Systems | Development | Analytics | API | Testing

Modern software development moves fast. Delivering bug free code is no longer just a goal. It is a requirement. But how do you ensure your code works before you even finish writing it? The answer is TDD, or Test Driven Development. In this guide, we will answer what is TDD, explore how it transforms the development lifecycle, and share practical examples you can apply immediately.

Extensibility is conceptually found in the areas of technology (such as Software Development) and System Design. The term extensibility may seem abstract to someone with no previous background in the field; in particular, when you hear phrases like: “Extensible Software”, “Extensible Architecture”, “Extensible API” or “Extensible Testing Tools”, it could be difficult to understand exactly how these would apply in day-to-day experiences.

Modern software development is continuously operating in a high-paced environment with high-pressure expectations to produce quality applications. To meet this expectation, open source automation tools help provide a faster, smoother testing process for today’s applications by providing a single tool to test all layers, including web, mobile, API, and performance.

Have you thought about why some companies can seamlessly scale their technology while others have outages, delays, and an increase in operating costs? As the complexity of digital products and services increases, organizations will continue to experience a challenge—to stay competitive, they cannot rely on legacy manual infrastructure management. Organizations can move from slow provisioning to overcoming configuration errors, then to react quickly to changes in demand.

In the world of software testing, one crucial element often overlooked is Test Data Management (TDM). As development and testing cycles become shorter, automated, and more continuous, the need for efficient management of test data grows. Whether you’re working in Agile, DevOps, or Continuous Integration (CI), having a robust test data management system in place ensures that your tests are reliable, reproducible, and efficient.

You may be surprised to learn that more than 70% of software failures due to unaddressed functional issues that could have been caught during testing. Think about it: you release a new app or system, a user clicks their way through a common user flow…and it fails. In our competitive digital economy, we assume performance and intuitiveness—one hiccup, and users will stop using you as a provider, and possibly undermine your credibility.

In an age where software developers are speeding up their code development to meet the demand of rapid application deployment, there are new tools being developed based on Artificial Intelligence (AI) technology. Replit and Cursor have received a lot of excitement for both of these platforms due to their use of artificial intelligence in assisting developers with coding.

Modern teams push code multiple times a day. But how do you verify that the changes made are free from bugs? And how do you integrate testing as part of your pipelines? In this blog, let’s explore how to integrate Keploy into your CI/CD pipelines. We’ll be using GitHub Actions for the examples.

APIs (Application Programming Interfaces) power nearly every modern digital experience, from mobile apps and online payments to AI-driven services and real-time data processing. As software systems increasingly rely on microservices and distributed architectures, the number of API interactions continues to grow, making reliability and performance more critical than ever.

Imagine pushing a new feature to production, only to discover that it crippled half your APIs, pushed other teams into delays, and launched a series of frantic bug repairs in the middle of the night. For most dev teams, this is not a describe-a-scenario but a reality. With increasingly intricate apps and quickening release rhythms, making sure each code change integrates just right is a vital need. That’s where CI testing enters the picture.