Artificial Intelligence has crossed its nascent stage and is gaining rapid momentum. Organizations are looking into investing in AI for immediate intelligent insights that will result in long-term cost savings. Gartner predicts that augmenting AI will create $2.9 trillion of business value in 2021 alone.
There has never been a better need for Quality Assurance than now. All kinds of applications are constantly being released and are continuously updated to provide more functionalities and better experiences to the end user. QA teams are expected to be agile, multi-skilled, be involved from the beginning of the development, and deliver high quality.
Leveraging AI and applying it to QA engagements is a new wave to help QA engagements become predictive and analytics based.
When it comes to combining AI and QA, this can be done in two ways: using QA while building AI & using AI to build QA.
Using QA while building AI
84% of CEOs think that AI-based decisions can be trusted only if they are explainable. More often than not, AI systems are not trusted easily.
For an artificial intelligence system to be deployed, it has to be proven accurate, secure and reliable. Building such an AI model is no easy task. It involves lots of data training, and rigorous testing and tuning. This process requires highly skilled domain-aligned QA to make the training success.
Using AI to empower QA
Quality assurance automation has already been a key tool for many software building enterprises. QA automation software is used to make the product market ready at lesser costs in shorter intervals.
Bringing AI with its superior predictive capabilities into the automated testing arena elevates the testing approaches and takes quality assurance to the next level.
Some QA use cases where AI can empower QA teams
Even the best QA teams could possibly overlook some defects during the QA process. This issue can be resolved when using AI to help the QA teams. QA teams can leverage the test cases generated by AI based on pre-conditions and past coverage, and reduce software bugs.
Predictive Analytics Based on Pattern Recognition
Rather than testing after the code has been written, AI can predict potential defects that could occur in a particular module. Based on previous defect history, it can predict which team or person is likely to produce more bugs in the code.
Root Cause Analysis
When large or multiple teams are involved in building a software, although ownerships may be assigned to each team/individual, it is difficult to point the origin point of a defect. AI can be used in this case to point to the root cause of the issue, and point to the team or member who can fix the issue, thereby saving time for everyone involved.
Faster Release Cycles
AI testing can generate results very quickly, even for large codebases, thereby reducing the product release times. The bot can run unsupervised during nights and developers can begin work the next day based on the results of the AI testing.
Test Data Generation
Test data generation takes up a lot of time during software development cycles. This test data is used to test the software and ensure that all the components are working as expected. AI can be used to generate test data that is very close to actual production data that will be fed when the software goes live.
In a highly competitive digital landscape, it’s important to stay relevant and deliver unmatched user experiences, regardless of whether you’re a B2C or a B2B business. AI driven Quality assurance is an integral part of running businesses of the future and can help mature your QA teams from being a reactive QA organization to a proactive & cognitive QA organization.
With TVS Next as your AI based QA partner, you can deliver products and services that stand out among the rest and with business clarity. To know more: