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6 Ways AI is Shaping The Future of Software Testing

April 27, 2018

Artificial Intelligence(AI) and Machine Learning(ML) are two of the hottest buzzwords that have now become the new normal in technology. Advances in AI and ML allow industries and Enterprise IT to make intelligent decisions and radically transform processes. As software testing shifts gears from manual to automation to embrace the speed for DevOps and digital transformation, AI has emerged to be the key lever for this change.

Whether manual or automated, software testing can benefit immensely from using AI, BOTs and the intelligence derived data and analytics. What are some ways in which AI is changing this dynamic?

Optimizing your test suite

One of the key problems facing software development and testing is over-engineering that results in a considerable loss of time, effort and resources. As businesses release faster and use automation to move forth, they often struggle with large amounts of backlog. AI BOTS can help clear the backlog and enable focusing on testing right. BOTs can identify similar/redundant and unique test cases, thereby removing the duplicates and enhancing traceability.

Instant feedback

One of the main benefits of intelligent test result analysis is the instant feedback and enhanced response time. The smarter analytics identifies hotspots easily and executes test cases automatically. An obvious use for AI is to take over the highly repetitive tasks that call for repeated inputs and report the outputs. AI can mimic these input-based scenarios very easily be it user actions for mobile app testing or more complex combinations. Based on historical information.

Predictive and Prescriptive Analytics

BOTS thrive on data and luckily most businesses have vast amounts of production and test data generated by automation suites. AI and ML can be used to analyze test results, identify defects and predict the quality. It uses this data to predict the key parameters of processes and prescribe the best course of actions. An intelligent approach to identify usage and failure trends to spot the most critical and less obvious faults.

Defect analysis

An intelligent approach to identify defects early and provide suggestions to avoid these defects and speed up cycle time. AI can accurately detect usage and failure trends to spot the most critical and less obvious faults. This allows teams to prioritize regression test cases based on risks identified.

Chatbot testing

Chatbots are not limited to eCommerce live chats. These conversational agents are now used for a variety of apps from messaging services, help desk and personal assistance. With the Internet of Things becoming more commonplace, the application of chatbots is increasing. However, the accuracy of the chatbot application relies heavily on the quality of the app and that’s where AI and ML come in. New tech like QMetry BOT Tester make chatbot testing easier by using GUI-based and headless automation testing techniques. Using QMetry BOT Tester you can now automate complex bot testing process, validation of user intent derived by chatbots etc for various chatbot types like Facebook Messenger, Skype, and Slack.

Tools of the trade

Enterprises can no longer ignore the innovation potential of AI and ML for the software development ecosystem. As some of the biggest companies are embracing these trends to take better decisions, improve response times, carry out repeated tasks and reduce errors, we will need tools that enable this change. To a certain degree, this will also mean reskilling of resources and a revamp of the testing cycle in the long run.

AI has significant impact on the quality function with benefits ranging from better quality to speed to market, traceability, optimization, better coverage and remarkable savings on the overall cost of development. If you want to understand how best you can utilize the power of AI to optimize your software testing efforts, contact QMetry’s experts today.

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Deepak Parmar
Deepak Parmar
Head – Marketing & Channel Management
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