Author: Vaibhav Kulshrestha

Introduction For years, software teams followed a strict rule: Never test in production! However, with the rise of cloud computing, DevOps, and continuous delivery, this mindset is evolving. Testing in production is no longer a reckless practice—it’s a strategic approach to ensuring software quality in real-world environments. But is it really a good idea? Let’s…

Introduction In today’s fast-paced software development landscape, ensuring system reliability is more critical than ever. Traditional testing methods help identify known issues, but what about unknown vulnerabilities? This is where Chaos Engineering comes in—a proactive approach to testing system resilience by intentionally injecting failures to uncover weaknesses before they cause real-world outages. What is Chaos…

Introduction As software applications become more complex, traditional test automation struggles to keep up with frequent releases, dynamic interfaces, and diverse testing requirements. This is where AI-powered test automation is transforming the landscape, enabling faster, smarter, and more reliable testing. With AI, testing is no longer just about writing scripts—it’s about self-learning, predictive analysis, and…

Introduction Traditional software testing often takes place towards the end of the development cycle, making it difficult to detect defects early. This leads to increased costs, longer release cycles, and poor software quality. Enter Shift-Left Testing, a proactive approach where testing is integrated earlier in the software development lifecycle (SDLC). But why is this approach…

Introduction Software testing has come a long way from manual test execution to automated scripts. Now, artificial intelligence (AI) and machine learning (ML) are pushing the boundaries even further. As development cycles shrink and the demand for flawless software increases, traditional testing methods alone are no longer enough. The future of software testing lies in…

Introduction As software development accelerates, traditional testing approaches often struggle to keep up with rapid release cycles. AI-powered test automation is revolutionizing software testing by making it faster, smarter, and more efficient. With machine learning algorithms, predictive analytics, and self-healing test scripts, AI is transforming how teams ensure software quality. Why AI in Test Automation?…

Introduction Software testing has evolved significantly, but traditional testing methods often miss subtle bugs that can compromise software quality. Mutation Testing is an advanced technique that challenges test suites to improve their effectiveness. In 2025, where AI-driven applications, cybersecurity concerns, and rapid development cycles demand more resilient software, Mutation Testing is gaining prominence as a…

🚀 In today’s fast-paced development cycles, testing everything is unrealistic. Risk-Based Testing (RBT) is a strategic approach that ensures high-risk areas get the most attention, improving efficiency, reliability, and product quality. 🔹 What is Risk-Based Testing? Risk-Based Testing (RBT) is a testing strategy that prioritizes test efforts based on:✅ The likelihood of failure 📉✅ The…

Software testing is evolving rapidly, and AI-powered test automation is at the forefront of this transformation. Traditional test automation has its limitations—high maintenance costs, flakiness due to UI changes, and slow adaptation to dynamic applications. However, Artificial Intelligence (AI) is now reshaping the landscape by making test automation smarter, faster, and more reliable. 🔍 The…

In today’s fast-paced software development environment, delivering high-quality applications quickly is a necessity. DevOps has transformed software delivery by integrating development and operations, enabling rapid and continuous releases. However, ensuring quality in this fast-paced environment can be challenging. This is where Continuous Testing (CT) plays a crucial role. Continuous Testing is an integral part of…