# Infosys Whitepaper 
Title: RPA validation pitfalls and how to avoid them 
Author: Infosys Limited 
Format: PDF 1.6 

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 PERSPECTIVE RPA VALIDATION PITFALLS AND HOW  TO AVOID THEM MANOJ AGGARWAL, Delivery Manager, Infosys 

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 External Document © 2018 Infosys Limited Abstract While many organizations are adopting robotic process  automation (RPA) to increase operational efficiency,  the approach for testing and validating robots is still a  software-first strategy. The lack of the right testing and  validation strategy can result in under-performing robots  that are unable to meet the desired business and efficiency  outcomes. This paper outlines erroneous RPA testing  strategies and provides an effective approach to RPA  validation.<|endoftext|>External Document © 2018 Infosys Limited 

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 External Document © 2018 Infosys Limited Introduction To execute any business operation or workflow in large enterprises, services personnel or business teams usually work with multiple IT  applications. Robotic process automation (RPA) can automate these business workflows across multiple and disparate applications using  software robots that mimic the actions of human users as depicted below: The increased adoption and criticality of RPA for business process automation makes it essential to design and build RPA robots that exceed the  level of productivity and quality delivered by human users. However, many RPA robots tend to underperform due to poor design and improper  implementation in addition to workload scheduling decisions. Further, QA teams struggle to identify these issues during robot validation  primarily due to recurring test strategy and test execution challenges as described below: What to test How to test Where to test Focus on validating IT application functionality: Since business process  automation is always implemented on ap- plications used by the business, it is fair to  assume that these applications are tested  and work properly in production. However,  QA teams continue to invest significant  effort to validate application behavior  like querying the database to verify data  updates, checking error messages in case of  negative scenarios, etc.  Missing non-functional requirements  driven by the operating environment:  Sometimes, validation teams miss out on  capturing and validating operating envi- ronment requirements like business SLAs,  process execution window and dependent  activities.<|endoftext|>Validation of robots as software: One of  the most striking challenge is that validation  teams treat robotic process testing as software  validation. Their approach focuses on test  steps automation, validation of the Robot in- puts with database and re-verification of steps  performed by Robots. While some of these  validation steps are relevant, teams tend to  miss the key validations required for seamless  and predictable functioning of RPA robots.<|endoftext|>Automating RPA testing:  There are many  instances where efforts are made to auto- mate RPA testing using test automation tools  without much clarity on what needs to be  automated when RPA itself works in an auto- mated manner.<|endoftext|>Validation in incorrect environments:  Many QA teams begin validating RPA  robots in SIT and other lower environments  where the application baseline is usually  not in sync with the production environ- ment. This asynchrony results in robots fail- ing to perform in the production environ- ment during the first few runs.<|endoftext|>      

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 External Document © 2018 Infosys Limited A successful example of robot validation  A good example for RPA validation can be gleaned from the manufacturing industry that has successfully deployed physical robots over many  years with a very high level of quality and predictability. On comparing how physical robots are tested with how RPA robots should be tested,  the following key testing attributes emerge:  1. The robot’s ability to perform as per the instructions   2. The robot’s ability to perform tasks autonomously 3. The robot’s ability to perform at higher efficiency 4. The robot’s ability to handle exceptions gracefully A recommended approach for RPA validation   Combining the knowledge of software validation with the processes followed by the manufacturing industry for robot validation provides a  fitting approach for RPA validation that overcomes the testing challenges mentioned above: What to test How to test Where to test Validation scenarios: Since robots are  designed to mimic business workflows, com- panies can re-use/create test cases similar to  UAT test cases for RPA validation. The focus  should be on validating business flows and  business exceptions rather than application  functionality Non-functional requirements validation in  the operating environment: Teams should  capture non-functional requirements like  the ability to operate autonomously, SLA  delivery efficiency and expected volume to  be processed within preset timeframes Functional validation of robots: Validation  teams should set up the input case data for  all business cases within the determined  scope and then begin robot processing.  They should also validate the outcomes  and re-runs in cases of errors/unexpected  outcomes Exception scenarios: Teams should set  up the inputs case data with exception  scenarios and validate the robot’s ability  to identify and report these exceptions for  human intervention. They should also re-run  and validate these tests in case of errors/un- expected outcomes UAT or higher environments: QA teams  should perform validation of RPA only in UAT  or higher environments to prevent issues  arising from incorrect application versions or  environment set-up Stabilization in production: Teams should  train robots with simple cases and at low  volume during initial runs and gradually  increase complexity and volume to ensure  minimal impact in case of any processing  mismatch  

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 External Document © 2018 Infosys Limited External Document © 2018 Infosys Limited 

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 External Document © 2018 Infosys Limited Skill recommendations  RPA validation teams should have a deep  understanding of business processes  across regular and exception workflows to  ensure that robots are tested for all possible  business scenarios. Additionally, the teams  should be well aware about the scheduling  mechanism and controller team operating  model of the RPA robots so that they can  validate the operational efficiencies of RPA  robots before deployment into production.  Each RPA validation team should also  leverage a shared team of experts who  have in-depth knowledge of object  design and error handling mechanisms.  This is important to maintain focus on the  performance tuning of robots to meet the  operational efficiency requirements.<|endoftext|>As the RPA ecosystem continues to  evolve, one can expect more complex  robot implementations with optical  character recognition (OCR) and natural  language processing (NLP) inputs as well  as AI integrations. Moreover, robots will  increasingly compete for shared resources  like server time and application access to  complete the business processes. In such  a scenario, RPA validation teams should  continue acquiring in-depth knowledge  on evolving validation needs and improve  the strategies to support these complex  business requirements.  Exit criteria  Before a robot is deployed into production,  it is important to check that it has  been validated to exceed the business  benchmarks and is predictable. Here  are some key parameters that should  be considered as the exit criteria for  robots before they are deployed into live  production: • The adherence to business SLAs should  be higher than that of manual processing  SLAs • The number of business processing  exceptions should be equal to or less  than the number of manual processing  exceptions • The total number of cases processed  without human intervention should be  equal to or greater than 95% of the in- scope cases  • Robot availability should exceed 98%  Typically, robots meeting these criteria are  well-placed to take over business processing  responsibilities from human users and  are proven to deliver the desired business  benefits.<|endoftext|>

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 External Document © 2018 Infosys Limited Conclusion RPA has the potential to transform how  organizations execute business workflows  by enabling higher efficiency, faster  outcomes and almost error-free operations.  One of the key success drivers is the  right testing strategy for RPA validation.  Validating robots using existing software  testing models is ineffective because it  often results in non-performing or under- performing assets. To overcome this  challenge, organizations need a strategy  that tests the robot’s capacity to work  autonomously, handle exceptions well,  operate at higher efficiency, and follow  preset instructions. With the right validation  approach, skills and exit criteria, RPA can  help organizations meet the desired  business outcomes of error-free, predictable  and efficient business operations.<|endoftext|>External Document © 2018 Infosys Limited 

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 © 2018 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this