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SDLC Security: It's Personal for JFrog

The SolarWinds hack, which has affected high-profile Fortune 500 companies and large U.S. federal government agencies, has put the spotlight on software development security — a critical issue for the DevOps community and for JFrog. At a fundamental level, if the code released via CI/CD pipelines is unsafe, all other DevOps benefits are for naught.

Using Helm to Deploy a Kubernetes Application to Multiple Environments (QA/Stage/Prod)

One of the most typical challenges when deploying a complex application is the handling of different deployment environments during the software lifecycle. The most typical setup is the trilogy of QA/Staging/Production environments. An application developer needs an easy way to deploy to the different environments and also to understand what version is deployed where. Specifically for Kubernetes deployments, the Helm package manager is a great solution for handling environment configuration.

Generate Code Coverage Reports using Coveralls and Codefresh

Coveralls is a web service that allows users to track the code coverage of their application over time in order to optimize the effectiveness of their unit tests. Once you are managing your application and associated resources within a CI/CD platform like Codefresh, you want to receive insights on the test coverage automatically with every pipeline build. This post provides an overview of how this can be achieved with Coveralls and Codefresh.

Install JFrog Platform on Kubernetes in Under 20 Minutes

We get it, installing Artifactory and the JFrog DevOps Platform on Kubernetes can be daunting. As easy as we’ve sought to make it with our official JFrog installation Helm charts, there are a lot of decisions to be made. That’s meant to give you the widest possible choice for how to best fit your JFrog installation to your infrastructure. But choice can be overwhelming, too.

Where is Your Next Release Bottleneck?

A typical modern DevOps pipeline includes eight major stages, and unfortunately, a release bottleneck can appear at any point: These may slow down productivity and limit a company’s ability to progress. This could damage their reputation, especially if a bug fix needs to be immediately deployed into production. This article will cover three key ways using data gathered from your DevOps pipeline can help you find and alleviate bottlenecks in your DevOps pipeline.

Code Coverage Analysis Using Codecov and Codefresh

Codecov is a code analysis tool with which users can group, merge, archive, and compare coverage reports. Code coverage describes which lines of code were executed by the test suite and which ones were not. However, this is not to be confused with a testing tool. Codecov does not run your tests, that is the job of your testing tools. The analysis that Codecov provides will classify code in either of the following states: Additionally, In this tutorial, we will.

All That Developers Need Is a Browser (or How to Be More Productive by Having Less)

What would you say if I would tell you that you can be as productive with the cheapest laptop as with the one you already have? Would you believe me if I would say that there is no need for you to install an IDE, compilers, CLIs, Docker, and whatever else you might have on your laptop? How about having a full development environment created whenever you need it instead of dealing with virtual machines and whatever else might be fulfilling your development needs?

Achieving Continuous Deployment with Artifactory Webhooks & Docker

Continuous Deployment (CD) requires setting up your infrastructure and automation to update your solution with the latest code change from the main branch. That’s what we call “Liquid Software”. Full automation makes your deployment seamless, less error prone, faster and it makes the feedback loop shorter because you can now deploy after each change. Achieving continuous deployment requires the following elements.

Manual steps in parallel groups available for Pipelines

Bitbucket Pipelines now allows steps with a manual trigger to be used in parallel groups, satisfying one of the highest voted feature requests. This feature provides more flexibility in Pipelines, allowing teams to configure pipelines with multiple options and then only run the steps they actually need to run, at the time they want. For example you can choose which environments should be deployed for individual developers, giving them different environments to test and do their work.