Systems | Development | Analytics | API | Testing

You have probably seen pages where fresh information loads in without a page reload, or some forms that submit without an apparent refresh. What is happening here is that API calls are being made to send and receive information in background. API calls generate seamless and responsive application experiences. In this introductory tutorial, you will learn to examine an API call in the Chrome DevTools; replay it in Python, and sanitise the Flask REST API data for safe and organised input into the database.

In an increasingly connected digital ecosystem, with applications allowing for smooth communication across platforms and services, it is important to understand what is API testing in order to maintain the delivery of reliable software solutions. API testing is foundational to modern software assurance, effectively auditing the invisible bridges lending themselves to data us and functionality between different software components to ensure they are working optimally numerous conditions and scenarios.

Choosing the right tool for quality assurance is not easy. There are so many options that promise to handle everything from planning to reporting. That is why we put together this guide to the 20 end-to-end test management software for 2025. These tools are built to manage the full testing lifecycle in one place, from test case creation to execution, analytics, and reporting.

Agentic AI systems don’t fail like traditional software - they hallucinate facts, pursue the wrong goals, overuse tools, and forget context. These failures look “correct” to traditional test cases, but feel dangerously wrong to users. One team tested an AI support bot - it passed every check, but in production, it gave refund advice that violated company policy. Not a code error. A reasoning failure.

Tired of wasting hours triaging errors and digging through logs? With BugSnag from SmartBear and GitHub Copilot, you can now debug faster : AI surfaces the root cause, explains the issue, and helps you fix it – all from your IDE. Powered by SmartBear’s integration with the Model Context Protocol (MCP), and bringing runtime context into your development tools, errors from BugSnag are automatically enriched with the technical metadata your LLM needs to deliver accurate, relevant suggestions.

Software is now shipped faster than ever and testing evolved beyond rigid scripts and predefined steps. One approach that has always embraced adaptability, critical thinking, and curiosity is exploratory testing: the process of learning, designing, and executing tests simultaneously — often uncovering issues that traditional testing might miss. As Artificial Intelligence (AI) becomes more embedded in the software development lifecycle, many wonder: will AI replace exploratory testing?

As enterprises scale their DevOps initiatives, DevOps bottlenecks are emerging from an unexpected source: inadequate test data management practices. But what can you do about it? In this blog, we will explore the common drawbacks in traditional test data management practices. Then, let’s dive into how real-life enterprises are overcoming DevOps bottlenecks with proven solutions that accelerate delivery and reduce operational risks.

Service level agreements (SLAs) are the contractual backbone between customers and technology vendors, outlining expected service availability, performance metrics, and remedies like service credits when service providers fail to meet agreed-upon service levels. This service agreement assures both the technical quality as well as the service quality of the services provided, and underpins the value perspective of the client.