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As the automotive industry accelerates toward intelligent and autonomous vehicles,Advanced Driver Assistance Systems (ADAS)are
at the forefront of this transformation. From enabling adaptive cruise
control to collision avoidance and fully autonomous capabilities, ADAS
technologies are rapidly evolving. However, the complexity of developing
these systems demands innovative approaches, and this is wheresimulation and virtualizationhave become indispensable.
Virtualization and
simulation offer a cost-effective, scalable, and safe solution to test,
validate, and refine ADAS systems across every stage of the development
lifecycle. Let’s explore how these technologies are reshaping ADAS
innovation.
Simulation Across the ADAS Lifecycle
1. Requirement and Design Stage
Virtual Requirement Validation:
Virtual environments allow early-stage validation of system
requirements. For example, adaptive cruise control requirements can be
tested against real-world scenarios like dense traffic or high-speed
highways to ensure alignment with functional and safety standards.
Virtual Prototyping:
High-performance virtual prototypes of vehicles can simulate and test
initial designs without the need for physical prototypes. Most of the
tools available in market, enable comprehensive design testing, allowing
teams to experiment with sensor placement, software algorithms, and
vehicle dynamics.
Example:
Camera placement for ADAS features like blind spot detection can be
optimized virtually to maximize visibility and functionality.
2. Development Stage
Code Validation: Nightly builds
running on virtual platforms ensure software stability and allow
developers to detect and resolve bugs early in the cycle.
Component Simulation:
Individual ADAS components like ECUs or sensors can be tested in
isolation using simulation before integrating them into the system.
Fusion Testing:
Virtual environments enable testing of sensor fusion, such as radar and
video data integration, to ensure seamless processing for features like
lane-keeping and collision avoidance.
3. Testing and Validation Stage
Scenario Testing at Scale: Simulation tools allow extensive testing of ADAS configurations across diverse scenarios:
Weather Conditions: Simulating rain, snow, fog, or bright sunlight to ensure system reliability under extreme conditions.
Time of Day: Validating system functionality during daytime, dusk, and complete darkness.
Synthetic Data for Vehicle Runs:
Instead of physically driving millions of kilometers, synthetic data
simulates long-distance driving across varied terrains, traffic
densities, and edge cases.
Regulatory Compliance:
Virtual environments ensure systems meet global safety standards, such
as Europe’s mandate for advanced blind spot monitoring and driver
drowsiness detection.
4. Post-Production Updates
Continuous Improvement:
Over-the-air (OTA) updates for ADAS systems can be validated in virtual
environments before deployment. For instance, adding new traffic sign
recognition features can be tested virtually to ensure compatibility
with existing systems.
Key Applications of Virtualization in ADAS Development
Dynamic System IntegrationADAS
systems integrate multiple sensors like cameras, radar, and LIDAR,
which must work harmoniously. Virtual platforms enable real-time testing
of sensor fusion, ensuring accurate processing of inputs for features
like adaptive cruise control or highway assist.
Photorealistic 3D VisualizationSimulation
platforms provide high-resolution environments to replicate real-world
scenarios. These include traffic dynamics, weather effects, and
lighting conditions, enabling realistic testing of ADAS features like
pedestrian detection and lane departure warnings.
Massive Parallel TestingSimulation
enables the execution of thousands of scenarios in parallel,
dramatically reducing time-to-market. Complex situations, such as urban
intersections or highway merges, can be tested across various conditions
simultaneously.
Cost and Time EfficiencyBy
reducing the dependency on physical prototypes and road tests,
virtualization significantly lowers costs and accelerates development
timelines.
Fusion and Virtualization for Complex ADAS Scenarios
Radar and Video Fusion
Radar provides precise distance measurements, while
cameras enable object classification. Simulating the interaction of
these sensors ensures robust system performance for features like
collision avoidance.
Example: Testing how radar
and video work together during heavy rain or in low-light conditions
ensures reliability in real-world scenarios.
Central Computer Fusion
Modern vehicles rely on central computers that
process inputs from multiple sensors. Virtual simulations allow testing
of edge cases, such as sudden lane changes or unexpected obstacles, to
validate decision-making processes.
Weather Conditions: Validating emergency braking or lane-keeping under heavy snow, icy roads, or rain.
Urban Traffic: Testing pedestrian detection in densely populated areas with unpredictable pedestrian behavior.
Now a days suppliers/OEMs are also bringingdigital / virtual twins.
Virtual twins, or digital twins, are precise virtual models of physical
systems that enable simulation, analysis, and optimization in
real-time. In the context of Advanced Driver Assistance Systems (ADAS),
virtual twins replicate vehicle components and their environments to
test and validate features like blind spot monitoring and driver
drowsiness detection.
The Benefits of Virtualization and Simulation
Safety Assurance: Testing rare and critical scenarios in controlled virtual environments ensures robust system performance.
Cost Savings: Reducing physical testing and prototype development lowers costs significantly.
Scalability:
Simulation platforms support parallel testing, ideal for Continuous
Integration (CI), Continuous Deployment (CD), and Continuous Testing
(CT).
Early Validation: Validating requirements and designs in virtual environments ensures better decision-making and reduces rework.
The Road Ahead
Virtualization and simulation are more than tools—they’re enablers
of innovation in the ADAS ecosystem. By bridging the gap between
physical and digital testing, these technologies empower automakers to
build safer, smarter, and more efficient vehicles.
The future of mobility will rely on these advancements to deliver
reliable autonomous driving capabilities. As we move forward, how do you
see simulation shaping the automotive industry? Please share your
thoughts.
This article is posted in Linked in by Nusrat Bano
In today’s fast-changing automotive industry, Advanced Driver
Assistance Systems (ADAS) are transforming how vehicles interact with
their surroundings, making driving safer and more convenient. However,
creating and testing these advanced systems is not easy. The process
takes time, costs a lot, and needs many real-world tests.
This is where Artificial Intelligence (AI), Machine Learning (ML),
and synthetic data come in to change the game. Let’s see how they are
helping in ADAS development.
The Role of AI and ML in ADAS Development
1. Enhanced Data Processing:
What it does:AI
and ML process data from sensors like cameras, radar, and LIDAR. They
identify patterns, predict actions, and help vehicles make better
decisions.
Example:ML models predict pedestrian behavior in crowded cities, allowing vehicles to respond to unexpected situations.
Suppliers and OEMs:Companies
like NVIDIA, Bosch, and Tesla are using these technologies for tasks
like collision avoidance and adaptive cruise control.
2. Improved Test Scenarios:
What it does:AI creates test environments that mimic real-life conditions, testing situations like sudden braking or heavy traffic.
Example:AI simulations recreate rush-hour traffic to test systems in complex scenarios.
Suppliers and OEMs:Siemens and General Motors use AI-driven tools for ADAS validation.
3. Automation in Validation:
What it does:AI automates validation, reducing manual efforts and ensuring compliance with standards like ASIL D.
Suppliers and OEMs:Companies like AVL and BMW are using automated platforms to improve validation.
Synthetic Data: A Game Changer in ADAS Testing
1. What is Synthetic Data?
Synthetic data is computer-generated data that
mirrors real-world driving conditions. It includes variables like
weather, traffic, and time of day, allowing comprehensive testing.
2. Advantages of Synthetic Data:
Cost-Effective:Eliminates expensive physical tests by recreating them virtually.
Faster Development:Reduces time-to-market by feeding diverse scenarios to ML models.
Safe Testing:Allows testing of dangerous scenarios like high-speed crashes in virtual environments.
3. Examples of Use Cases:
Testing pedestrian detection, lane-keeping, and weather adaptability without needing real-world tests.
4. Suppliers and OEMs:
Suppliers like Bosch, NVIDIA, Ansys, and IPG
Automotive provide solutions with synthetic data technologies. OEMs like
Mercedes-Benz, Tesla, and Volvo use synthetic data to validate their
systems.
Integration of AI, ML, and Synthetic Data in the ADAS Lifecycle
1. Requirement and Design:
AI analyzes requirements against simulations, ensuring early design validation.
2. Development:
ML models trained with synthetic data refine ADAS features like object detection.
3. Testing and Validation:
AI simulations test ADAS features in parallel across extreme conditions.
4. Post-Production Updates:
Synthetic data ensures new features integrate well with existing systems.
Key Benefits of AI, ML, and Synthetic Data
Reduced Costs:Fewer prototypes and tests save money.
Faster Time-to-Market:Parallel simulations speed up development.
Scalability:Enables testing millions of kilometers without physical limitations.
The Road Ahead
AI, ML, and synthetic data are transforming the way ADAS systems
are developed. By reducing costs, saving time, and improving safety,
they are helping us move closer to fully autonomous vehicles.
As automotive companies continue to innovate, the future looks promising with smarter and safer vehicles on the roads.
As vehicles evolve into software-defined vehicles (SDV), they rely on a new development paradigm in which hardware and software are decoupled. The Automotive Software Factory (SWF) supports SDV by providing the infrastructure, tools, and processes necessary to develop and deploy complex software, often within a multi-platform environment that includes AUTOSAR, Embedded Edge software, and AAOS. This setup enables rapid, reliable delivery of software updates and fosters continuous innovation.
AUTOSAR Classic and Adaptive Platforms
AUTOSAR (AUTomotive Open System ARchitecture) is a global development partnership that provides standardized architecture, interfaces, and development tools for automotive software, enabling interoperability and modularity across suppliers and OEMs. The two main AUTOSAR platforms are:
1. AUTOSAR Classic Platform
Target: Primarily for ECUs (Electronic Control Units) managing safety-critical and time-deterministic tasks like engine control and braking.
Architecture: Based on a layered architecture (e.g., Basic Software, Runtime Environment, Application Layer).
Main Focus: Real-time systems with limited dynamic memory allocation.
Software Factory Context: In an SWF, AUTOSAR Classic modules are integrated, built, and validated against requirements using CI/CD pipelines. Classic AUTOSAR ECUs are simulated on virtual ECUs (vECUs) to verify functional safety and real-time constraints before physical testing.
Testing and Release:
Simulators: Tools like vECU, QEMU, or specific AUTOSAR emulators.
Hardware-in-the-Loop (HIL) Testing: Tools like dSPACE are used to simulate real-time responses for safety-critical ECUs.
2. AUTOSAR Adaptive Platform
Target: Designed for high-performance, non-safety-critical applications, such as ADAS (Advanced Driver Assistance Systems) and infotainment.
Architecture: Service-oriented architecture supporting dynamic memory management and multi-core processors.
Main Focus: Flexibility, Ethernet communication, and adaptive applications that can evolve over time.
Software Factory Context: In an SWF, the Adaptive Platform facilitates the rapid addition of new features. Adaptive AUTOSAR components are typically developed and tested in containerized environments or on the cloud for scalability.
Testing and Release:
Containerized Simulation: Deployed in a containerized format (e.g., Docker) for easy orchestration and testing.
Cloud-Based Testing: Simulation on cloud infrastructures like AWS Graviton for large-scale service validation and integration testing.
Edge computing in automotive enables processing data closer to the source, reducing latency, and enhancing real-time decision-making. An Embedded Edge Software stack based on Yocto Linux and tools like Eclipse LEDA and Kanto Container Orchestration is used to support these requirements.
1. Yocto Linux
Purpose: Used to create custom embedded Linux distributions for automotive applications. Yocto supports high configurability and is ideal for building minimal Linux setups optimized for automotive systems.
Software Factory Context: Yocto configurations are part of the software build pipeline, allowing for cross-compilation and testing across various hardware setups (e.g., ARM-based ECUs).
Testing and Simulation: Yocto-based builds can be tested on virtualized setups like QEMU or cloud-based emulations, ensuring they work reliably before deployment.
2. Eclipse LEDA
Purpose: A tool for edge device management and application lifecycle. It integrates with IoT frameworks, managing deployment, configuration, and monitoring.
Software Factory Context: LEDA assists in orchestrating edge applications, pushing updates, and monitoring them across distributed devices, often used in conjunction with CI/CD tools for automated management.
Containerized Applications: LEDA supports containerized application deployment (e.g., Kanto-based), providing flexibility for different types of edge applications.
3. Kanto Container Orchestration
Purpose: Orchestrates edge applications and services within containerized environments, supporting multi-node deployment.
Software Factory Context: In an SWF, Kanto manages and deploys containers at the edge, integrating with cloud infrastructure for remote updates and over-the-air (OTA) or firmware-over-the-air (FOTA) upgrades.
OTA/FOTA Services: OTA/FOTA management is integrated into the factory’s pipeline, allowing edge software to be updated seamlessly, improving the user experience and minimizing in-service time.
Android Automotive Operating System (AAOS)
Android Automotive OS (AAOS) is a full-stack, open-source platform for infotainment systems, built by Google, based on Android. It enables rich user experiences through multimedia, navigation, and in-vehicle services.
Key Components of AAOS in an SWF:
Infotainment and App Ecosystem: AAOS supports a diverse range of apps, enhancing the in-car experience with real-time data, location-based services, and media streaming.
Customizable Framework: OEMs can customize the AAOS platform for their specific needs while leveraging Android’s extensive developer ecosystem.
Software Factory Context: In the SWF, AAOS applications undergo a comprehensive build, simulate, test, and release cycle. Developers can create, test, and update AAOS apps via CI/CD pipelines, while containerized testing or cloud-based simulations (e.g., AWS Graviton) emulate real in-vehicle environments.
Testing: Use of QEMU and vECU for simulating AAOS applications and system updates, facilitating pre-deployment validation and user experience refinement.
Simulation, Testing, and Release Tools for SWF and SDV
In an Automotive Software Factory, simulation and testing are fundamental to ensuring software reliability and performance. Key simulation and testing tools include:
QEMU: A widely used open-source machine emulator and virtualizer for testing embedded systems like ECUs.
vECU: Virtual ECUs allow for software testing independent of hardware, accelerating development and reducing dependency on physical prototypes.
Cloud-Based Simulation (AWS Graviton): AWS Graviton-powered EC2 instances provide scalable cloud infrastructure for ARM-based simulation, essential for testing ARM-based automotive applications.
HIL and SIL Testing: Hardware-in-the-loop (HIL) and software-in-the-loop (SIL) simulations enable realistic testing of automotive systems in the SWF, verifying integration with real hardware components when needed.
CI/CD for Testing and Release:
Automated Test Frameworks: Tools like Google Test, Robot Framework, and Jenkins are used for automated testing across CI/CD pipelines.
Compliance Testing: Specialized tools (e.g., VectorCAST, Rapita Systems) ensure compliance with ISO 26262 and Automotive SPICE.
OTA and FOTA Updates: Integrated into the CD pipeline, OTA/FOTA services allow the SWF to deliver secure, real-time updates to vehicles in the field, enabling continuous improvement and new feature rollouts.
Summary: Automotive Software Factory for SDV
The Automotive Software Factory (SWF) for Software-Defined Vehicles (SDV) leverages cutting-edge technologies like AUTOSAR Classic and Adaptive platforms, Embedded Edge Software (Yocto Linux, Eclipse LEDA, Kanto), and Android Automotive OS to build, test, and deploy software with precision. Key processes such as CI, CT, and CD, supported by cloud-based simulations (AWS Graviton), HIL/SIL testing, and OTA/FOTA services, ensure automotive software can evolve rapidly while maintaining quality and safety standards.
This integrated approach provides the flexibility to innovate and the rigor to meet compliance, ensuring that SDVs remain secure, reliable, and capable of adapting to new demands. As a result, the Automotive Software Factory represents the foundation of next-generation automotive software development, leading to faster time-to-market, enhanced customer experiences, and sustained competitiveness in the evolving automotive landscape.
The automotive industry is evolving rapidly, with software playing a critical role in vehicle functionality, safety, and user experience. Modern vehicles are embedded with sophisticated software systems that manage everything from engine control to infotainment. As the demand for more advanced features grows, so does the complexity of automotive software. To meet these demands, automotive companies have adopted the Automotive Software Factory concept, an integrated approach focused on optimizing software development and deployment. Key to this factory approach are processes like CI/CT/CD (Continuous Integration, Continuous Testing, and Continuous Delivery), coupled with cloud-based simulations for robust testing and verification.
Automotive Software Factory Overview
An Automotive Software Factory streamlines software development for the automotive industry. It encompasses tools, methodologies, and infrastructure designed to automate and accelerate the software lifecycle while ensuring high-quality and compliant software. A typical automotive software factory involves:
Continuous Integration (CI) for ongoing development and integration.
Continuous Testing (CT) to maintain quality across every code change.
Continuous Delivery (CD) for faster deployment cycles.
The ultimate goal is to maintain a seamless workflow, providing real-time insights into software performance and enabling fast, reliable delivery.
Key Processes in an Automotive Software Factory
Continuous Integration (CI):
CI is the backbone of the software factory, allowing for regular code integration from multiple contributors. In an automotive context, CI ensures that new features or updates integrate smoothly into the existing software without causing system issues.
Key CI Activities:
Automated Builds: Frequent builds to compile, link, and package code.
Integration Testing: Basic tests to verify that new code doesn’t break existing functionality.
Continuous Testing (CT):
CT is essential in the automotive industry, where safety and reliability are paramount. Automated testing is conducted across the software lifecycle, from unit testing to system-level validation.
Key CT Activities:
Unit and Module Testing: Verifies individual code units’ functionality.
Integration and System Testing: Ensures compatibility between modules and assesses performance within the complete vehicle system.
Regression Testing: Confirms that new updates haven’t disrupted existing functions.
Safety-Critical Testing: For functional safety standards like ISO 26262, ensuring that software adheres to strict safety requirements.
Continuous Delivery (CD):
CD in the automotive context refers to the ability to push tested software updates or new features to production or for in-vehicle testing.
Key CD Activities:
Automated Release Pipeline: Smooth, error-free progression from development to deployment.
Rollback Mechanisms: Swift reversion to previous versions in case of issues.
Compliance Checks: Ensuring every release adheres to industry standards like Automotive SPICE.
Methods and Tools in CI/CT/CD for Automotive Software
The success of an Automotive Software Factory heavily relies on selecting the right methods and tools to support CI/CT/CD processes. Below are the commonly used methods and tools for each process:
Simulation and Model-Based Testing: MATLAB/Simulink for behavior modeling, IPG CarMaker for real-world scenario simulations.
Cloud-Based Testing: AWS Device Farm, Google Cloud for on-demand test environments.
CD Tools and Methods:
Containerization: Docker for isolated environments.
Orchestration: Kubernetes for managing deployment clusters.
Release Automation: Jenkins pipelines, ArgoCD for Kubernetes-native CD.
Monitoring and Logging: ELK Stack, Prometheus, Grafana for real-time insights.
Compliance Verification: tools like VectorCAST and Rapita Systems for runtime testing and compliance.
Benefits of Cloud-Based Simulation in Automotive Software Factory
Cloud-based simulation has transformed how automotive software is developed and tested. With cloud-based simulation, developers can perform extensive tests on virtualized infrastructure, reducing dependency on physical prototypes. Key benefits include:
Scalability and Flexibility:
Cloud platforms like AWS, Azure, and Google Cloud offer on-demand resources that scale up or down based on testing needs.
Multiple simulations can run concurrently, allowing for extensive testing in a fraction of the time required for physical testing.
Cost Savings:
Cloud-based simulations reduce the need for physical hardware and testing facilities.
Companies save costs associated with equipment maintenance and space for physical prototypes.
Faster Testing and Feedback Cycles:
Testing can be conducted in parallel across various virtual scenarios, accelerating feedback and fixing cycles.
Developers gain quicker insights, enabling faster adjustments and refinements.
Enhanced Collaboration:
Cloud-based simulation platforms enable real-time collaboration among geographically dispersed teams.
Teams can access the same simulation environment, test results, and data from anywhere, facilitating a seamless collaborative environment.
Improved Testing for Edge Cases and Rare Scenarios:
Simulations can replicate rare or hazardous conditions, such as extreme weather, to ensure the software behaves predictably.
This capability enhances the robustness of safety-critical systems, critical for autonomous and advanced driver assistance systems (ADAS).
Data-Driven Development:
Cloud simulations generate large data volumes that can be analyzed for insights into software performance.
Data analytics tools help improve predictive maintenance, error detection, and optimization of vehicle performance over time.
Challenges and Considerations
Despite its advantages, adopting a CI/CT/CD model with cloud-based simulations in automotive software development comes with challenges, such as:
Data Security and Compliance: Ensuring data protection and compliance with industry standards like ISO 26262 and Automotive SPICE.
Resource Management: Cloud simulations, while flexible, require careful resource management to avoid unexpected costs.
Integration Complexity: Integrating cloud-based simulations with on-premise systems and HIL/SIL setups can be complex.
To mitigate these challenges, companies should adopt a strategic approach, focusing on compliance, cost-control mechanisms, and robust integration frameworks.
Conclusion
The Automotive Software Factory concept, with its CI/CT/CD model and cloud-based simulations, is reshaping the future of automotive software development. By adopting these practices, companies can build reliable, high-quality, and compliant software faster and more efficiently. The flexibility and scalability provided by cloud-based simulations not only accelerate development but also improve testing rigor, enabling the creation of safer and smarter vehicles.
As the automotive industry continues to evolve, companies that embrace CI/CT/CD processes and cloud-based simulations will be better equipped to meet the demands of the next-generation vehicle software market, maintaining a competitive edge and ensuring the highest standards of quality and safety.
In this lesson, we’ll cover the essential concepts of Jenkins shared libraries.
We
are in an era of microservices, where modern applications are composed
of individually deployable components. Unlike monolithic applications,
this approach often requires multiple pipelines to manage the deployment
of each microservice.
With Jenkins' pipeline-as-code, you can
script your entire CI/CD process, treating it just like application
code. This allows you to version control your pipeline, ensuring that it
undergoes rigorous testing before being used for any application
deployments.
What is Jenkins Shared Library?
When we say CI/CD as code, it should be modular and reusable and mainly follow the DRY principles (Don’t repeat yourself). This is where Jenkins Shared Library comes into play.
Shared library – As the name indicates, it is a library that can be shared.
Jenkins Shared library is
the concept of having a common pipeline code in the version control
system that can be used by any number of pipelines just by referencing
it. In fact, multiple teams can use the same library for their pipelines.
You
can compare it with the common programming library. In programming, we
create individual libraries that anyone can use just by importing them
into their code.
Assume, you have ten Java microservices pipelines, the maven build step gets
duplicated on all the ten pipelines. Whenever a new service is added,
you will have to copy and paste the pipeline code again. Let’s say you
want to change some parameters in the Maven build step. You will have to
change it in all the pipelines manually.
To avoid pipeline code duplication,
we can write a shared library for Maven build, and in all the pipelines
we just have to refer to the Maven build library code. In the future
for any Maven build changes, you just have to update the shared library
code. It will be applied to all the pipelines using the Maven build
library.
Shared Library Github Repo
The Jenkins shared library examples used in this guide are hosted on a GitHub Repository.
Clone the repository to follow along with the guide.
This directory holds all the global shared library code that can be called from a Jenkins pipeline, including all the library files with a .groovy extension.
The name of each Groovy file in the vars directory corresponds to a function name in the pipeline.
For example, if you have a file named deployApp.groovy in the vars directory, you can call deployApp() directly in your Jenkins pipeline.
Here is a simple shared library code for Git checkout.
Don’t
worry about the syntax. We can generate it using the Jenkins pipeline
generator. In the upcoming lessons, we will look at it practically.
The vars directory also supports .txt files for the documentation of shared library code.
For example, if you have a file named maven-build.groovy, you can have a helper file named maven-groovy.txt.
You can write the respective shared library function help documentation
in markdown format in this file. The help file can be viewed from <your-jenkins-url>/pipeline-syntax/globals page.
2. src
Used for organizing more complex pipeline code. It follows a typical Java-style package structure.
Here, you can add custom structured and object-oriented code Groovy code to extend your shared library code. Also, you can import core Jenkins and its plugin classes using an import statement.
You might ask, when we have a vars directory, what is the need for src?
There
are scenarios where the groovy DSLs will not be flexible enough to
achieve some complex functionalities. In this case, you can write custom
Groovy functions in src and call them in your shared library code.
The src directory is added to the classpath during every script compilation. So we can directly use the classes defined in the src directory in Jenkinsfiles.
3. resources
All
the non-groovy files (e.g., text files, templates) required for your
pipelines can be managed in this folder. Typically files.
One such
example is that you might need a common JSON template to make an API
call during the build. This JSON template can be stored in the resources
folder and can be accessed in the shared library using the libraryResource function.
Or You can use a HTML file as a template for
sending HTML-formatted email notifications from your Jenkins pipelines.
In your pipeline script or shared library function, you can load this
template and replace the placeholders with actual values.
Real World Example
In
larger enterprises, it’s common to have central platform teams that
play a key role in standardizing and optimizing CI/CD workflows.
When
Jenkins is used within these organizations, these platform teams
typically develop shared Jenkins libraries in collaboration with
applcation teams to manage application and infrastructure deployment.
By
utilizing shared libraries, platform teams ensure that all application
teams adhere to consistent security practices and standards in their
pipelines. This approach eliminates the need for each application team to write and maintain similar pipeline code, allowing them to leverage the shared library instead.
New projects can quickly set up their CI/CD pipelines by importing the shared library, reducing the time to get started.
These
libraries are also designed to be extensible, so if the standard
libraries don’t fully meet the specific needs of a project, they can be
easily customized.
Platform teams usually provide comprehensive documentation and support for the shared library, making it easier for application teams to use it effectively.
Also,
it’s also common for teams to contribute to these shared libraries,
enabling collaboration and ensuring that the organization maintains
efficient and streamlined pipeline libraries over time.
In the next lesson we will get our hands-dirty by creating and implementing a shared library.
Create Jenkins Shared Library
In the last lesson, we learned about the concepts of Jenkins Shared
Library, and in this lesson, you will learn how to create and integrate a
basic shared library in Jenkins and use it in a sample pipeline
We will look into the following four things to get your hands dirty with the shared library.
Creating a Shared Library Structure
Creating Custom Shared Library Code
Configuring Jenkins to Use the Shared Library
Using the Shared Library in a Pipeline Stage.
Executing a Declarative Pipeline with a Shared Library.
Let's look at each one in detail.
💡
Note: In this lesson, we will concentrate only on the vars folder to create your first shared library. The advanced shared library lesson will cover src and resources.
Create a Shared Library Structure
Jenkins
shared library has the following structure. You can get the basic
structure and code used in this article from Github -> jenkins-shared-library
All
the files under the vars directory are global functions and variables.
The file name is the function name. We will be using the filename in our
declarative pipeline.
Create Custom Shared Library Code
In this section, we will create the shared library code for Git Checkout functionality.
Generating Pipeline Syntax Using Snippet Generator:
You
can create code snippets that can be used in the shared library
function using the Pipeline Syntax Generator available in Jenkins. This
will make our lives easier when creating custom DSL libraries. All the
supported pipeline functionality can be generated from the snippet
generator.
You can access the syntax generator from your Jenkins on /pipeline-syntax/ path. For example,
http://<Node-IP>:30000/pipeline-syntax/
Here is the screenshot, which shows creating a git checkout pipeline snippet using the pipeline syntax generator.
After giving every detail, click on the Generate Pipeline Script button to generate pipeline syntax
def call(Map stageParams)
- A simple call function that accepts a Map as an argument. From the
pipeline stage, we will pass multiple arguments, which will be passed as
a map to the shared library.
stageParams.branch - it's the branch parameter that comes from the pipeline stage, and we use stageParams to access that variable in the shared library.
Commit the changes and push it to your repository.
Configuring Jenkins to Use the Shared Library
Now that we have a basic git checkout library ready, let's add it to Jenkins configurations.
Step 1: Go to Manage Jenkins --> System
Step 2: Find the Global Trusted Pipeline Libraries section and add your repo details and configurations as shown below.
Using the Shared Library in a Pipeline Stage
We always call the library using the filename under vars. In this case, gitCheckout is the filename created under vars.
This is how we call the gitCheckout library from the pipeline or Jenkinsfile
Create a new pipeline
and run the above pipeline code in it, once the build is finished, you
can get an overview of your pipeline build as shown below
Like
gitCheckout, you can create all your pipeline steps a shared library
and you don’t have to repeat your common functionalities in all your
pipelines.
Introduction to Jenkins Multibranch Pipeline
If you are looking for a well-automated Pull Request-based or branch-based Jenkins Continuous Integration & Delivery (CI/CD)
pipeline, this lesson will help you get the overall picture of how to
achieve it using the Jenkins multibranch pipeline.
Jenkins’s
multi-branch pipeline is one of the best ways to design CI/CD workflows
as it is entirely a git-based (source control) pipeline as code.
This lesson will discuss all the key concepts involved in a Jenkins multi-branch pipeline setup.
What is a Multi-branch Pipeline?
A
multi-branch pipeline is a concept that automatically creates Jenkins
pipelines based on Git branches. It can automatically discover new
branches in the source control (Github) and automatically create a
pipeline for that branch. When the pipeline build starts, Jenkins uses
the Jenkinsfile in that branch for build stages.
SCM (Source Control) can be Github, Bitbucket, or a Gitlab repo.
You
can choose to exclude selected branches if you don’t want them to be in
the automated pipeline with Java regular expressions.
The
multi-branch pipeline supports PR-based branch discovery. This means
that branches get discovered automatically in the pipeline if someone
raises a PR (pull request) from a branch. If this configuration is
enabled, builds will be triggered only if a PR is raised. So, if you are
looking for a PR-based Jenkins build workflow, this is a great option.
You can add conditional logic to the Jenkinsfile to build jobs based on the branch requirement.
For
example, if you want the feature branch to run only unit testing and
sonar analysis, you can have a condition to skip the deployment stage
with a when condition, as shown below.
So whenever the code is merged develop branch, the pipeline will run the unit testing and sonar analysis stages skipping the deployment stage.
Also,
multi-branch pipelines are not limited to the continuous delivery of
applications. You can use it to manage your infrastructure code as well.
One
such example is having a continuous delivery pipeline for Docker image
or a VM image patching, building, and upgrade process.
GitFlow-style branching strategy
We will walk you through a basic build and deployment workflow to understand how a multi-branch pipeline works.
Let’s say I want a Jenkins pipeline to build and deploy an application with the following GitFlow-style branching strategy.
Note:
The branching strategy explained here is for demonstration purposes. It
may vary depending on an organization's specific design and standards.
Development starts with a feature branch, where developers commit code to the feature branch.
Whenever
a developer raises a PR (Pull Request) from the feature branch to the
develop branch, a Jenkins pipeline should trigger to run unit tests and
static code analysis.
After successfully testing the code in the feature branch, the developer merges the PR to the develop branch.
Now, a pipeline should trigger from the develop branch and deploy the application to the dev environment.
When
the code is ready for release, developers raise a PR from the develop
branch to the main branch. This should trigger a build pipeline that
will run the unit test cases and code analysis, and create the release
artifact.
Once the PR is merged to main, the release artifact
should be deployed to pre-prod or staging environment, depending on the
requirements.
From the above conditions, you can see that
there is no manual trigger of Jenkins jobs, and whenever there is a pull
request for a branch, the pipeline needs to be triggered automatically
and run the required steps for that branch.
This workflow builds a
great feedback loop for engineers and avoids dependence on the DevOps
team to build and deploy in non-prod environments.
Developers can check the build status on GitHub and make decisions on what to do next.
This workflow can be achieved easily through a Jenkins multi-branch pipeline.
Note:
We are not considering automated deployment to production, as most
organizations require manual approvals before deploying to production
environments, which are typically handled through dedicated production
deployment pipelines.
Multi Branch Pipeline Workflow
Here is how the multi-branch pipeline works for the GitFlow-style branching strategy we discussed above.
When
a developer creates a PR (pull request) from a feature branch to
develop a branch, GitHub sends a webhook with the PR information to
Jenkins.
Jenkins receives the PR, finds the relevant multibranch
pipeline, and automatically creates a feature branch pipeline. It then
runs the jobs with the steps mentioned in the Jenkinsfile from the feature branch.
During checkout, the source and target branches in the PR merge.
The PR merge will be blocked on Github until Jenkins returns the build
status (Impleted using staus checks and branch rulesets).
Once the build finishes, Jenkins will update the status to GitHub PR. Now you will be able to merge the code.
This process continues for all the PRs.
The following image shows the high level multi-branch pipeline workflow.
Multi-Branch Pipeline Best Practices
Let’s have a look at some of the best practices for a multibranch pipeline.
1. Repo Branching – Have a Standard Structure
It
is essential to have a standard branching structure for your
repositories. Whether it is your application or infra code, having a
standard branch will reduce the inconsistent configurations across
different pipelines.
2. Shared Libraries – Reusable Pipeline Code
Make
use of shared libraries for all your multi-branch pipelines. Reusable
libraries make it easy to manage all the pipeline stages in a single
place.
Pull Request Vs Commit Triggers
Try
to use a PR-based pipeline rather than a commit-based pipeline. If a
code repo gets continuous commits, it might overwhelm Jenkins with many
builds.
Commit-based triggers are also supported in PR-based discovery. Here, the commit trigger happens only when the PR is still open.
Regular Pipeline Vs. Multibranch Pipeline
A regular pipeline job is meant to build a single branch from the SCM and deploy it to a single environment.
A
multibranch pipeline is meant for building multiple branches from a
repository and deploy to multiple environments if required.
A pipeline job supports both pipeline steps to be added in Jenkins configuration and form SCM.
Use pipeline jobs for AD-Hoc jobs, parameterized job executions, and debug pipelines as code.
Do not use a multibranch pipeline if you do not have a standard branching and CI/CD strategy.
Create GitHub App For Jenkins Status Checks
In CI/CD workflows, developers need
an efficient way to verify the build status without diving into the
pipeline details. This is where Jenkins status checks come into play.
By
integrating Jenkins status checks with GitHub PRs, developers can
easily view the build status directly within the pull request. They can
also access detailed stage information on GitHub and follow a direct
link to the Jenkins job log.
Here is an example of status checks.
This setup mirrors how PR-based builds are typically configured in real-world projects.
To enable this integration, a GitHub App must be created for Jenkins.
In this lesson, we’ll walk through a step-by-step guide on creating the GitHub App and the necessary credentials.
In the next lesson when we create Jenkins multi-branch pipelines, we can integrate status checks with GitHub Pull Requests.
Creating Github App
To create a GitHub App, select your GitHub profile and go to Settings as shown below.
Scroll down and select the Developer settings
Then click the New GitHub App button to create a new GitHub app
You need to fill out the following details in the configurations.
GitHub App name: This name has to be unique. For example, jenkins-techiescamp-app. Replace it with the required name.
Homepage URL: Enter your Jenkins URL.
Webhook URL: Jenkins URLs with the webhook path (http://64.227.177.136:30000/github-webhook/)
Under Repository permissions, choose the following permissions from the drop-down menu.
Administration: Read-only
Checks: Read & write
Commit statuses: Read & write
Contents: Read-only (to read the Jenkinsfile and the repository content during git fetch).
Metadata: Read-only
Pull requests: Read-only
Under Subscribe to events, select the following events:
Check run
Check suite
Pull request
Push
Repository
Now, click the Create Github app button.
After creating the app, you will see a notification to generate the private key as shown below.
Click on the generate the private key option and click Generate a private key button as given below.
It will download a private key.
Now, you need to convert the key to a format that can be used with Jenkins using the following command. Replace key-in-your-downloads-folder.pem with your downloaded private key.
We need to add the converted key to Jenkins credentials.
Install Github App
Now, on the app configuration page, you will see an option called Install app, as given below. Click that option to enable this app for all the repositories.
Click Install
You can choose all or individual repositories you need and then click Install.
Add Private Key to Jenkins Credentials
Now, we need to add the converted PEM key to the Jenkins credentials.
Go to Jenkins Home –> manage jenkins –> Credentials.
Under credentials, select the global option.
Then, choose the Add Credentials Option to add a new credential.
Now you need to choose the Kind as GitHub App.
Also we need the Github App ID. You can get it from the Github App configuration as shown below.
In the key field, add the converted-github-app.pem private file contents we converted before, and then click the Create button as shown below.
That’s pretty much it.
In
the next lesson we will look at how to use Github App integration with
multi-branch pipeline to enable status checks on Pull requests.
Multi Branch Pipeline Setup
In this lesson, you will learn about setting up a multi-branch pipleine on Jenkins.
Before
jumping into implementation, let’s look at multibranch pipeline Jenkins
example Jenkinsfile that can be used in the pipeline.
For the multibranch pipeline to function, the Jenkinsfile must be present in the SCM repository.
If
you're learning or testing, you can use the multibranch pipeline
Jenkinsfile provided below. This Jenkinsfile includes a checkout stage
and several dummy stages that echo messages.
Also, you can clone and use this Github repo, which has this Jenkinsfile
pipeline {
agent {
kubernetes {
yaml '''
apiVersion: v1
kind: Pod
spec:
containers:
- name: maven
image: maven:3.8.4-openjdk-17
command:
- cat
tty: true
'''
}
}
options {
buildDiscarder logRotator(
daysToKeepStr: '16',
numToKeepStr: '10'
)
}
stages {
stage('Cleanup Workspace') {
steps {
cleanWs()
sh """
echo "Cleaned Up Workspace For Project"
"""
}
}
stage('Code Checkout') {
steps {
checkout([
$class: 'GitSCM',
branches: [[name: '*/main']],
userRemoteConfigs: [[url: 'https://github.com/spring-projects/spring-petclinic.git']]
])
}
}
stage('Unit Testing') {
steps {
sh """
echo "Running Unit Tests"
"""
}
}
stage('Code Analysis') {
steps {
sh """
echo "Running Code Analysis"
"""
}
}
stage('Deploy Code To Dev & QA') {
when {
branch 'develop'
}
steps {
sh """
echo "Building Artifact for Dev Environment"
"""
sh """
echo "Deploying to Dev Environment"
"""
sh """
echo "Deploying to QA Environment"
"""
}
}
stage('Deploy Code to Staging and Pre-Prod') {
when {
branch 'main'
}
steps {
sh """
echo "Building Artifact for Staging and Pre-Prod Environments"
"""
sh """
echo "Deploying to Staging Environment"
"""
sh """
echo "Deploying to Pre-Prod Environment"
"""
}
}
}
}
Setup Jenkins Multi-branch Pipeline
This section will guide you through the step-by-step process of setting up a multibranch pipeline on Jenkins.
This
setup is based on GitHub and the latest Jenkins version, but you can
also use Bitbucket or GitLab as your SCM source for a multibranch
pipeline.
Create a Multibranch Pipeline on Jenkins
Step 1: From the Jenkins home page create a “new item”.
Step 2: Select the “Multibranch pipeline” from the option and click ok.
Step 3: Click “Add a Source” and select GitHub.
Step 4: Under the credentials field, select Jenkins and create a credential with your GitHub username and password.
Step 5: Select the credentials you created in previous lesson and provide your Github repo to validate the credentials as shown below.
If you are testing a multi-branch pipeline, you can clone and use the demo GitHub repo.
Step 6: Under
“Behaviours,” select the option that matches your requirements. You can
either choose to discover all the branches in the repo or only branches
with a Pull Request.
The pipeline can discover branches with a PR from a forked repo as well.
Choosing these options depends on your required workflow.
And if you want to trigger the pipeline build on commits, select the below appropriate option under the Discover Branches section below
There are additional behaviors you can choose from the “add” button. (Optional)
For
example, if you choose not to discover all the branches from the repo,
you can use a regular expression or wildcard method to find specific
branches from the repo, as shown below.
Here is an example of a regular expression and wildcard usage
In the configuration above, I have included all the branches.
Step 7: If
you choose to have a different name for Jenkinsfile, you can specify it
in the build configuration. You can provide the required name in the
“Script Path” option. Ensure the Jenkinsfile is present in the repo with
the same name you provide in the pipeline configuration.
Also, Enable “Discard old builds” to keep only required build logs as shown below.
If your Jenkinsfile is in the root directory of the GitHub repo, simply enter Jenkinsfile as the script path.
Step 8: Save all the job configurations. Jenkins scans the configured Github repo for all the branches with a PR raised.
The following image shows the job scanning the three branches.
Now that all configurations are completed, let's test the multibranch pipelines.
Test Multi-branch Pipeline
For
demo purposes, I have chosen the option “Exclude branches that are also
files as PRs" With this option, the build will get triggered for PRs
and PR merges.
To experiment with a multibranch pipeline, you can use this repo with a sample Jenkinsfile.
Update
some content in the readme file in the develop branch and raise a PR to
main. This will send a webhook to Jenkins, which will send back the
Jenkins job details, as shown below.
Now, go to your Jenkins multi-branch pipeline and click on Pull Requests.
You can see a new build has been triggered because of the PR.
If
the build fails, you can commit changes to the develop branch. As long
as the PR remains open, this will automatically trigger the PR pipeline.
In
the Jenkinsfile, I've included a condition to skip the deployment stage
for the develop and main branches if it's a PR build. You can verify
this in the Jenkins build stages, where the skipped deployment stages
will be clearly visible, as shown below.
After the build is successfully completed, merge the PR.
Once
you merge the PR, Jenkins will trigger the main branch pipeline and
execute all the deployment stages specified in the Jenkinsfile for the
main branch.
For example, in our pipeline, we skip the dev and QA
deployments if the branch is main. You can observe this in the stages
output.
Troubleshooting Multibranch Pipelines
In this section, we'll cover common issues you may encounter with multibranch pipelines and how to troubleshoot them.
Branch Discovery Issues
If
new branches created in the SCM aren't appearing in the Jenkins
pipeline, try running the "Scan Repository Now" option to rescan the
repository. Additionally, review the repository scan configurations
within the pipeline settings.
PR Webhooks Not Triggering Pipelines
If
a webhook isn't triggering the pipeline, check the webhook delivery
status in GitHub for the status code and any errors. Also, ensure the
Jenkins URL is correctly configured. You can inspect Jenkins logs under Manage Jenkins > System Logs > All Jenkins Logs
to see if Jenkins is receiving the webhook. The logs should indicate
why jobs aren't being triggered if the webhook is successfully received.
Commits Not Triggering the Pipeline
To
ensure that each commit triggers the branch pipeline, make sure to
select the "Discover All Branches" option in the branch discovery
configuration. This setting ensures that any commits to discoverable
branches or pull requests automatically trigger the pipeline.
Setup Jenkins Email Notification
In Jenkins, email notifications are
an essential feature that helps teams stay informed about the status of
their builds and pipelines.
Configuring email notifications
ensures that developers and stakeholders receive timely updates about
successes, failures, and other important events in the CI/CD pipeline.
This lesson will guide you through the steps to set up and configure email notifications in Jenkins.
Prerequisites
For this email notification setup, you need a valid SMTP server to send Emails.
Usually in organisations, the respective SMTP details can be obtained from the organizations network team.
You can also create you own SMTP server using services like AWS SES. You can follow this guide for SES setup.
Step 1: Create Credentials with SMTP Username and Password
To
securely manage your SMTP credentials, it's best to store them in
Jenkins' credentials store. Here’s how to create SMTP credentials:
Go to Manage Jenkins -> Credentials and create a global credential.
Select the kind as Username with password, enter your SMTP server's username and password, and create the credentials.
Step 2: Configure SMTP Server for Jenkins
To send emails, Jenkins needs to be configured with the SMTP server settings. Here's how to do it:
First, go to Manage Jenkins ->System
Then scroll down to the Jenkins Location section and enter your from address, which is your SMTP server's name.
You have to specify the from address in the following format
example@<smtp-server-name>
For example, my SMTP server's name is devopsprojects.dev, and I have given it as jenkins@devopsproject.dev.
Now, scroll further down to the Extended E-mail Notification section and specify your SMTP server address, port, select SSL and select the credentials you created in the previous step.
Ensure you have given your SMTP server's address and click the Save button to save the configurations.
Step 3: Test Email Notification on a Job
Once the SMTP server is configured, you can add email notifications to individual Jenkins jobs.
⚠️
In
this pipeline, we will be using emailNotification shared library we
created. If you haven’t configured the shared library yet, please refer
to Lesson 4.2 for instructions on how to set it up.
Given below is the example pipeline code for email notification.
