What are the best practices for testing RPA bots?

# Clearly define the objectives of your RPA testing efforts. Understand what aspects of the bot's functionality and performance need to be tested, such as data accuracy, exception handling, and system integrations. # Begin by conducting unit testing on individual components or modules of the RPA bot. Test each function in isolation to ensure that it performs as expected before integrating it into the entire workflow. # Implement thorough data validation tests to ensure that the RPA bot processes data accurately. # Involve end-users in the testing process through UAT. Gather feedback from users to ensure that the RPA bot meets their expectations.

?? What are the best practices for testing bots? Hope that they won't break, plan that they will ... How? Identify all the ways the people can break your design and test for it. What are some examples of potential failure points that can be avoided? Links changing, files being relocated, files being renamed, cell references changing, uncoordinated maintenance of a server, database and or virtual machine, automation software version upgrade, unexpected file type, unexpected file size, load times for applications and screens within them, differences in browser versions, operating system versions, desktop resolutions etc. And I'm just getting started ... :) When you identify points of failure early ... you win! ?? Like to support this

->Clearly define the scope of testing, including in-scope and out-of-scope elements. ->Determine the testing levels (unit, integration, system, etc.) and types (functional, non-functional) ->Document detailed both Postive & Negative test cases that include preconditions, test steps, expected results and post-conditions ->Ensure that test cases are clear, unambiguous, and traceable back to requirements ->Identify and create necessary test data to execute the test cases. Also, Consider different scenarios and edge cases to achieve comprehensive test coverage ->Conduct reviews of the test cases with relevant stakeholders

Always thinks the bot will fail, that will make the developer catch the exceptions right at the development stage itself. Testing the bot after development and changing the code based on the testing is one thing and catching the exceptions during the development is another and it will also save a lot of time. Also prepare the various cases of testing ,what happens if this goes wrong and how bot will handle that. And test it repeatedly until there are no more test cases available

1. Build test cases, map it with user stories and acceptence criteria. 2. Adapt Continuous development/testing/integration 3. Maintian Defect log- ensure root cause anlayisis is done for all defects and tracked until closure 4. Ensure Defect log is in the Knowledge base for new developers to proactively prevent errors 5. Adapt Automated testing using Uipath Test suite. This is key to success of Regression testing 6. Make Test data and test steps pre requisite to development 7. Ensure Solution Architect reviews and signs off code with coding standards- configurable, modular,scalable 8. Keep 4S principle - Scalability, Speed, Stability, Secure 9. Inspite of all preventions, in LIVE, hyper care is important

Most important key is to establish a testing environment that replicates the production environment which helps to systematically validate that a software application functions as intended, meets requirements, and is free from defects/Errors. Test both Postive & Negative cases include preconditions, test steps, expected results and post-conditions in test environment which should be approved stockholder before moving the code to production. Testing in an environment that mirrors the production setup helps uncover potential problems before the software is released to users.

- Use a test environment that is a replica of the production environment, where the bot can be tested without affecting the real data and processes. - Test the bot’s functionality, performance, reliability, compatibility, and integration in the test environment, and identify and fix any errors or issues. - Use realistic and representative data, but avoid using sensitive or confidential data. Use data protection techniques such as masking, anonymization, or encryption. - Document and track the test cases, results, and issues, and use tools such as UiPath Test Manager or UiPath Orchestrator to manage and monitor the testing activities and outcomes.

The best setup for RPA testing infrastructure is to have three to four lower environments, each of which should undergo regular cloning of production data. This practice ensures that we are in sync with the latest trends and scenarios in the production environment. It is helpful in simulating various scenarios without impacting the live environment, such as downtime or critical data issues. Once all test cases are tested and approved in these non-production environments by appropriate stakeholders, only then should they be promoted to production.

Use translation tables & avoid hard-coding values. Doing this allows non-technical folk to be able to manipulate pieces of the automation on the fly.

Ensure the quality and reliability of application/webpage at different level in RPA, ->Unit Testing - Verify that each unit of code functions as intended once it have been developed ->Integration Testing - Identify issues that arise when units are integrated into larger subsystems ->System Testing - Evaluate the system's compliance with specified requirements and its overall functionality ->Acceptance Testing - Validate if the system meets business requirements & ensure that the system is ready for release and meets user expectations ->Regression Testing - Evaluate the system's resistance to unauthorized access and potential vulnerabilities. Also, Identify and address security risks to protect sensitive information

In an Agile-driven environment, testing should be an integral step in every phase of the RPA lifecycle: During the development phase, automated unit testing should be conducted with 100% code coverage, notifying of any breaking modules whenever there are new commits. In the testing phase, system integration testing (SIT) takes place - the core testing team runs through all test cases in a manual/automated/hybrid way, ensuring that the same can be promoted for business users' testing. User acceptance testing is carried out as a distinct step - the real process owners or the concerned business team test it end-to-end. Finally, pilot testing in the pre-prod/prod environment is performed to verify results manually for the live environment.

A Robust testing framework #Work with operations team to define the UAT scenarios. #Work with technology team to define the as is stage of technology infrastructure bed. #Update leaders on UAT results.

Advocating for 'Hyper Testing,' I share an implementation experience using UiPath's test suite to deploy testing bots alongside real production bots, running them regularly in pre-production environments. This proactive approach addresses new changes or characteristics affecting automation before reaching production, maximizing testing/non-prod license utilization. Furthermore, Hyper Testing significantly reduces trade-off time, allowing rapid implementation of features without disrupting functionality. This results in accelerated release cycles, improved software quality, and reduced costs.

No RPA solutions is 100% error free for all the time, there can be time where there is an update on the system or application which we are using and we have to make changes and that is where to monitor the bot and identify where exactly the error(this has to be implemented during development) occurs in the file and change it and update it. There will be a development team and production support team and this is where this team comes into play and do a significant role in the business side of things.

Testing shouldn't be considered a task that occurs during a finite period in the project, but rather a continuous lifecycle. Automating continuous testing of automations in UAT environments where new features can be introduced prior to production environments is critical to get ahead of potential issues.

RPA bots are as smart as we are ! So Errors are subject to happen , Testing the robots is essential and it enhances the outcome and purpose of bots introduction to finish that certain task ! 1. Understand the process better , the type of input , process times , expected output , persona of the team who injects the input 2. Create test data and test cases , discuss the test scenario with the SME to align on same page 3. The right environment , it must be validation stage or non production stage but the versions of software should match to prod 4. Do perform the test cases considering the factors such as timing , volume of data , application load 5. don’t ignore even warnings, document all the errors discuss with them and document

Just like any other piece of software, bots require different levels of testing that help detect deviations from the expected behaviour. These are well identified in previous comments. One point that seems to be missing is around stress and load testing: bots are meant to perform under certain conditions; over time such conditions can vary, and in the worst case scenario, an increase in load might either make the bot unstable, or cause it to take longer to complete that initially estimated. This can be mitigated (or even avoided) through different tools. Testing the edges of the system involves as well the platform technology, hence this type of testing is very important, but at the same time quite tricky.

Testing rpa bots involves following the below practices: 1. Develop comprehensive test cases. 2. Conduct unit testing for individual components to find error in workflow 3. Ensure seamless interaction through integration testing. 4. Deploy bot in UAT env and run tests with simulated data. 5. Include negative testing for unexpected scenarios and check how bot handles it 6. Perform regression testing after a new change or functionality is implemented 7. Validate input and output data for accuracy. 8. Test scalability under varying workload to check performance of bot efficiency 9. Foster collaboration among developers, testers, and end-users for diverse insights and document results of all test cases