Author: Anil Polywal

Introduction In today’s data-driven world, graphs and network visualizations have become fundamental components of modern applications — from social networks and recommendation engines to financial fraud detection and IoT systems. However, testing these complex graph structures presents unique challenges that traditional testing methods struggle to address effectively. This is where Artificial Intelligence (AI) revolutionizes graph testing. By…

Introduction The software development lifecycle (SDLC) is evolving rapidly, with increasing demands for faster releases, higher quality, and more efficient testing processes. Traditional testing methods, while effective, often rely on reactive approaches—identifying and fixing defects after they occur. However, with the rise of predictive analytics, quality assurance (QA) teams can now anticipate defects, optimize testing efforts, and enhance…

As software systems become increasingly complex and dynamic, traditional test automation methods often fall short in delivering the agility, adaptability, and intelligence needed to keep pace. This gap has given rise to a transformative paradigm—cognitive computing in test automation. By mimicking the way humans learn, reason, and respond to information, cognitive systems can fundamentally reshape…

As businesses increasingly adopt chatbots for customer service, sales, and support, ensuring their quality has become a priority. A poorly performing chatbot can result in user frustration, reputational damage, and lost opportunities. Traditional testing methods often struggle to keep pace with the complex, dynamic nature of conversational interfaces. This is where artificial intelligence (AI) steps…

As software systems become increasingly complex and user expectations continue to rise, traditional test automation methods often fall short in adapting to changing requirements, environments, and behaviors. In this context, cognitive computing is emerging as a transformative force in test automation, promising smarter, adaptive, and context-aware testing systems. But what exactly is cognitive computing? How…

As digital products become more visually complex and user experience continues to influence competitive advantage, ensuring UI consistency and design accuracy is no longer a luxury — it’s a necessity. Visual bugs like misaligned buttons, missing elements, or broken layouts can erode trust, reduce conversions, and frustrate users. This is where visual testing plays a vital role.…

Artificial intelligence has evolved rapidly from purely statistical models to more nuanced, hybrid systems that combine neural networks and symbolic reasoning. This emerging paradigm, known as neuro-symbolic AI, merges the intuitive pattern recognition of neural networks with the logic-driven precision of symbolic systems. While the fusion promises smarter, more explainable AI, it also introduces a…

As AI adoption accelerates across industries, the infrastructure supporting intelligent applications is evolving just as quickly. Federated learning — where AI models are trained and operated across distributed devices like mobile phones, wearables, and IoT sensors — has emerged as a game-changing approach for privacy-preserving AI. But with it comes an important and complex question:…

As industries increasingly rely on digital twins—virtual replicas of physical systems—to optimize performance, predict failures, and simulate real-world behavior, the need to test these systems rigorously has become more important than ever. With AI entering the fold, testing digital twins is no longer just about validating models; it’s about building trust in a system that…

What is Regression Testing and Why is it Important? Regression testing involves re-running functional and non-functional tests to ensure that previously developed and tested software still performs as expected after changes. This process is crucial for maintaining software stability and reliability during development or maintenance. When to Carry Out Regression Tests It’s advisable to perform…