Systems | Development | Analytics | API | Testing

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.