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ci-cd-pipeline

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ci-cd-pipeline

Code Build Environment Variables | Common Types & Associated Mistakes

The use of environment variables in continuous integration/continuous deployment (CI/CD) pipelines or code build systems allows developers to transmit configuration data, settings, or secrets to the build process without having to hardcode them into the source code.
ci-cd-pipeline 409

What is CI/CD & How Does It Work? | Basic Concepts and Structure

Wondering about the basic concepts of what is CI/CD and how it works? This article is a one-stop knowledge pager for everyone interested in learning professional skills.
ci-cd-pipelinejenkinssource-control-management 385
Read More
1How to Apply Token for PerfDogService DISCOVER THE SIMPLE STEPS TO APPLY FOR A TOKEN IN PERFDogSERVICE AND BEGIN STREAMLINING YOUR PERFORMANCE TESTING WORKFLOW TODAY.
2Most common types of software bugs in QA Testing As per Wikipedia, "A software bug is an error, flaw or fault in a computer program or system that causes it to produce an incorrect or unexpected result, or to behave in unintended ways".
3An Introduction to Levels of Software Testing & Implications Software testing is a process that consists of different levels of software testing, each with its own specific goal and purpose. In this article, we will discuss these levels and what they mean for you as a developer or QA professional working in the industry.
4Revolutionizing Mobile App Testing: Challenges and Solutions This article highlights the top eight challenges of traditional mobile testing and provides solutions to overcome them. It sheds light on the current state of mobile development and testing practices, and how WeTest can revolutionize mobile application testing with its four key benefits.
5Optimizing Keyframe Recognition in App Startup Time-Consuming Analysis Using Scikit-Learn This article briefly introduces the model optimization process using the scikit-learn image classification algorithm in start-up time-consuming applications. In the subsequent sequel, TensorFlow CNN, transfer learning, and other algorithms will be used to provide a comparison of recognition effects.
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