Skip to main content

Selenium testing in Jenkins with an in-memory X server


This articles explains how to setup integration testing of web applications (that require a browser instance) on a Jenkins server. The article assumes you are able to understand the title. If you need a reminder follow the links below.

Automated browser testing (source: Jeremy Keith)
What is Selenium? Read here.
What is Jenkins? Read here.
What is an X server? Read here.

If you are still puzzled, this article is not for you.

Integration testing of web application is more complicated than unit testing, because they require a browser instance to be running. In order to run a browser (such as Firefox) you need and X server. The testing server usually doesn't have one.

One solution to this is to run the browser instance on another machine through VNC. This has the advantage of not using the same resources as the testing server. However it requires setting up a new machine, and configuring the integration tests to run a browser in it.

Another solution is to use Xvfb, a display server that implements the X11 protocol, but does not require a screen to run. Instead all the operations are run in-memory. This allows Firefox to run without complaining, because it is tricked into believing it has a screen.

Step by step installation instructions:

1. Install Xvfb (and fonts)

apt-get install xvfb x11-xkb-utils xfonts-100dpi xfonts-75dpi xfonts-scalable xfonts-cyrillic x11-apps

2. Install the xvfb-plugin for Jenkins from the Jenkins Admin dashboard.

3. The administrator also needs to configure Jenkins as shown here.

4. The rest can be done on a per Jenkins-project basis, without administrator rights. Simply follow the instructions here.

Comments

  1. This is a a really nice blog, You can visit here for more information that will feed your blog.........

    ReplyDelete

Post a Comment

Popular posts from this blog

Basic cell counting and segmentation in Matlab

Counting cells manually is a tedious error prone process for humans. Given a large data set of microscopy images this task can be achieved much faster by means of basic computer vision techniques. In this tutorial we will segment cells from an image following a method similar to the one presented by Yongming Chen in 1999. The method uses basic morphological operations and the watershed algorithm to segment the cells. Nowadays better methods for cell segmentation exist. This method was chosen for its simplicity and ease of implementation.

We start with an image of cell-like structures by Anna-Katerina Hadjantonakis and Virginia E Papaioannou.

A = imread('cells.jpg');
We convert the image to grayscale:
I = rgb2gray(A);

To be able to extract the dimmer cells, it is necessary to perform some local contrast adjustments
I = adapthisteq(I);


Objects on the borders can be caused by noise and other artifacts. We can eliminate objects on the borders of the image like this:
I = imclearborder(…

Project planning in a text file

Whenever you work on a project it is important to be able to plan it ahead of time. This holds true for small and big project, from planning a trip to the spa to building a spaceship. The small project plans can be maintained in you thoughts while bigger ones require tools to help you see the big-picture of the project and manage task at a lower level. There are projects which start with a fully prepared plan and projects which pivot overnight, thus invalidating any original plan. For the latter flexibility is very important, and tools like Trello offer a great solution because they can be adjusted to fit your project.

However, it may happen sometimes that the project starts adjusting to the tool or that you still want to maintain a bigger picture of the main points of the project. You may also need to produce a rough development schedule to serve as a long term road-map.

I have prototyped a tool (and defined a workflow) which allows you to plan such projects.

To better understand how…