There’s a paradox inherent in the way most enterprises approach digital transformation.
Few would argue that digital transformation isn’t an imperative. And while studies confirm that it is certainly a C-suite priority, a real disconnect seems to have arisen in many enterprises. They talk a good game about their digital initiatives, but dig a little deeper and it turns out that only around 15 percent are automating common test activities. Yet, there is little doubt that test automation is essential to achieving lift-off with digital transformation. Unfortunately, enterprises are finding that a huge backlog of test cases is getting in the way of accelerating release times and getting initiatives to market faster.
The Changing Role of Testing
Effective test practices are rightly considered vital to product quality. For that reason, in the last few years we have seen increased adoption of Agile practices and DevOps. More recently, Quality Engineering is changing how companies view the process of achieving product quality. It has also impacted the way we regard the traditional SDLC phases such as development and testing. These roles and practices have changed, but the need for testing has never been more pressing. Testing needs to cover a lot more ground now. For example, applications interact through a variety of different means, and require the ability to sync seamlessly with data systems. Not to mention the proliferation in sensors throughout the IoT, and voice and chatbots that all have additional testing requirements.
Help is on the horizon. Using artificial intelligence (AI) in automated testing is a relatively new, but significant development. It is a really useful tool enterprises should consider when looking to increase the degree of automation in their testing.
Artificial intelligence and machine learning (ML) concepts, together with testing best practices, can be leveraged to optimize test cases and take advantage of test data. Harnessing AI bots to help with the backlog has some very attractive benefits. It helps enterprises deliver on their strategic business goals faster: shorter time-to-market, improved customer experience and greater efficiency.
AI-enabled automation, or what we call Intelligent Automation, delivers considerable technical benefits too. With intelligent automation, bots are deployed to help enterprises focus on testing right, rather than testing more. One of the ways we achieve this is by improving test case quality. Another is to remove cases of duplication and to improve test traceability. Teams find themselves in a better position to shift left and incorporate Quality Engineering or CI/CD principles into their SDLC. It also means that software teams are better positioned to incorporate much more complex forms of testing, such as for the IoT or voice and chat bots.
How can bots help?
At Infostretch, we have three main ways bots can help with automating test case backlogs:
1. Test optimization: Bots like the Infostretch TOBOT can improve test quality and efficiency by weeding out duplicate tests and improving the reusability of tests.
2. Analyze + predict + optimize: AI and ML concepts can be harnessed to analyze test results, spot defects and predict quality. A tool like Infostretch’s QMetry Wisdom can then prescribe actions to optimize test outcomes and learn from the steps taken by test and development teams to effectively prioritize next steps and even outline the scope of testing for the next iteration.
3. Voice and chat bots: Introducing voice and chatbots is a great way to optimize customer service interactions, increase cross-selling or up-selling, and integrate seamlessly with other channels to create a holistic view of the customer. The only issue is that the complexity of integrating them into a product or service can deter many enterprises. Infostretch’s QMetry BOT Tester is a test automation framework for chatbots and voicebots that cuts that complexity. It automates complex bot testing processes for leading chatbot brands, speeds up testing of chatbot interaction flows and prevents manual testing errors.
Integrating Intelligent Automation
AI bots can be a valuable weapon in your testing arsenal. Infostretch has developed proven bot-testing blueprints that ensure that whatever your enterprise’s testing challenge, we address the underlying business need, deploy the right approach and get digital initiatives off the ground faster. To learn more about how to advance testing effectiveness, take our QE Maturity Model Assessment or get in touch directly.
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