Azure DevOps Integration for Smarter Project Management

Azure DevOps offers a robust platform for project management, and integrating it with other tools can take your workflow to the next level.

By integrating Azure DevOps with GitHub, you can enable seamless version control and collaboration across your team.

With Azure DevOps, you can manage your entire project lifecycle, from planning to deployment, all in one place.

Azure DevOps also integrates with popular project management tools like Jira and Trello, allowing you to customize your workflow to suit your needs.

Azure DevOps Integration is a powerful feature that allows you to automate data exchange between tools, enabling seamless collaboration between teams. By integrating Azure DevOps with other platforms, you can streamline your development processes and improve efficiency.

With Azure DevOps integration, you can automate the synchronization of test cases, defects, and requirements, ensuring that every update and status change is reflected across both platforms. This is achieved through real-time updates and bi-directional synchronization, as seen in the integration with qTest and Tricentis Tosca.

To get started with Azure DevOps integration, you'll need to set up global settings, such as specifying the Azure DevOps collection/organization URL and personal access token. This will allow you to import your Azure DevOps repositories into SonarQube and enable two-way sync between tools.

Here are some common use cases for Azure DevOps integration:

Azure DevOps Integration is a powerful tool that can help streamline your development process. It allows you to integrate Azure DevOps with other tools like Jira and ServiceNow, enabling seamless collaboration and data exchange between teams.

One of the key benefits of Azure DevOps Integration is that it eliminates data duplication and swivel-chairing between tools, saving time for your engineers. You can also improve data accuracy and quality by avoiding human error and poor data fidelity.

With Azure DevOps Integration, you can automate traceability and provide more autonomy to each development team to use the tool that best meets their needs. This is especially useful for teams that need to collaborate on joint projects, as it ensures that status updates, default and custom fields, comments, attachments, and relationships are visible in both tools instantly.

You can also use Azure DevOps Integration to automate data exchange between tools, such as incident tickets in ServiceNow flowing to Atlassian Jira as a bug, and then to Azure DevOps for resolution. This creates a collaborative environment where teams can work together seamlessly.

Here are some common use cases for Azure DevOps Integration:

Integrating ServiceNow with Azure DevOps can be a game-changer for your team's productivity. With integration, you can save time by eliminating data duplication and swivel-chairing between tools.

By automating the flow of data, you can boost collaboration between support engineers and developers. This is made possible by exchanging updates, comments, and screenshots instantly.

One of the biggest benefits of integration is resolving production issues faster. Automating the flow of issues between support and dev can save you a lot of time and hassle.

Here are some key benefits of integrating ServiceNow and Azure DevOps:

An incident in ServiceNow can flow directly to Azure DevOps as a bug to be addressed, making it easier to track and resolve issues. This seamless integration also makes status updates, default and custom fields, comments, attachments, and relationships visible in both tools instantly.

Planview Hub provides scalable, sophisticated near real-time integrations with Microsoft Azure DevOps and your toolchain to remove manual efforts and overhead.

With Planview Hub, you can integrate Azure DevOps with other tools seamlessly, allowing teams to collaborate on joint projects without any hassle. This integration enables the flow of data between tools, making it easier to manage projects and track progress.

Planview Hub is 4x faster to set up and 10x easier to maintain and scale than any other integration solution out there.

Here are some benefits of using Planview Hub for Azure DevOps integration:

With Planview Hub, you can automate the flow of data between tools, saving time for your team members and eliminating data duplication or swivel-chairing between tools.

Azure DevOps Integration is a game-changer for teams looking to streamline their development and testing processes. By integrating Azure DevOps with other tools, teams can automate data exchange, eliminate duplication, and boost collaboration.

Automating data exchange between tools is a key benefit of Azure DevOps integration. This can include features like ADO flowing to Jira as an epic, or child stories created in Jira flowing back to ADO as child Product Backlog Items (PBIs).

Teams can exchange comments in real-time from within their tools, ensuring that everyone is on the same page. Status updates also appear in both tools instantly, reducing the risk of errors or misunderstandings.

With integration, teams can save time, boost collaboration, and resolve production issues faster. They can also improve data accuracy and quality, and automate traceability for seamless compliance and navigation.

Here are some key benefits of Azure DevOps integration:

Some specific examples of Azure DevOps integration include:

By integrating Azure DevOps with other tools, teams can create a seamless workflow that improves collaboration, reduces errors, and increases efficiency.

Integration with UiPath ensures the synchronization of automation items and immediate updates, maintaining traceability of requirements and tests across multiple systems and departments within seconds.

By integrating UiPath with Azure DevOps, your teams can harness UiPath's automation capabilities alongside Azure DevOps' development tools, allowing for progress tracking and coordination.

This integration provides testers with early visibility into requirements, improving product quality by giving QA teams ample time to plan good test coverage.

Improving data accuracy and quality is another benefit, as it avoids human error and poor data fidelity.

Automating traceability ensures proper test coverage when a requirement changes, by tracing the links between requirements and their tests.

The integration also aligns testing teams across multiple testing tools, improving product quality by seamlessly supporting the use of specialized testing tools.

Here are some key benefits of UiPath and Azure DevOps integration:

The integration automatically establishes traceable links between automation tasks and development work items, ensuring more precise compliance reporting tailored to the unique needs of your UiPath workflows.

This integration enables your testing, development, and quality assurance teams to work together seamlessly, effortlessly transferring test cases, automation tasks, test defects, and other essential work items between the two platforms.

Bi-directional synchronization ensures that status updates, default and custom fields, comments, attachments, and relationships are visible in both tools instantly.

Planview Hub's model-based integration allows for fast and easy integration setup, making it easy to get started.

AdaptiveWork and Azure DevOps can be integrated to streamline project management and development processes.

This integration enables seamless collaboration and data exchange between project management and development teams.

Projects and tasks in AdaptiveWork are seamlessly integrated into Azure DevOps, aligning project management efforts with development workflows.

Updates in ADO, including sprint progress, backlog changes, and resource allocations, are reflected back in AdaptiveWork, ensuring a unified project view.

Real-time synchronization of updates fosters a transparent and collaborative environment, bridging the gap between project management and agile development.

Planview Hub’s model-based integration ensures a smooth and rapid setup, empowering teams to focus on delivering value through aligned execution.

Here are some key benefits of integrating AdaptiveWork and Azure DevOps:

To set up two-way sync webhooks, you'll need to create two webhooks in Azure DevOps: one for creating items and one for updating them. This is required for each integration you add, even if the project already has an Aha! webhook configured.

The process of setting up webhooks involves copying the webhook URL from Aha! and pasting it into Azure DevOps. This is a crucial step that ensures seamless communication between the two platforms.

In Aha!, you'll need to copy the webhook URL from the Enable step of the integration configuration. This URL will be used to trigger the creation of a new webhook in Azure DevOps.

You can add a new service hook in Azure DevOps by clicking the + Create subscription button on the Service hooks page. From there, you can select the Web Hooks service and choose the Work item created trigger to set up the creation webhook.

Similarly, you can set up the update webhook by selecting the Work item updated trigger from the dropdown menu. This will enable Aha! to receive updates in real-time, ensuring that your data remains synchronized across both platforms.

Here's a quick rundown of the steps:

To configure Azure DevOps, you'll need to create a new project in Azure DevOps if you don't already have one. You can do this by following the guide on Microsoft documentation.

To run your Katalon project on Azure DevOps, you'll need to configure your pipeline. This involves opening your Katalon project in Azure DevOps, going to Repos > Files, and clicking Set up build. From there, you can select Starter pipeline and review your pipeline YAML section.

In your pipeline YAML section, you can refer to the sample script to edit the YAML file. With the Katalon for Azure DevOps extension, you can set up your pipeline using the assistant in an easier way. You can also use the Tasks section to search for the Execute Katalon Studio Tests task and configure it to run your tests.

Here are the steps to configure the Execute Katalon Studio Tests task:

To add a SonarQube service endpoint in Azure DevOps, you need to declare your SonarQube server as a service endpoint in your project settings. This is a crucial step to integrate SonarQube with your Azure DevOps project.

In Azure DevOps, go to Project Settings > Service connections. This is where you can manage all your service connections, including SonarQube.

To create a new SonarQube service connection, select New service connection and then select SonarQube from the service connection list. You'll be prompted to enter your SonarQube Server URL, an authentication token, and a memorable Service connection name.

Once you've entered the required information, select Save to save your connection. This will create a new service endpoint in your Azure DevOps project settings.

Here's a step-by-step guide to creating a SonarQube service endpoint:

With a SonarQube service endpoint set up, you can now use it to import your Azure DevOps repositories into SonarQube, as described in the article section "Importing your Azure DevOps repositories into SonarQube".

The configuration structure of Azure DevOps is a crucial aspect to understand. The root key of the integration configuration is the resources key, which specifies the resources you want to query from your organization.

You can use the -kind: repository specifier to specify an object from the Azure DevOps API. The selector key is used to filter the resources you want to query.

The following resources can be used to map data from Azure DevOps: repository, work item, and board. You can reference any field that appears in the API responses linked in the Azure DevOps integration API reference.

To create multiple mappings of the same kind, you can add another item to the resources array. This allows you to map different fields to different Port entities.

Here's a breakdown of the configuration structure:

Pay attention to the value of the blueprint key, if you want to use a hardcoded string, you need to encapsulate it in 2 sets of quotes, for example use a pair of single-quotes (') and then another pair of double-quotes (").

To configure your pipeline in Azure DevOps, you need to create a new pipeline or edit an existing one. You can start by adding a Prepare Analysis Configuration task, which is mandatory for .NET solutions and Java projects. This task helps integrate seamlessly with MSBuild, Maven, and Gradle tasks.

For .NET projects, you'll need to add a Run Code Analysis task after your build task, and then a Publish quality gate Result task. You should also enable continuous integration under the Triggers tab of your pipeline.

For C family projects, you'll need to insert specific steps in your build pipeline, including making the Build Wrapper available on the build agent, adding a Prepare analysis configuration task, running your build through a command line, and then adding a Run code analysis task.

To construct a pipeline in Azure DevOps, you'll need to add specific tasks in a particular order. For example, when working with .NET projects, you'll need to add a Prepare Analysis Configuration task before your build task, followed by a Run Code Analysis task and a Publish quality gate Result task.

You can also use the Prepare Analysis Configuration task with Maven or Gradle projects, but you'll need to add a new Prepare Analysis Configuration task before your build task and a Publish quality gate Result task.

The order of tasks may vary depending on the type of project you're working with. For instance, when working with C family projects, you'll need to make the Build Wrapper available on the build agent, followed by a Prepare analysis configuration task, a Command Line task to run your build, a Run code analysis task, and a Publish quality gate Result task.

In general, the tasks you'll need to add to your pipeline will depend on the type of project and the build technology you're using.

Here's a summary of the tasks you may need to add to your pipeline:

Remember, the specific tasks and order may vary depending on your project's requirements and build technology.

Ingesting Git objects is a key part of pipeline configuration, allowing you to bring in data from the Azure DevOps API.

This integration uses a YAML configuration to describe the ETL process, finding a balance between being too opinionated and too broad.

The approach allows you to ingest a variety of object resources provided by the Azure DevOps API.

The integration makes use of the JQ JSON processor to select, modify, concatenate, transform, and perform other operations on existing fields and values from the different Azure DevOps API routes.

This ETL process enables you to perform operations on data from the Azure DevOps API into the desired data model.

The configuration reflects a thoughtful approach that considers the needs of different users, avoiding unnecessary complexity.

Here is an example snippet from the config which demonstrates the ETL process for getting pull-request data from Azure DevOps into the software catalog:

This ETL process is a great example of how to bring in data from Azure DevOps into the developer portal.

The YAML configuration allows you to describe the ETL process to load data into the desired data model.

This approach has proven to be effective in various use cases, providing a flexible and scalable solution for pipeline configuration.

Maintaining code quality and safety is a top priority in Azure DevOps, and that's where quality gate policies come in. These policies can analyze code and identify issues in all pull requests on your project.

You can prevent the merge of pull requests with a failed quality gate by adding a SonarQube/quality gate status check branch policy on the target branch. This is a highly recommended practice to quickly track, identify, and remediate issues in your code.

Preventing pull request merges when the quality gate fails is as simple as adding a branch policy. However, projects configured as part of a monorepo cannot use this status check branch policy to prevent pull request merges.

To report your quality gate status in Azure DevOps, you'll need to set up SonarQube to import your Azure DevOps repositories. This involves clicking the Add project button and selecting Azure DevOps from the drop-down menu.

SonarQube can also report your quality gate status to Azure DevOps pull requests for existing and manually-created projects. To do this, you'll need to set the project settings at Project Settings > General Settings > DevOps Platform Integration.

To ensure all your pull requests get automatically analyzed, add a build validation branch policy on the target branch. This will help you catch any issues early on and prevent them from making it into your main branch.

Here are the steps to set up quality gate reporting in Azure DevOps:

In a monorepo setup, you'll need to set up each SonarQube project to report quality gate status manually. This involves setting up projects as shown in the Reporting your quality gate status in manually created or existing project section.

You'll need to enable monorepo support by setting the Enable monorepo support setting to true at Project Settings > General Settings > DevOps Platform Integration. This is a crucial step to ensure your projects are correctly integrated.

You can create multiple configurations for each DevOps platform starting in Enterprise Edition. This is in contrast to Developer Edition, where you can only create one configuration per DevOps platform.

To ensure the correct project is being analyzed, adjust the Analysis Scope and pass your project names to the scanner. This will help you get accurate results.

Here's a summary of the different configuration options available:

Maintaining a high level of security and testing is crucial for any project. Using pull requests allows you to prevent unsafe or substandard code from being merged with your main branch.

You can set up branch policies to analyze code and identify issues in all of the pull requests on your project. These policies are optional, but they're highly recommended.

Ensuring your pull requests are automatically analyzed is a great way to catch issues early. Add a build validation branch policy on the target branch to make this happen.

This policy will automatically analyze all pull requests, giving you peace of mind and saving you time in the long run.

To set up your pipeline in Azure DevOps, you need to configure it by following these steps: Open your Katalon project in Azure DevOps, go to Repos > Files and click Set up build, select Starter pipeline, and review your pipeline YAML section. You can refer to our sample script to edit the YAML file.

To successfully execute your pipeline, you need to add the Execute Katalon Studio Tests task, enter the Katalon Runtime Engine version, and command arguments. You can also use the command builder to generate the arguments. For example, you can use the following command arguments: -retry=0 -statusDelay=15 -testSuitePath="Test Suites/New Test Suite" -browserType="Chrome" -apiKey=$(katalon_api_key).

If you are using Linux, you need to take a look at the fields that require additional configuration. You can add these inputs to the pipeline script by clicking Add. With the Katalon for Azure DevOps extension, you can set up your pipeline using the assistant in an easier way.

To set up a successful integration with Azure DevOps Server, you'll need to meet some prerequisites.

First and foremost, you'll need to be running Azure DevOps Server 2022, Azure DevOps Server 2020, or Azure DevOps Server 2019 (including Express editions).

You'll also need to have set a SonarQube Server base URL in Administration > Configuration > General Settings > General.

Here are the specific versions of Azure DevOps Server that are supported:

Importing your Azure DevOps repositories into Sonarqube allows you to easily create Sonarqube projects from your Azure DevOps repositories. If you're using Developer Edition or above, this is also the first step in adding pull request decoration.

To set up the import of Azure DevOps repositories, you'll need to set your global DevOps platform settings and add a personal access token for importing repositories.

Here are the steps to follow:

After setting your global settings, you can add a project from Azure DevOps by clicking the Add project button in the upper-right corner of the Projects homepage and selecting Azure DevOps.

You can also add a new Sonarqube service endpoint in your Azure DevOps project settings to connect your Sonarqube server. This involves entering your Sonarqube Server URL, an authentication token, and a memorable Service connection name.

Automatic branch detection is only available when using Git, which is useful when analyzing projects with Azure Pipelines.

To prepare your analysis configuration, you'll need to configure the required settings before executing the build. The Prepare Analysis Configuration task is mandatory and helps to integrate seamlessly with MSBuild, Maven, and Gradle tasks.

This task configures all the required settings before executing the build, making it a crucial step in the analysis process. If you're using .NET solutions or Java projects, this task will help to integrate seamlessly with MSBuild, Maven, and Gradle tasks.

To configure branch analysis, you'll need to use the following tasks in your build definitions: Prepare analysis configuration, Run code analysis, and Publish quality gate result. These tasks will help you analyze your projects and display the quality gate status in the build summary.

Here's a summary of the tasks you'll need to configure:

Note that you should disable git shallow clone to make sure the scanner has access to all of your history when running analysis with Azure DevOps. This will prevent errors like "Missing blame information..." and "Could not find ref...".

Filtering unwanted objects is a crucial step in managing the data that comes into your software catalog. You can use the selector and query keys to filter out objects that don't meet your criteria.

The Ocean Azure DevOps integration allows you to query objects from the work-item kind using WIQL, giving you fine-grained control over which work items are ingested. This is done by adding a wiql key with your WIQL query as the value within the selector object.

To filter out repositories with names starting with "service", you can use the query key like this. This is just one example of how you can use the query key to filter out unwanted objects.

The integration makes use of the JQ JSON processor to select and modify the objects that are ingested. This allows you to perform operations on existing fields and values from the different Azure DevOps API routes.