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2 weeks ago | 
devops.com | Saqib Jan
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3 weeks ago | 
datasciencecentral.com | Saqib Jan
AI has radically changed Quality Assurance, breaking old inefficient ways of test automation, promising huge leaps in speed and the ability to test things we otherwise couldn’t easily test before. But getting AI to be trusted by QA teams is a major challenge across the industry. A big part of the difficulty stems from the “black box” nature of AI tools where the internal processing is abstracted away.
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1 month ago | 
devops.com | Saqib Jan
Application programming interfaces (APIs) are the backbone of modern applications. However, failures — whether from third-party outages, network issues or rate limits — can cause major disruptions. While traditional testing falls short in preparing teams for these real-world issues, something more is needed to build a truly resilient system. Most companies often make common engineering mistakes when adopting technology, generation after generation.
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1 month ago | 
buff.ly | Saqib Jan
Application programming interfaces (APIs) are the backbone of modern applications. However, failures — whether from third-party outages, network issues or rate limits — can cause major disruptions. While traditional testing falls short in preparing teams for these real-world issues, something more is needed to build a truly resilient system. Most companies often make common engineering mistakes when adopting technology, generation after generation.
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1 month ago | 
thenewstack.io | Saqib Jan
Generative AI (GenAI) is helping many of us with things we do every day. It is also rapidly advancing quality assurance (QA), fueling breakthroughs in testing that promise to exponentially speed up delivery and achieve unprecedented scale, potentially shattering old limits on automation. But getting testing teams to trust GenAI is also proving more challenging than it looks. A big part of the problem is the feeling that AI is a black box with unclear processes and outputs.
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2 months ago | 
datasciencecentral.com | Saqib Jan
Software development cycles accelerate constantly, pushing quality assurance teams to keep pace. However, the pressure engineering leaders face to ensure quality under the speed and complexity modern pipelines require is also immense. And simply doing more of the same old way of things isn’t enough with the advancing user demands. Interestingly, while much of the focus has been on accelerating coding or transforming creative workflows, GenAI is profoundly reshaping quality assurance.
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2 months ago | 
devops.com | Saqib Jan
As organizations strive for faster, more reliable software delivery, continuous integration and continuous deployment (CI/CD) pipelines function as the critical arteries of modern development. However, when these essential pathways become slow, unreliable or require excessive manual intervention, they transform from conduits of innovation into significant sources of friction.
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2 months ago | 
thenewstack.io | Saqib Jan
As engineering teams grapple with increasingly distributed architectures, microservices and the need for rapid innovation, the technical challenges of building and maintaining reliable CI/CD systems at scale have become more important than ever. However, successfully navigating these technical complexities is also about achieving speed and efficiency in software delivery while creating a positive and productive experience for the engineers who build and maintain these systems.
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Mar 27, 2025 | 
cloudnativenow.com | Saqib Jan
Kubernetes has emerged as the de facto standard for orchestrating containerized applications, offering unprecedented agility, scalability and speed in delivering software. It enables organizations to respond rapidly to market demands and evolving customer needs. Teams adopt Kubernetes precisely because it accelerates development and deployment cycles, stirring up innovation at a pace otherwise impossible.
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Mar 27, 2025 | 
cloudnativenow.com | Saqib Jan
Kubernetes has emerged as the de facto standard for orchestrating containerized applications, offering unprecedented agility, scalability and speed in delivering software. It enables organizations to respond rapidly to market demands and evolving customer needs. Teams adopt Kubernetes precisely because it accelerates development and deployment cycles, stirring up innovation at a pace otherwise impossible.
