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AI Code Generation Inside ADLC: How It Cuts Dev Time Without Cutting Quality

ADLC, ai, AI (Artificial Intelligence), AI CI/CD, AI Code Generation, AI Coding Tools, ai development, AI Development Lifecycle, AI in Software Development, AI software development lifecycle, AI Workflow Automation, AI-Assisted Coding, Artificial Intelligence, Devops, DevOps & Automation, digital transformation, Enterprise AI Development, Enterprise Technology, GitHub Copilot, Intelligent Testing, LLM in Software Engineering, software development, Spritle Blog, SpritleSoftware

Introduction Development timelines are shrinking, but expectations are rising. US engineering teams are expected to ship faster, iterate more often, and still maintain production-grade quality. According to GitHub’s 2025 developer report, over 70% of teams now use some form of AI-assisted coding, yet many still struggle to translate that into real delivery speed. Here’s the […]

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From Exploration to Application: My Journey Building an AI-Assisted API Testing Tool

ADLC, ai, AI (Artificial Intelligence), AI Debugging, ai development, AI Development Lifecycle, AI in Software Development, AI Observability, Artificial Intelligence, CI/CD, DevOps & Automation, Intelligent Testing, Predictive Bug Detection, Software engineering, Software Quality Assurance, Spritle Blog, SpritleSoftware, Technology & Innovation, Technology Insights, Technology Trends

A few weeks ago, I started exploring how AI could support backend automation testing using Antigravity. My goal was simple—reduce the effort involved in writing, maintaining, and scaling API tests while improving overall efficiency. I began by experimenting with backend test execution using REST Assured, with Antigravity assisting me in generating and structuring test logic.

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AI Debugging in ADLC: Catching Production Bugs Before They Exist

ADLC, ai, AI (Artificial Intelligence), AI Debugging, ai development, AI Development Strategy, AI in Engineering, AI in Software Development, AI Observability, AI software development lifecycle, Artificial Intelligence, Bug Prevention, DevOps & Testing, DevOps AI, Intelligent Testing, Predictive Debugging, software development, Software Development Life Cycle, Software Quality, Spritle Blog, SpritleSoftware, Technology & Innovation

Introduction Production bugs are expensive—but the real cost isn’t just fixing them. It’s lost revenue, damaged trust, and engineering time spent firefighting instead of building. According to IBM’s Cost of a Data Breach Report (2023), issues caught in production can cost up to 15x more than those identified during development. The uncomfortable truth? Traditional debugging

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From Physical AI to AI-Augmented QA:  The Next Evolution of Testing

ai, AI (Artificial Intelligence), AI & QA Innovation, AI in QA, AI Software Testing, AI Testing, AI-Augmented QA, Artificial Intelligence, automation testing, digital transformation, Future of QA, Intelligent Testing, physical ai, QA, QA Automation, Quality Assurance, Quality Engineering, Software Testing, Spritle Blog, SpritleSoftware, Technology Trends, Test Automation

Many of you may already be familiar with Physical AI — the evolution of artificial intelligence from purely digital intelligence to systems that understand and interact with the real world. Physical AI enables machines to: Understand physical environments Adapt to real-world conditions Make autonomous decisions Execute actions in dynamic systems This shift — from information

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