How to Install and Uninstall perl-Test-Regression.noarch Package on Fedora 35
Last updated: December 12,2024
1. Install "perl-Test-Regression.noarch" package
Please follow the guidance below to install perl-Test-Regression.noarch on Fedora 35
$
sudo dnf update
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$
sudo dnf install
perl-Test-Regression.noarch
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2. Uninstall "perl-Test-Regression.noarch" package
This tutorial shows how to uninstall perl-Test-Regression.noarch on Fedora 35:
$
sudo dnf remove
perl-Test-Regression.noarch
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$
sudo dnf autoremove
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3. Information about the perl-Test-Regression.noarch package on Fedora 35
Last metadata expiration check: 4:16:17 ago on Wed Sep 7 02:25:42 2022.
Available Packages
Name : perl-Test-Regression
Version : 0.08
Release : 16.fc35
Architecture : noarch
Size : 16 k
Source : perl-Test-Regression-0.08-16.fc35.src.rpm
Repository : fedora
Summary : Test library that can generate outputs and compare against them
URL : https://metacpan.org/release/Test-Regression
License : GPL+ or Artistic
Description : Using the various Test:: modules you can compare the output of a function
: against what you expect. However if the output is complex and changes from
: version to version, maintenance of the expected output could be costly.
: This module allows one to use the test code to generate the expected
: output, so that if the differences with model output are expected, one can
: easily refresh the model output.
Available Packages
Name : perl-Test-Regression
Version : 0.08
Release : 16.fc35
Architecture : noarch
Size : 16 k
Source : perl-Test-Regression-0.08-16.fc35.src.rpm
Repository : fedora
Summary : Test library that can generate outputs and compare against them
URL : https://metacpan.org/release/Test-Regression
License : GPL+ or Artistic
Description : Using the various Test:: modules you can compare the output of a function
: against what you expect. However if the output is complex and changes from
: version to version, maintenance of the expected output could be costly.
: This module allows one to use the test code to generate the expected
: output, so that if the differences with model output are expected, one can
: easily refresh the model output.