We recently talked about how Test Automation and Robotic Process Automation are not the same though they both are record and playback mechanisms. We also mentioned if automation meets with Artificial Intelligence it aids businesses to go beyond conventional performance trade-offs to achieve extraordinary levels of efficiency and quality. This article is to build on top of that notion and discuss how Intelligent Automation can optimize Quality Engineering practices, especially Application Testing practices.
In their 2018 Application Development predictions Gartner said,
By 2022, 40% of Application Development (AD) projects will use AI-enabled test set optimizers that build, maintain, run, and optimize test assets.
They were absolutely right, and the percentage might even increase by 2022 considering the unprecedented rush IT world is facing to innovate quick, develop fast and deliver faster. Every business that makes, sells, or serves through software is forced to improve its Quality and Speed. Unfortunately, many thought it is a choice - chose ‘Speed’ and assumed ‘Quality’ will follow. They have adopted Agile Methodologies, started practicing DevOps, employed automation wherever they can but could not get results they anticipated. As much as Automation and Artificial Intelligence can improve your team’s productivity and application performance, if they are infused into wrong processes, they can decelerate the initiatives.
Every other influencing entity has been talking about the importance of infusing Automation and AI into development and testing practices. But not many talk about the struggle of finding the right providers. If you are someone who handles Sourcing, Procurement or Vendor management in your firm, you are most likely to feel the heat already. Not to make you more nervous but for the sake of your software’s quality, it is important to ask yourself a few questions before you swim in too deep without the right apparatus.
What separates 'Test Automation' and 'Intelligent Automation in Testing'?
First of all, let me clarify that IA in test is not another coined term to re-package test automation solutions. IA does a lot more than record and play. Although, it is an umbrella term that covers a range of strategies and technologies from RPA to Deep Learning to Cognitive techniques.
When we automate Test case creation, frameworks, and execution to a certain extent; it sure does saves time, increases coverage, and reduces maintenance. But Intelligent Automation brings in a TDD or BDD based suite, enables analytics test digital frequently across channels, and prioritize execution based on the changes. Analytics in Test Automation is merely limited to pass or fail logs, but IA provides detailed data analytics such as RCA (Root Cause Analysis). When AI/ML is involved an engineer need not intervene manually for test maintenance each time there is a change in application.
Where does IA fit in your STLC?
Thanks to DevOps and modern Quality Engineering practices, Testing has slowly moved left in SDLC (The magical Shift-left). It is great for application’s health, cuts down repair and restoration costs in the long run but newer test environments and sudden change in team’s work arrangements obviously strain IT (at least at the beginning). That is where Automation came into the picture. Automating the test case creation, execution, data collection, and record maintenance saved a lot of time for the human testers so they can spend more time on exploratory testing and innovation. Intelligent Automation is nothing but an evolutionary approach where Automation is driven by huge amounts of (structured and unstructured) Data with AI and ML at its core. To bring in agility into application testing, identify potential use cases in testing phases, and employ bots that can automate the process using real-time as well as taught information.
Here, we marked the most practical use cases of Intelligent Automation in an average Software Testing Life Cycle and their benefits.
When you integrate IA framework in the right processes, they can help you*:
|Optimizing cost||Optimizing time||Optimizing quality|
(*All the statistics are results of Qentelli’s POCs, learnings from our Automation Projects and general observations from our practices and their results)
Are you evaluating your IA service provider against right criteria?
SPVM (Sourcing, Procurement, and Vendor Management) leaders’ responsibilities start with ensuring the quality and speed are not compromised with any new addition to the IT set-up. So, these are the elements you must not miss while shortlisting your IA vendors.
One might think the list would start with ‘Cost’, but always start with ‘Benefits’. Ask the vendor to present a (demonstrated) list of outcomes that can be achieved with their solution. Check if they can promise a year-to-year cost reduction in terms of operations. Ensure you are not getting a Python Container in the name of automation solution and you will have to build Artificial Intelligence on top of it yourself. If you chose to (you should) ask your vendor preference to run a POC for one of your use cases, judge it against ease of programming, control, and maintenance. Pick a low-code or no-code based on the in-house technical expertise and external assistance your vendor is ready to provide. Grade your vendors' list against the software/hardware requirements to support their solution, security compliance, screens scraping capabilities, cognitive capabilities, and alignment to your requirements discussed earlier. It is always wise to assess both benefits and risks before signing up. When you discuss cost, do check what is the initial set-up cost, vendor license fee and support & maintenance cost. Calculate the total cost of ownership before taking a call.
IA can be your fastest route to Continuous Testing
Digitally enabled businesses are adapting Agile methodologies and practices like DevOps to stay ahead in the race. Software Development teams across the globe are already using a bunch of tools to label themselves doing the infamous DevOps. For best or worst, there is an effort to break silos between the development teams and business teams. The new intelligent test automation tools that you bring in would work their best if their integration makes less to no changes in the existing set-up.
IA can be exploited to automate testing environment creation which can improve the number of test cycles and ensure rolling out top-notch quality application/code at the end of every single sprint, of course without impacting compliance and security standards.
We did successfully implement process automation across the client’s applications to transform their software testing life cycle. The client had a legacy monolith windows-based POS application in which testing is done manually and used to take 6-8 weeks for each cycle. After our multiple automation efforts, we managed to achieve:
Test cases per day
Test cases were automated
Test coverage improvement
Projects like these can reap more benefits using Intelligent Automation.
All we say is, Intelligent Automation for Testing comes with growth agenda AKA scaling. Most of the RPA solution providers already offer a range of IA tools. We agree most of them are fairly immature in terms of using data to create actionable insights. But the sooner you start having your hands on the technology, the easier it will be to cope with the competition. Every IA solution needs training, consistent learning to be able to grow as a self-healing system or make real-time decisions without human intervention.
Still have questions around Intelligent Automation for Testing and want to discuss with an expert about how to adapt it? Feel free to write to us. email@example.com