Application Development

From QA to RPA: An unlikely origin story

Posted: September 08, 2017

Humble beginnings

Robotic process automation, or RPA, is poised to take the world by storm.

By 2025, McKinsey and Company estimates that RPA’s impact on the global economy could reach as high as $6.7 trillion. This is primarily based on the technology’s ability to match the output of 110 million to 140 million full-time equivalents (FTEs).

However, RPA’s status as a productivity powerhouse began with more basic forms of technology back in the 2000s. To fully understand how it bloomed into the process enabler that it is today, we need to go back to its roots. 

A prelude: What RPA is and what it does

But first, some context. 

Robotic process automation refers to the use of software robots that operate on top of existing IT infrastructure or systems (your APIs and existing software user interfaces) to execute specific business functions.

Unlike software in the traditional sense, these bots mimic the behavior of an end user, meaning that they interact with the front-end infrastructure that is already in place. This makes them API-agnostic, so naturally, their integration capabilities are unparallelled.

RPA’s core purpose is to automate repetitive functions such as manual data entry so that workers can execute tedious tasks with much greater speed and efficiency, and focus instead on the workflows and business goals that matter most to them.

The advent of RPA: Automated software testing

Far from being the new kid on the block, RPA is more of a sleeper, and its origins can be traced through recent history.

For example, bots have been posting on social media, sending out emails and conversing over web chat since the early 2000s. They have been used in call centers for more than a decade, specifically in the form of interactive voice response (IVR) for customer-service calls. A part of RPA’s lineage can also be traced back to screen-scraping tools that funnel data from a web form to an underlying application. 

However, the technology’s breakout moment happened in a somewhat unexpected venue: testing automation used by software quality assurance.

When agile software development came to the fore in the early 2000s, test engineers turned to QA automation to handle their more repetitive test cases so they could create continuous application development models. For instance, they sought to automate:

●      Unit tests: These vet the most basic building blocks of an application.

●      Regression tests: Must be executed with every change that is made to a piece of software.

Rather than repeatedly running these tests manually, automation developers taught a separate program to mimic the rules and steps for a test case, so that testing engineers could run them automatically. This methodology caught on quickly for its time-saving benefits, and thusly (and inadvertently) became a spiritual predecessor to RPA.

Similar to how test automation uses a separate program to test code, RPA uses bots to interact with an API based on step-by-step rules for completing a repetitive task within an application.

And that brings us full circle to where RPA is today.

Where RPA is and where it’s going

Demand for RPA grows at a rate of 20 to 30 percent every quarter, and it’s no mystery as to why. It empowers modern workers to automate tedious knowledge work through a non-technical user interface - click, drag and drop.

Business users can simply tell one of these trained software robots to do something, and it will do it. In fact, RPA already has viable use cases for all of the following:

●      Field form entry.

●      Automatic invoicing.

●      Accounting.

●      Auditing.

●      Migrating data between applications.

Last but not least, RPA can enable high-quality performance from your IT systems. It reduces the amount of end-to-end testing that would be required when building out custom APIs, integrations and ETL logic to dissolve information silos.

This is because RPA bots obviate that customization in the first place. They can be taught to extract raw data and move it to a target source from front-end to front-end, without any changes to underlying infrastructure. This shrinks what would otherwise be a much larger testing surface replete with custom APIs and integrations. Developers are then free to focus time and energy on enhancing the quality of existing solutions.

RPA hereby closes a loop – one that began with QA development, and now ends at the opportunity to add more value to your existing enterprise IT, and business processes, with less work.

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