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Artificial intelligence (AI) has come a long way since its experimental debut as a computer program designed to beat chess grandmasters. The first big achievement was IBM’s Deep Blue, which beat world chess champion Garry Kasparov. That incident was in a true sense an inflection point in AI technology.

AI is only as good as the data that’s fed into its engine. The AI approach is to build systems and apps that learn and improve themselves, which is known as machine learning. The efficiency of machine learning algorithms depends on the computing power of IT systems.

A new generation of apps that can speak, listen, sense, reason, think, and act are available to us on our mobile phones and desktops. With the advent of AI, there has been a complete paradigm shift in software development and software testing in terms of the quality of apps and the speed at which they are delivered to customers. From a software testing perspective, AI can be used to synthesize a huge amount of data to predict the right strategy and to prevent future failures in software delivery.

AI techniques are affecting all aspects of software testing. The use cases in the following table are all seeing improvement as a result of AI.

Use Case

AI Impact

Test strategy

Analyze customer and production data to understand important features and potential automation areas

Optimization of test cases

Propose a minimum set of test cases based on business criticality

Automation

Suggest complex and business-critical scenarios for automation

Early prediction of quality

Comb through the defect data set and recognize patterns of defects for better resolution

User interface (UI) testing

Employ self-learning image pattern recognition for UI automation

At Mindtree, we are utilizing AI-based software testing in our work with many large organizations in various industries to streamline and automate defect triaging, reducing manual effort by more than 50%. In addition, we are helping with the automation of business processes and transactions through AI algorithms to improve accuracy and outcomes.

Please reach out to me if you’re interested in learning more about our AI-based software testing approach and how it can help improve your business outcome.

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About the Author

Mudit Kumar
Head of Test Engineering Services – APAC

Mudit Kumar is a Solution Architect at Mindtree for large transformation deals and pursuits in Quality Engineering space. He is an engineering graduate from Indian Institute of Technology (IIT) and an MBA post-graduate from Symbiosis University. Having two decades of experience working with Fortune 500 clients across 20 countries, Mudit has been instrumental in driving many testing transformation deals spanning across multiple industries. Today with the rapid adoption of Agile/DevOps in digital world, Mudit assists Mindtree clients with testing strategies that are aligned to business objectives of time to market, cost, and quality. Several of his technical papers on regression testing, machine learning, and automation have been published in international forums.

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