Codacy automates code reviews and monitors code quality on every commit and pull request reporting back the impact of every commit or pull request, issues concerning code style, best practices, security, and many others. It monitors changes in code coverage, code duplication and code complexity. Saving developers time in code reviews thus efficiently tackling technical debt. JavaScript, Java, Ruby, Scala, PHP, Python, CoffeeScript and CSS are currently supported. Codacy is static analysis without the hassle.
Based on our record, locust seems to be a lot more popular than Codacy. While we know about 55 links to locust, we've tracked only 4 mentions of Codacy. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I'm trying to use Codacy to review my code. One of the issues is regarding the use of the "setcookie" function. Source: over 2 years ago
Does anyone have an example on how to get this conversion done on github actions where I can convert the *.coverage file into a *.xml file for uploading to codacy.com. Source: over 2 years ago
Online analysisFinally, if you want a simple way to analyze your code without having to manually configure everything locally, you can use an online code review service such as Codacy (shameless plug here). We already integrate some of the mentioned detection tools in this article and we are working every day to improve the service. The other main benefit of using automated code review tools is to allow you to... - Source: dev.to / about 3 years ago
Because you care and because you always want to be better, automation is a great way to optimize your review workflow process. Go ahead and do a quick search on Google for automated code reviews and see who better fits your workflow. You'll find Codacy on your Google search and we hope you like what we do. - Source: dev.to / about 3 years ago
Finally, let's compare the response time of the requests. For that, we will use Locust , an open source load testing tool. The tests will run for 5 minutes, and will increase 4 requests per second every second until they reach 1000 requests per second. - Source: dev.to / about 2 months ago
Locust: Another open-source tool, Locust is particularly flexible due to its support for Python scripts. It can conduct load tests across multiple machines, making it possible to simulate millions of users simultaneously. An exceptional feature of Locust is its web-based UI, which allows real-time tracking of performance metrics during test execution. - Source: dev.to / about 2 months ago
Locust is a perfect tool to use on such occasion:. - Source: dev.to / 2 months ago
So, in theory, we can handle 300 requests per minute on a single server which was the assumption we started with. After this, I decided to play with this configuration and see what we could achieve. But, to go ahead I need a system to measure the metrics of our load testing. So I quickly set up Locust on my system. Locust is an open-source easy to setup load-testing framework. - Source: dev.to / 2 months ago
The OpenTelemetry Demo is composed of microservices written in different programming languages that talk to each other over gRPC and HTTP; and a load generator which uses Locust to fake user traffic. - Source: dev.to / 6 months ago
SonarQube - SonarQube, a core component of the Sonar solution, is an open source, self-managed tool that systematically helps developers and organizations deliver Clean Code.
Apache JMeter - Apache JMeter™.
CodeClimate - Code Climate provides automated code review for your apps, letting you fix quality and security issues before they hit production. We check every commit, branch and pull request for changes in quality and potential vulnerabilities.
Loader.io - Loader.io is a simple cloud-based load testing service
CodeFactor.io - Automated Code Review for GitHub & BitBucket
gatling.io - Gatling is an open-source load testing framework based on Scala, Akka and Netty