Systems | Development | Analytics | API | Testing

It’s easy to create APIs very quickly. However, creating APIs that are reliable when used in challenging distributed systems is much harder. Many development teams are moving quickly to ship features to the market, which has led to the verification vs validation distinction becoming the middle ground between a successful launch and an incident that could occur from a production system.

Today’s software ecosystem consists of various devices, integrations, and user environments. The same application can have different behaviours in each environment, regardless if it had passed through unit, integration and system testing stages. Therefore, beta-testing is necessary. Beta testing is the bridge between an internal QA team and a company publicly launching its application.

Every digital experience we rely on – from booking cabs to transferring money — runs on dynamic, interconnected software systems. The speed at which applications are evolving is much faster than the traditional test approach can keep up with. Manual scripting breaks whenever there is a change to the user interface; automation will require regular maintenance to fix the automated scripts; and teams are continually losing confidence in the release stability.

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.

In today’s fast-paced software environment, getting product features out the door quickly is the minimum. Getting features out the door quickly + with reliability is what separates high performing teams opening up larger opportunities. This is where DevOps testing comes into play, testing not just at the endpoint of the development + operations lifecycle but as an integrated process throughout every step of the life of the product.

Software Development is progressing faster than ever, as software teams are now able to regularly release new features in cycles that often last a week to a day. As a result of this cycle, bug tracking and QA processes are sometimes not enough to prevent there from being bugs in place before a problem arises. When these bugs enter production, they cause a lot of frustration among users, downtime, and a large amount of time being spent trying to fix the problem which makes users angry.

In today’s world of software development, API-first has become more than a trend it is a best practice that allows teams to build scalable, modular and best of all future-ready applications. In an API-first approach, the Application Programming Interface (APIs) are not an afterthought, they are envisioned, documented and agreed upon before the development of an project in begins.

The user experience of an app can often generate impacts in unexpected areas. As the industry shifts to a rapid development cycle with more new features being released rapidly and a growing number of code changes each month, development teams find it difficult to deliver new capabilities while also maintaining stability. The question is not whether regression testing is necessary. It is how to decide which form of regression testing is best for a given change.

In the digital-first world of today, many applications depend on RESTful APIs as the connectivity placeholder: whether between microservices, mobile applications, third-party integrations or SaaS platforms, it is important for ensuring these APIs are reliable, secure and performant. This is where you can utilize REST API testing.

Choosing the best project methodology for a software project can help or hinder it greatly. Teams frequently evaluate Agile vs Waterfall. When teams choose an approach, they often have to consider the schedule and the manner in which they want to deliver the software. This choice becomes especially important when engineering teams and QA teams work together. Rapid changes during development along with rapid product releases and QA testing are common.

Have you observed how many software projects often begin with enthusiasm but however time become stuck in endless planning, changing requirements, and delayed delivery?

Developers and Quality Assurance (QA) teams utilize many different API testing and validation tools to help them simplify the processes of testing, debugging, and validating APIs in the increasingly API-centric world of software development. Modern teams often combine End-to-End Testing with API-level testing to ensure full workflow reliability.

In the last ten years, software testing has advanced significantly, but today’s applications require more than just using conventional forms of automated software testing or entry-level tools that employ artificial intelligence (AI). The rise of microservice architectures, API calls, and continuous deployment has led to another category of software testing products called "Generative" AI Testing tools.