Validata Blog: Talk AI-powered Testing

Measuring Software Development Effectiveness

Measuring Software Development Effectiveness

There is a saying that goes, 'You can't manage what you can't measure.' In software development, the true measure of effectiveness doesn't lie solely in the lines of code written or the frequency of commits; it's found in the rate at which you deliver tangible value to your users. The ability to consistently provide value to users, promptly respond to their needs, and transform these into functional features is the ultimate gauge of success. In essence, it's not about how frequently you change the code; it's about how often you can positively impact and delight your users with meaningful updates and enhancements.

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How AI Is Shaping the Future of Quality Assurance

How AI Is Shaping the Future of Quality Assurance

Software testing is currently undergoing a big transformation. We are seeing a major move towards using Artificial Intelligence (AI) to automate the design and validation of tests while significantly reducing reliance on human effort. With AI in the mix, banks and financial organizations can generate and execute tests autonomously, refining its capabilities through human guidance and input. As a result, we are on the brink of having access to virtual testing workforces for every development team.

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Common Test Data Challenges

Common Test Data Challenges

The quest for fast, dependable test data poses numerous hurdles for application development teams, ranging from the speed and quality of data movement to concerns about security and costs throughout the software development lifecycle (SDLC). Below, we outline the most prevalent challenges organizations encounter in managing test data.
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DataOps vs. DevOps: The Dual Engines of Digital Transformation

DataOps vs. DevOps: The Dual Engines of Digital Transformation

When comparing DataOps and DevOps for practical digital transformation, it's essential to recognize that they serve distinct purposes. DevOps has significantly revolutionized software delivery for companies, while DataOps is transforming data utilization. Rather than debating which approach is better, the more pertinent question is: How can I harness both DevOps and DataOps to amplify the value I provide to my customers?
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