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10 Challenges Of Implementing AI In Quality Assurance

We’ve all read the hyperbolic headlines surrounding AI—that it will revolutionize all aspects of our lives, both personal and professional. While we cannot know ultimately how much transformation will occur due to AI, it is clearly a force to reckon with and is already having an impact on the field.

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2024: The Year of Testing

Caveat: I currently cover DevOps – including test – and security – including the *AST. This does give me a viewpoint that others may not have, and also potentially gives me blinders that others may not have. I am not generally a predictor but a reader of direction. Proclamations like.

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Evaluating The Impact: How QA Will Assess The Power Of New Generative AI-Based Testing Tools

The advent of generative AI-based testing tools is poised to revolutionize the landscape of Quality Assurance (QA) in software development. As these intelligent automated QA testing tools harness the capabilities of artificial intelligence to create, execute, and adapt test cases autonomously, QA teams face the challenge and opportunity of evaluating their impact.

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What a Scalable Approach to Testing Looks Like

Scalability is a necessity in the big picture of digital quality. But, along with it, organizations can expect a bit of disruption to their traditional testing workflows. Organizations have several options for scalable testing approaches to ensure application quality, but be prepared for teams to reskill or retrain along the.

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Machine Learning in Predictive Testing for DevOps Environments

In today’s fast-paced technological world, DevOps has become an integral part of software development. It emphasizes collaboration, automation, continuous integration (CI) and continuous delivery (CD) to improve the speed and quality of software deployment. Predictive testing is a key component in this landscape, where machine learning (ML) plays a pivotal role. By.

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Applying AI/ML to Continuous Testing

Artificial intelligence (AI) and machine learning (ML) can play a transformative role across the software development lifecycle, with a special focus on enhancing continuous testing (CT). CT is especially critical in the context of continuous integration/continuous deployment (CI/CD) pipelines, where the need for speed and efficiency must be balanced with.

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Now, Not Later: The Power Of Proactive QA

The allure of taking a wait-and-see approach to ensuring production quality is no mystery. It allows manufacturers to continue (or kick off) production with one less overhead cost—an enticing thought as the costs of doing business grow. For smaller manufacturers, pulling back on quality assurance (QA) may seem like the.

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Efficient Testing Practices to Maximize ROI

In today’s software development and testing environment, QA professionals face tightening budgets and delays in completing product roadmaps. What does that mean for their work? Testing teams must find a way to deliver measurable business value and optimize operating efficiency without sacrificing quality. Development and testing processes in most organizations.

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Using generative AI to improve software testing

Generative AI is getting plenty of attention for its ability to create text and images. But those media represent only a fraction of the data that proliferate in our society today. Data are generated every time a patient goes through a medical system, a storm impacts a flight, or a.

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The importance of edge case testing: When to fix the bug

Sometimes it's easy to determine the urgency of problems unveiled during the software testing process. Other times, edge cases emerge. While major bugs that affect many users should be fixed as quickly as possible, edge case issues are more difficult to prioritize. These are problems that affect a limited number.

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