We have written earlier how Artificial Intelligence can increase the efficiency and speed of software product development. Now that AI in software development is gaining acceptance, let's look at how AI can play out in software testing- its potential as well as shortcomings.
After test automation, AI-based testing looks like the obvious next step. Here's how things have rolled out in the software testing space:
Let's take an overview of what AI can do in Software Testing.
'As organizations aim for continuous delivery and faster software development cycles, AI-led testing will become a more established part of quality assurance. When considering only software testing tasks, there are several tasks that quality Assurance engineers perform multiple times. Automating them can drive huge increases in productivity and efficiency.
In addition to the repetitive tasks, there are also several tasks that are similar in nature, which, if automated, will make the life of a software tester easier. And AI can help identify such fit cases for automation. For instance, the automated UI test cases that fail every time we make a change in a UI element's name can be fixed by changing the name of an element in the test automation tool.
Artificial Intelligence has several use cases in software testing, including test case execution, test planning, automation of workflows, and maintenance of test cases when there are changes in the code.
But what are the limitations?
Even though Artificial Intelligence holds strong promise for testing, it will be hard for mere technology to completely take over.
Artificial Intelligence can't (yet) function on its own without human interference. Until then, organizations need human specialists to create the AI and to oversee operational aspects that are automated with AI. In short manual testers will always be a part of the testing strategy to ensure bug-free software.
While there have been significant advancements in Artificial Intelligence, it does not beat the logic, intuitiveness, and empathy inherent in humans. AI will bring about more impactful change in the way it assists software testers to help them perform their tasks with more accuracy, precision, and efficiency. But for all tasks that need more creativity, intuitive decision making, and user-focused assessments, it may have to be human software testers who hold the fort. For a while at least!
Organizations can use AI-based testing tools to cover the basics of software testing, and easily uncover defects by auto-generating test cases and executing them for desktop or mobile. However, such an approach isn't feasible when you need to assess a complex software product with various functions and features to test. Experienced software QA engineers bring a wealth of insights to the table that goes beyond the data.
They can make the decisions that must be made even when data doesn't exist. When a new feature is being implemented, AI may struggle to find enough solid data to define the way forward. Experienced software testers may be better suited to such situations where they can make intuitive leaps based on nothing more than their judgment.
AI can seamlessly help with tasks that are repetitive in nature and have been done before. But, even if we leverage AI to its full potential, there are jobs within QA that demand human assistance.
Just like automation aims at reducing manual labor by addressing monotonous tasks, AI-led QA minimizes repetitive work with added intelligence by taking it up a notch up.
This means QA engineers should keep doing what they do best. However, it will help QA testers to familiarize themselves with technologies AI to advance their career when these tools become commonplace. The truth is that AI is making a stand, but we still need diligent, creative, and expert QA engineers on our product development teams.
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