Systems | Development | Analytics | API | Testing

Doing things manually on GitLab is okay, but it can get repetitive and time-consuming. That’s where the GitLab Python API comes to the rescue! With it, you can automate tasks like creating projects, branches, or merge requests using just a few lines of Python code. It’s incredibly handy for DevOps, scripting, or personal projects! In this blog, we’ll dive into setting up and using the GitLab Python API step by step.

CORS errors can be a frustrating roadblock during API testing - but they’re fixable. These errors happen when your web app tries to access resources from another domain without proper permissions. Here’s how to tackle them step by step.

At SmartBear, we celebrate innovators in software testing who make a real impact on their teams. The Automation All-Star Award celebrates the pioneers using SmartBear Zephyr’s no-code automation to achieve transformative results. We’re thrilled to share the stories of this year’s winner, RAC Motoring Services, and our fantastic finalists: Capgemini, Infor PSSC, Pepper Money, and the Center for Sustainable Energy (CSE).

This benchmark report provides a side-by-side comparison of Google’s Gemini 2.5 Pro and OpenAI’s o1 models in AI-driven software testing. Across both unit test generation (UTG) and API test generation (ATG), Gemini 2.5 Pro demonstrated clear superiority in key areas. In summary, Gemini 2.5 Pro outperformed OpenAI o1 by: OpenAI’s o1 model had strengths in smaller-scale applications, but overall it generated shallower tests and struggled to match Gemini on complex projects.

This article originally appeared on SD Times on May 30, 2025. We’re sharing it here for our audience who may have missed it. The headlines are seductive: AI will replace developers. Coding is dead. Ship 10x faster with half the team. It’s the kind of hype that grabs attention and fuels confusion.

Due to artificial intelligence and Large Language Models, having streamlined data communication, easy access and easy discovery is now more important than ever. Many people are unaware of it, but the llms.txt file is very important and key to what happens in the machine learning and web sectors. With this plain text file, websites and platforms can interact with LLMs, giving metadata that can impact the way AI indexes, interprets and generates their content.

Ever deployed code only to watch everything crash? We’ve all experienced that sinking feeling ,which is exactly why testing matters so much. While most developers understand that testing is vital, two important approaches : unit testing and regression testing are frequently confused, despite serving completely different functions. In this blog, we’ll clear up the confusion, examine their core methodologies, and how tools like Keploy can automate and enhance your testing processes.

Automation can make cloud migration faster, cheaper, and safer. It reduces migration time by 50%-80%, lowers costs by up to 30%, and minimizes risks like data loss and downtime. Manual migrations often fail due to human error, complexity, and unforeseen expenses. Here's how automation solves these challenges.

How we architected a responsive, HTTP-aware scale-to-zero mechanism to improve resource efficiency across cloud native workloads in Choreo. Cloud platforms promise elasticity, but achieving true efficiency, especially for idle or sporadically used services, requires more than just horizontal scaling. At Choreo, we set out to rethink how workloads behave when they're not actively serving requests.

In the field of software quality assurance, there are two types of testing often referenced, smoke testing and regression testing. While they are both vital to software quality, each has its own unique functions and overlaps in the software development cycle. This post explores the differences between smoke testing vs regression testing, and when and how to effectively implement each.