Cloud computing is the process of running workloads in the clouds – IT environments that abstract, aggregate, and share scalable resources across the network. Neither cloud computing nor clouds are technologies in and of themselves. And this application of automation has not bypassed RPA.
Many companies see and understand the need to implement robotic process automation (RPA). And this is logical since it will allow them to increase the efficiency of their activities, regardless of their global nature, the number of employees, or other determining factors. And if there are fewer questions regarding the development of a process based on RPA technology because often development environments for such processes have visual programming tools, which helps to get used to new technology, then when implementing industrial projects of the corresponding technology, the question arises, how will it be managed? Are there any standardized approaches for implementing an RPA project and a whole pool of organizational issues that are no less important than writing the project code?
RPA is increasingly being used to automate processes while saving time and money. However, in terms of simplifying the deployment process and delivering the written process, the Orchestrator system cannot provide a suitable solution. As a result, numerous RPA development companies use common techniques to automate the delivery process.
The CI / CD “conveyor” is an automation of the delivery and integration process of any software for a project. For it to work correctly, the pipeline must be developed before the coding process begins, allowing it to work when coding, testing, and directly injecting it.
Continuous Integration is a software development practice used by developers to make it easier to develop and test code by automating related tasks. Using it in RPA, developers continuously integrate code changes into a central repository, and tests are run on a separate server.
Continuous delivery is a practice that allows easy packaging and continuous deployment of code. It allows you to customize and package software, such as building an RPA bot and deploy it continuously at a lower cost.
- Faster delivery : shorter turnaround times, resulting in faster time to market
- Support : Problem detection during the build phase of the bot is much faster, allowing for faster problem resolution and error-free bot deployment.
- Improvement : End-user involvement in continuous development results in improved usability. New third-party feedback requirements may be met on a daily basis.
- Updates : Users receive updates on time because rolling releases using CDs is easier and less time-consuming. Release cycles, or sprints, in short, are targeted and tested for bugs before moving on to the next sprint.
- Monitoring : The progress of the development process can be communicated to the user, allowing real-time monitoring and elimination of pending feedback.
- Releases : Software deployments are painless and low-risk because the code can be viewed and edited on demand.
A common CI / CD implementation system for RPA projects using Azure DevOps Pipelines, GIT is a repository (due to its greater acceptance in client security services) and Docker containerization.
For an example of the flow of a standard CI / CD implementation for RPA projects, the following sequence can be given:
- The code written in UiPath Studio is committed to the Git repository. UiPath development studio – allows you to natively customize the integration of the commit process into the studio interface, as you could see in the solution from Microsoft – Visual Studio.
- A push commit triggers the pipeline in the Azure system, which in turn initiates the CI process.
- In a Docker container, the process is launched on a dedicated server for testing the process. Using the built-in code testing capabilities in UiPath, we have the ability to compose unit tests for each part of the process and make an assessment of the correctness of the process to execute them.
- Writing the logic of interaction with Pipelines – this container, upon successful completion of the tests, can be delivered to the production server, and merge with the local storage will be performed to update the latest version of the code.