Code Coverage For Python

Python is the most important language to develop quality products and code coverage for python metrics play a crucial role in maintaining the quality of that product. So, let us see how to use Code Coverage and Python together to obtain the best product possible.

Many applications, software testing, and websites are developed daily. So, this software and applications’ quality plays a crucial role in being ahead of the competition. 

The quality of the programs majorly depends upon the quality of the code. Many tests are run to assure the quality of the codes. Unfortunately, these tests are not sufficient as they cannot cover each Line of the code. So software testing metrics like Code Coverage and Test Coverage are used to check these tests’ efficiency.

Table of Contents

Unit testing  and generating code coverage for python using the open-source tool

Typically, in any programming language like Python, Kotlin, Java, or Swift, tests can be manual and automated. As manual tests take a long time, automated tests are used for better efficiency. Automated tests include unit tests and integration tests. 

Code coverage fits in unit testing using pytest unittest and coverage.py and will assist one in checking code covered during unit testing. It can also be extended to integration testing and system testing applications in the browser without unit test cases.

The integration test checks the overall performance of the components of your application. In comparison, the unit test covers a smaller part, usually a particular component for testing, i.e., function.

RKTracer code coverage tool  for Python 

  • RKTracer supports all the versions of Python from 2.7 to 3.x or the latest version.
  • It supports all operating systems that Python supports: Microsoft Windows, Linux, Apple Mac OS, and Android OS.
  • RKTracer supports all the build environments.
  • It can work with various web-frameworks of Python as it supports integration testing. 
  • It is effortless to turn on and off for instrumentation.
  • It can integrate with the pytest unittest and generate code coverage of all levels that are Line coverage, Branch coverage, Statement coverage, Condition coverage, and Multi-condition coverage.
  • RKTracer can generate code coverage for testing apps in the browser without any test cases.
  • Finding unreachable code is very easy with the help of color-coding in HTML reports generated by RKTracer.

How RKTracer Tool Works?

We can generate code coverage python with just four steps:

  • Instrumentation using RKTracer. The RKTracer tool can instrument hundreds of source files within seconds. We can also instrument selected source files or source files from the selected folder.   
  • Testing Application with or without any test-cases.The test suite or you can also generate code coverage without any test cases.
  • Collect coverage data at runtime at any given point of time with or without killing the live application or while running thousands of test cases.                  
  •  Generate Html coverage Reports and XML reports for Jenkins and SonarQube.  

Generating code coverage in Python using RKTracer is very easy. Let us take two projects, “prime examples” and “Django cms,” an open-source project, to understand the process. In the first example, we will do a unit test of a prime number program. In the second example, we will do integration testing with the Django framework using the RKTracer tool.

Example 1- How to generate coverage reports for the Prime example program

We are using a prime example because it will be easy to understand the test process. To run the test, we will input a random number in the program, and it will give the output stating whether the number is a prime number or not. We will check the test’s code coverage and increase the code coverage by adding the required test conditions.

  •  Let us instrument the prime example program, using the command “rktracer -on . py -v,” and it will instrument source files.
  • Now let us run the prime project with the command “python3 main.py” and press enter. It will ask you to enter a number.
  • Let’s enter the number 5, and it will give an output that “5 is a prime number”. Now we can quit the test, and RKTracer will collect coverage data and write to the rk-coverage.txt file. RKTracer will use rk-coverage.txt data and generate the code coverage of the test run.
  • To us generate the Html code coverage report with the command “rkresults,” then it will generate a link to the HTML file.
  • As we open the file, we can see the code coverage reports. We can see the summary of different levels of code coverage in the top right corner. It shows the number of lines, statements, and multi-conditions covered, missed, or partially covered.
  • We can also check the code coverage report of different files individually. If you double-click on a particular file, it will open the program and show the coverage report by highlighting colors to interpret it better.

          Green color =Line or statement covered in the test run.

          Yellow color= Partially Line or statement covered in the test run.

          Red color= Uncovered Line or statement covered in the test run.

  • Now we know which lines or statements or conditions are covered and not covered with the test. So, we can add some more tests to increase the code coverage.
  • Let’s re-run the program. Now pass the 5,7,77 and 54465656 numbers to test and Again generate the report using the “rkresults” command.
  • We can see that the code coverage is increased. More conditions are covered as we increase the right number of tests.

So, this way, RKTracer will help you check and increase the code coverage of your project.

Example 2- Integration Test with Django Framework

This example will give you an idea of how to generate code coverage using the RKTracer tool while performing integration tests with other frameworks. We have already created the built environment and a demo file for the test.

  • Let us instrument the source files, we can instrument the file with the same command, but we have to give the folder’s path with source files that need to be instrumented. So the command will be “rktracer -on ‘The Path’ py -v.”
  • As we press enter, it will instrument all the source files within a few seconds, and it also depends upon the resources available in the build system.
  • As we can see, it has processed 830 (some of them instrumented and some left out because of exclusion of source files from instrumentation) files within 9 seconds.
  • You can also exclude individual files or do selected instrumentation with this configuration file.
  • Now run the test and launch the Django framework server.
  • Keep the application running and open another terminal and send signal “pkill -USR1 -f “python.*manage.py” to dump coverage data without killing the application. Now generate the code coverage report using RKTracer in the new terminal.
  • As you open the report, you can see the overall code coverage.
  • You can search for a particular file and check the code coverage of that particular file.
  • You can open any particular file to see the code coverage, and if you right-click, it will show you the Function Summary, File Summary, and the Project Summary.

So, this way, you can generate code coverage reports by integrating them with any other framework.

Advantages of RKTracer Code Coverage tool:

  • RKTracer is a very ‘easy to use’ tool.
  • Speedy analysis. It instruments hundreds of python source files within seconds.
  • It can generate coverage reports for all testing types, for example, with or without unit test cases.
  • Generates HTML reports with the Line, statement, branch, condition, and multiple conditions levels of code coverage.
  • Generates easy to understand coverage reports.
  • Ready to use plug-ins like Jenkins, SonarQube, and integrates with in-house CI.
  • Support team with over 20 years of experience.

Conclusion:

Code coverage and test coverage are essential to maintain the efficiency of the tests. Different programming languages use different code coverage tools, but RKTracer can make things easy to use with any programming language.

Python is a straightforward yet compelling programming language, and so is the RKTracer tool for code coverage. RKTracer can work on any operating system and any build environment, just like Python.

Using the reports of different code coverage levels generated by The RKTracer tool, the tester can quickly improve the code coverage results. It can also integrate with different frameworks and generate code coverage very effectively.

Other code coverage tools have many problems. They are not easy to use and have no back-up features. They don’t cover all the code coverage levels (Line coverage, Statement coverage, Branch coverage, Condition coverage, and Multi-condition coverage).

RKTracer provides the solution to all these problems and generates more effective and faster results. Yes, code coverage is essential for your project. But that does not mean that it should be a complicated and time-taking process. Make the code coverage an easy and effective process with RKTracer

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