RPA Testing: What It Is, Importance, & Best Practices in 2024

The interest in RPA testing is expected to reach its highest level in the US. That is perhaps for good reason: According to EY, 30-50% of RPA projects fail annually, thus raising the importance of rigorous testing. 

RPA testing is crucial since it enables businesses to identify problems with the bot before they are integrated into the infrastructure, in addition to ensuring that the bot you have invested time and money developing is actually useful and functioning for you.  

In this article, we explain what RPA testing is and how it can be done. 

What is RPA testing?

Same as with other software tests, RPA testing is done to ensure the smooth and correct functionality of the automated workflow that has been developed.

What are the best practices of RPA testing?

1. Break down the processes  

You should be aware of the precise components you will be evaluating before you begin your testing journey. If the process is too complicated, try splitting it up into smaller steps. This will increase the likelihood of perfecting each stage of a protracted procedure before moving on to the next one.

2. Make sure the data is usable

Let’s assume that the business automates the creation of new employee contracts using RPA. Whether you have programmed the bot to do so or not, you must ensure that the employee data kept in the company’s database is useable, structured, and in a machine-readable format (e.g., CSV, RDF, etc.).

Using inaccurate, wrongly formatted, or unstructured data would lead to inaccurate results that would not reflect the actual legitimacy of the bot. 

3. Go over the scripts   

For the successful completion of each task, the script should be error-free, clear, concise, and relevant to the task. You could consult the Process Definition Document (PDD) which outlines the as-is manual processes that are being automated. Cross-checking each step in the PDD with its script will minimize the possibility of skipped or overlooked steps. 

4. Test the workflow 

Next, you should test the execution of each step to see whether the bot is able to provide you with the expected result. 

According to Wikipedia, testing a workflow roughly lies on the following grounds:

1. Test ID: The process you are testing should have an ID so you can order the succeeding and the preceding steps accurately.  
2. Test description: The process you are testing should have a brief description of what it actually is. 
3. Conditions: These are the hurdles the bot should clear before executing the task. The Given-When-Then (GWT) is a popular test case scenario:
   • Given: Given a new employee is added to the database and is not a duplicate,
   • When: When I “start” the contract automation solution
   • Then: Then the bot should extract the employee’s data and fill them into the contract draft. 
4. The steps: After assumptions and pre-conditions have been defined to make sure the prerequisites are met, you should let the bot carry out the steps taken from the PDD. 
5. Expected results: As a reference point, you should already know what the optimum outcome should be so you’d compare it with the bot’s performance – in our scenario, a new contract is created bearing the information of the new employee. 
6. Actual results vs. expected results: Once the workflow is executed, you should compare and contrast the actual result vs. the one you’d been expecting. 
7. Verdict: Now, you should know whether the bot has performed optimally or not based on the previous step. 

5. Implement/troubleshoot

If the bot has performed adequately and delivered what you were hoping for, you now need to implement it into the wider framework of the company’s infrastructure. Read our article on RPA implementation to learn more. 

But if the bot has failed to deliver, you need to go back to the drawing-room and address its shortcomings. These tips might help resolve the issues quicker: 

• Taking a screenshot of the error to specifically work around that and not waste time looking for errors where none exist, 
• Running the script again, but this time screen-recording the process so you could refer back to it without having to run it again,
• Going over the input data and the output file again to see whether there were any pre-existing issues with the data to have warranted a failed output,
• Keeping a detailed backup of the errors and the ultimate remedies as a template for the development team or the IT staff to refer back to, in case similar issues crept up in the future.

For more on RPA

To learn more about RPA, read:

• How do RPA Tools Differ From One Another?
• Workload Automation vs RPA: 3 differences you need to know
• Top 5 RPA Myths And The Reality Behind Them

And if you believe you would benefit from adopting an RPA solution for your enterprise, feel free to check out our data-driven list of RPA vendors.

And we will help you pick the best one tailored to your needs: