coverage_pth | pth file which enables coverage | Code Coverage Tools library
kandi X-RAY | coverage_pth Summary
kandi X-RAY | coverage_pth Summary
Installs a .pth file which enables coverage for all of python
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of coverage_pth
coverage_pth Key Features
coverage_pth Examples and Code Snippets
Community Discussions
Trending Discussions on Code Coverage Tools
QUESTION
In our Jenkins pipeline, we use SonarQube to report on our code coverage. After running all of our unit/integration tests to produce the .coverage file, we need to analyze this file to create the ".coverage.coveragexml" which is ultimately what is used by SonarQube to interpret the code coverage. We do this by using the CodeCoverage.exe:
...ANSWER
Answered 2022-Mar-25 at 19:29It seems the base image we use must have a non-enterprise edition of the Code Coverage tools (which is a requirement). We tested our SonarQube projects commands locally using an enterprise edition of the tools (I have Visual Studio 2022 Enterprise installed on my machine), and the coverage files produced contain the correct data. However, when we used a Visual Studio Professional install, the files are empty just like our Jenkins pipeline.
As stated, this started happening when the base image was updated - in particular it was around November 8th 2021. It seems the base docker image we were using (mcr.microsoft.com/dotnet/framework/sdk:4.8-20220210-windowsservercore-ltsc2019) has the latest 2022 tools, but it must not be an enterprise edition - hence the empty files.
We switched our pipeline over to using dotCover instead to perform the analysis, which works as expected and our SonarQube coverage is back to normal.
QUESTION
A very typical scenario with GitLab CI is to install a few packages you need for your jobs (linters, code coverage tools, deployment-specific helpers and so on) and to then run your actual stages/steps of a building, testing and deploying your software.
The Docker runner is a very neat and clean solution, but it seems very wasteful to always run the steps that install the base software. Normally, Docker is able to cache such layers, but with the way the GitLab Docker runner works, that doesn't happen.
Do we realize that setting up another project to produce pre-configured Docker images would be one solution, but are there any better ones? Basically, what we want to say is: "If the before
section hasn't changed, you can reuse the image from last time, no need to reinstall wget
or whatever".
Any solution like that out there?
...ANSWER
Answered 2020-Jan-29 at 14:23You can use the registry of your gitlab project.
eg.
QUESTION
I am using .Net Core Test --collect "Code coverage"
to generate a coverage file, I need to convert this for sonarqube, the issue is I do not nave the name of the file thats generated as its placed in a folder with a guid name and the file name itself is a GUID all under the TestResults
folder
The following script works to convert .coverage
files into coveragexml
, but its for the whole working directory
ANSWER
Answered 2020-Jan-29 at 09:52So you want to take only the last created code coverage file, you can filter the Get-ChiledItem
results to get the last one:
QUESTION
I have a rails model that contains an ActiveRecord::Enum
. I have a view helper that takes a value of this enum, and returns one of several possible responses. Suppose the cases were called enum_cases
, for example:
ANSWER
Answered 2020-Jan-16 at 01:36enum_cases
must be kept up to date when the production logic changes violating the DRY principle. This makes it more likely for there to be a mistake. Furthermore it is test code living in production, another red flag.
We can solve this by refactoring the case into a Hash lookup making it data driven. And also giving it a name describing what it's associated with and what it does, these are "handlers". I've also turned it into a method call making it easier to access and which will bear fruit later.
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Install coverage_pth
You can use coverage_pth like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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