Subscribe To Newsletter Digital Future Train your QA Engineers in RPA for Better Business This blog has been written with inputs from Swati Sucharita, Senior Project Manager Robotic process automation (RPA) is a fascinating technology that can turn heads in the automation era. RPA mimics the actions of business process users and automates simple to complex rule based processes. The automated processes can then be run using software robots (bots). RPA Goes Beyond Structured Data and Repetitive Tasks Enterprises that deploy RPA using traditional approaches to automation are limited to tasks that use structured data. However, tasks that have non-repetitive processes and use unstructured data would require cognitive abilities to achieve automation. With advances in Natural Language Processing (NLP) and machine learning, RPA tools are now able to simulate thought processes and adapt to evolving business requirements by recognizing, capturing and interpreting user actions through predictive analytics. Most RPA use cases are implemented in customer service processing, data migration & management, IT infrastructure support and back office administration related tasks in domains such healthcare, insurance, and manufacturing. One of the use cases for QA in insurance is claims processing. In most cases, processing insurance claims is done manually which is time consuming process and error prone. By turning to RPA these tasks require lesser resources, are able to eliminate errors and thereby reducing delays in processing claims. An important area for the adoption of RPA is QA Automation testing where there is tremendous scope to improve the various types of testing such as functional, regression and performance testing. It is evident that in the near future software testing will be more tool-oriented and a majority of automated testing can be done using cognitive RPA not just for repetitive tasks but also for complex processes by training the software to learn the processes over time and seamlessly adapting to the changing business rules. Cognitive RPA has the potential to dominate significant market share in automation testing. Some of the QA tasks that can be easily automated using RPA are login credential verification, data & UI validation, automated test data creation, test management process automation and automated issue/bug tracking Why you should consider RPA in automation testing Code-less: There is no need of coding or scripting Simplicity: It is easy to create a process through simple drag and drop actions Scalability & Accuracy: Multiple bots can be assigned work across multiple workstations – they are less prone to data entry errors Flexibility: The test process is independent of the platform on which the application has been deployed Cost saving and higher productivity: Increased savings due to lower utilization of manual workforce and higher productivity from bot deployment Data Migration and Change Management: Robots which work on legacy systems can easily be used to preserve the application’s data and integrity and it can also re-use the existing application logic, databases and validation without high re-structuring or maintenance costs. Test Integration: RPA has strong integration capabilities which can lead to more robust, quicker and effective automations. Cognitive Automation: RPA integrates structured and un-structured data using different AI features and extends the automation to more processes using cognitive capabilities for predictive analysis. RPA, a key differentiator in QA Testing, Data Security & Data Management Due to its non-evasive nature, performance, security, integration, data migration and data capturing capabilities, RPA is gaining a lot of prominence across different industries. In the area of security testing, RPA offers the capability to record every manual and automated action into audit logs. RPA can work with a wide variety of data formats, interfaces & systems, and can be customized to client specifications. Data inconsistencies can be caught in real-time as the RPA tool operates on existing GUIs. Further, test data can be generated more easily using RPA thus improving the overall quality of test automation. Screen scraping is another extremely useful ability for data extraction in scenarios where the application UI is accessed over virtual desktops. RPA also uses OCR which allows bots to distinguish characters under varying display conditions. Most companies have already implemented RPA in multiple sectors in order to transform customer experience through driving productivity by automating transactional tasks and increasing profitability. RPA adoption needs to go beyond repetitive tasks to include more complex processes. However, adopting RPA might initially seem like an uphill task, but with proper planning, documentation, implementation, and testing, one can fully leverage its potential to be a game changer. A well planned RPA adoption can beat implementation challenges by: Selecting the right processes to automate: RPA is best suited for tasks that are repetitive, rules-based, high volume, and do not require human judgement. Setting realistic expectations: RPA’s functionality, timelines, and expected results may vary widely from company to company. Hence it is required to take decisions on technology & processes based on one’s individual or company specific experience. Change management or maintenance: Regular scheduled maintenance or change management processes must be implemented and practiced. Infrastructure Issues: Every organization should have a centralized infrastructure team that that can expedite the implementation process. In conclusion, RPA in QA is poised to be the next big transformation to impact the software industry since bots can be configured in a matter of weeks to run automated testing. Apart from cost and productivity benefits, it also reduces the efforts on manual training by making intelligent use of resources and improving compliance thereby allowing humans to focus more on strategy, creativity and building connectivity.