In this world of rapid technology advancements, it’s critical to outline that the contribution of artificial intelligence (AI) and machine learning (ML) is extending well beyond generating creative content or unique artwork. A significant area where AI has made its impact felt is the realm of software testing and quality assurance (QA).
What is Automation Testing?
Let’s refresh our understanding of what automated software testing entails.
In the process of product testing, engineers use various approaches to ensure rigorous software analysis leading to optimal outcomes. Testing specialists manually craft and execute tests to detect bugs. Concurrently, quality assurance (QA) professionals strive to enhance the efficiency and security level of the entire development process.
However, despite the handy nature of manual testing for evaluating app features, it’s time-consuming, taking up to 35% of a test cycle. This is precisely where the role of skilled automation engineers becomes vital. They develop automated tools and strategies thereby eliminating the need for creating and running multiple test cases, which considerably optimizes the testing process.
Impact of Automation on the Testing Process
Implementing a test automation suite presents several opportunities such as expanded test coverage, decreased human errors, reduced risk of test failures, reusability of test cases, substantial budget savings, more structured test results, simplified test creation process, and automated CI/CD.
Given its benefits, it’s unsurprising that most IT companies dedicate between 30 to 50% of their QA resources to automated testing infrastructure and staffing.
Understanding Artificial Intelligence
Artificial intelligence is a groundbreaking technology that attempts to emulate human capabilities. Currently, the global AI market is worth over $142.3 billion. However, for clarity, it’s essential to differentiate between AI, machine learning, and deep learning.
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Artificial Intelligence: It’s about instructing computers to simulate human intelligence by learning from algorithms and large data quantities.
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Machine Learning: It involves training computers to learn from data procured via algorithms.
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Deep Learning: This approach is based on algorithms with artificial neural networks similar to the human brain.
Why Use AI for Test Automation
There are compelling reasons to leverage AI in the software testing domain.
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Enhanced Accuracy: With AI and ML capabilities, automated testing can effectively identify visual bugs and irregular patterns thereby improving software quality.
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Faster Product Delivery: Automated maintenance with AI/ ML algorithms is an efficient method to accelerate delivery time.
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Reduced Costs: Employing AI-enabled tools enable engineers to focus on complex issues while automating repetitive tasks, enabling cost reduction.
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Extended Test Coverage: AI-powered test automation tools guarantee a more in-depth and broader scope of software analysis compared to traditional tools.
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Real-time Feedback: AI-driven testing provides early-stage feedback enabling teams to save on effort and resources.
AI in Test Automation: Tools and Use Cases
AI-based test automation finds application in various areas. These include creating UI tests, automating API testing, regulating unit tests, and automating regression testing. Moreover, AI testing tools can speed up the self-healing process, reduce manual tasks, and also modify test scripts to accommodate code changes.
Key automated testing tools with AI capabilities are Appvance, Functionize, Parasoft, Applitools, and Testim.io.
Wrapping It Up
Harnessing the power of AI in automated testing is a progressive step in the QA & testing industry. It simplifies various processes and boosts the capabilities of engineers. With the right tools, AI-based automated testing enhances accuracy, minimizes costs, and quickens delivery times. However, it’s crucial to note while AI-powered tools optimize the test automation life-cycle, they still require human intervention and control for driving optimal results.
Tags: #AI, #AutomationTesting, #MachineLearning, #DeepLearning
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