Will AI make Software Testing irrelevant?

Our smart Virtual Assistants Alexa, Siri, and Google Home have already proven to all of us how wondrous and helpful the world of Artificial Intelligence (AI) can be. You must have come across numerous debates, discussions, and conferences around the globe that have woven around what AI can do and cannot do for the beings on the planet. Considering that the technology is so descriptive and expansive, a lot can be explored and discovered about its benefits as well as hazards.

When it comes to Software Testing, will the AI story kill the climax estimated by QA or the story will take a different twist?

Currently, Machine Learning (ML) and AI seem to be the most interesting zones in the QA process. Venture Capitalists, innovators, start-ups, QA practitioners, and every big and small enterprises are interested in how AI will contribute to their application development process. Software Testing and QA being an integral aspect, experts are focussing their energies on leveraging AI to automate and accelerate application testing.

This raises the concern, if AI and ML can get so efficient, can it kill the relevance of Software Testing and QA in the near future?

Diego Lo Giudice, VP and Principal Analyst at Forrester in one of his articles mentions, “AI will most likely help you be more productive and creative as a developer, tester, or dev team rather than making you redundant. Don’t be afraid. Take advantage of this opportunity and you’ll get an immediate return: It will give you more time to be more creative and to deliver more innovation — which will help you save your job in the long term!”

From the likes of these, AI comes across to be an enabler to the application development process, rather than a challenger. Today the software development and testing industry is considering AI for various amends and benefits. The reason being, it might have a significant impact on the way the application turns out. Practically, you can achieve a lot with AI that goes beyond the immediate benefits of automation.

Ensuring your software testing process is measurable and traceable is absolutely critical in the current scenario. A gaming application needs upgrades and testing to confirm its performance and functionality. When AI-led tools are applied, the testing activity can be measured by testers objectively and at ease across locations. It also give them the opportunity to trace back and forth through the tool to confirm and reconfirm the ‘gaming’ act. Efficiency might be the key to perfection, but traceability and measurability can be a massive contributor.

Taking this to the next level, Software Testing and QA has been constantly evolving through the years. The best way to take the notch higher is by making it a self-learning mechanism rather than a tool-infused process. Let’s see, when a banking application is being tested with an AI-led testing tool, each feature can communicate, and get cues from each other to become better and much more responsive. In a way, AI can help put testing on a ‘self-learning’ mode, where it encourages unsupervised learning for the application. This can be a revolutionary moment in the software development and testing cycle.

Can we expect an AI-enabled Testing environment soon?

An immediate response to this question would be, ‘How complicated can your application get in the near future?’ Test Automation evolved through the application development process to ensure that testing remains foolproof and indispensable. Building an AI-enabled environment across software testing ensures that testing continues to grow with relevance and gets absolutely business-critical.

AI is expected to get more and more pertinent for application development to ensure greater efficiency, identify defects with ease, and further accelerate the software development and testing cycle.

Test Automation being the rule of the game, AI can help to take it to the next level and make it much smarter by effectively applying data from relevant sources. In the case of applications that need visual verification and bots to operate, AI steps in to help you confirm the application’s performance and functionalities at various stages.

As application developers or enterprises with futuristic outlook, you can see AI contributing in automatic code reviews, improving security checks and vulnerability assessments, generating test cases, and even engaging big data. Additionally, AI and ML can help deliver real-time feedback on the application’s performance.

While the benefits of AI and ML could be overwhelming, the practical application testing process rests with the testing and quality assurance teams. Test Automation without a strategy and intellect can create chaos, similarly, applying AI and ML driven tools without a thought process can cause serious damage and delay to your testing activity.

The idea is to engage the AI-led tools to automate the test cases and run the testing process in the back-end. This will enable QA engineers to focus on high-ranking aspects such as security and risk management, enhancing scalability of the application, documentation, process assurance, regulatory compliance, or tracking key metrics for the application to keep improving and delivering as expected.

QA engineers and enterprises need to harness the power of AI to deliver better and faster application performance and experience. For instance, AI-driven tools might be helpful to identify issues or defects, but they can further help testers to identify a pattern and cut down the impact of digital-environment risks on the application. Merging human intellect with AI capabilities will help businesses to deliver desired consumer experience and cut down redundancy in testing.

In this era of daily deployments and DevOps transformation, organizations need to automate the test requirement traceability and versioning to accelerate the QA cycle and reduce overheads in test management and provide superior quality governance. Hence, it is critical to leverage AI platforms to test the quality of your enterprise applications.

Check out what Cigniti’s Intelligent Test Case Management Strategy has to offer for businesses to deal with their application testing and development challenges.