auto DevOps
Auto DevOps applies GitLab Flow throughout all its stages and jobs. You can think of it as a good example for the instantiation of GitLab Flow.
Auto DevOps is a collection of predefined, out-of-the-box CI/CD templates that auto-discover the source code you have. Based on best practices, these templates automatically detect, build, test, deploy, and monitor your applications.
The Auto DevOps pipeline shifts work left to find and prevent defects as early as possible in the software delivery process. The pipeline then deploys the application to staging for verification and then to production in an incremental/timed fashion.
Auto DevOps gets you started quickly, increasing developer productivity, and it can be easily customized to your needs, with support for the most common programming frameworks and languages. Auto DevOps is modular, customizable, and extensible, which allows you to leverage pieces of it in your pipelines or apply all of it for your application.
Get started
Combine GitLab Flow and GitLab Duo today to achieve significant improvements in end-to-end workflow efficiency that can lead to even higher levels of productivity, deployment frequency, code quality and overall security, and production resiliency and availability.
If you'd like to see a workflow in action that combines GitLab Flow and GitLab Duo and how it can benefit you, watch the following video:
