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Debugging Functions

Container Cloning

Project Goals

This assignment invites you to study, repair, and test a Python programs called perform-container-cloning. Specifically, it affords you to opportunity to continue to practice the task of debugging and testing a Python function that has defects inside of it. After learning more about how containers are cloned in a Python program and why this is needed when a program iterates through a collection while changing it at the same time, you will find a fix the fault in the provided source code. Once you have fixed the defect, you will document a provided test case that (i) creates an input for a function, (ii) passes that input to the function under test, (iii) captures the output of the function under test, and (iv) asserts that the captured function output equals the expected output if the function was implemented correctly. Instead of using a test automation framework to run the provided test you will run a function to complete the aforementioned steps.

Project Access

If you are a student enrolled in a Computer Science class at Allegheny College, you can access this assignment by clicking the link provided to you in Discord. Once you click this link it will create a GitHub repository that you can clone to your computer by following the general-purpose instructions in the description of the technical skills. Specifically, you will need to use the git clone command to download the project from GitHub to your computer. Now you are ready to add source code and documentation to the project so that you can learn more about how to clone a container in Python!


If you are an emerging proactive programmer who is not enrolled in a Computer Science class at Allegheny College, you can still work on this assignment! To get started, you should click the "Use this template" button in the debugging-functions-starter GitHub repository and create your own version of this project's source code. After creating your GitHub repository, you can follow all of the other steps!

Code Survey

If you change into the source/ directory of your GitHub repository, you will see one Python file called The perform-container-cloning module contains a defective function with the signature def remove_duplicates(list_one: List[Any], list_two: List[Any]) -> Tuple[List[Any], List[Any]]. Your task is to identify and fix the defects inside of this function! To aid your debugging efforts, you should use and extend the def test_remove_duplicates() -> bool function that should subject the remove_duplicates function to several tests. Although it does not use Pytest, it is possible to implement a test called test_remove_duplicates in the following fashion:

list_one = [1, 2, 3, 4]
list_two = [1, 2, 5, 6]
expected_list_one = [3, 4]
expected_list_two = [5, 6]
test_case_passed = True
(actual_list_one, actual_list_two) = remove_duplicates(list_one, list_two)
if expected_list_one == actual_list_one and expected_list_two == actual_list_two:
    print("Expected output correct for input lists: [1, 2, 3, 4] and [1, 2, 5, 6]")
    print("Expected output not correct for input lists: [1, 2, 3, 4] and [1, 2, 5, 6]")
    print(f"   actual_list_one: {actual_list_one}")
    print(f"   actual_list_two: {actual_list_two}")
    test_case_passed = False
return test_case_passed

Lines 1 and 2 of this function create two lists, called list_one and list_two, that have in common the values 1 and 2. Lines 3 and 4 of this function indicate that, if the remove_duplicates function worked correctly, then its output should be a tuple container the lists [3, 4] and [5, 6]. After making the assumption that the test case will pass on line 5, the function calls remove_duplicates and checks to see if the expected output equals the actual output returned by the function. If the expected output is correct, then line 8 displays a message indicating that is the case. Otherwise, lines 10 through 13 signal that the test did not pass and display diagnostic output to highlight this fact for the tester. Ultimately, if this test case passes correctly it will help to establish a confidence in the correctness of remove_duplicates. When the test case for the remove_duplicates function passes, then it should produce the following output:

Expected output correct for input lists: [1, 2, 3, 4] and [1, 2, 5, 6]
The test case passed!

Running Checks

Since this project does not use Poetry to manage project dependencies and virtual environments, it does not support the use of commands like poetry run task test. However, you can leverage the relevant instructions in the technical skills to enter into a Docker container and run the command gradle grade to check your work. If gradle grade shows that all checks pass, you will know that you made progress towards correctly implementing and writing about this project's program.


Did you know that GatorGrade and GatorGrader are open-source Python programs implemented by many proactive programmers? If you finish this source code survey and have extra time, please brainstorm some new features that you think these two tools should have, explain your idea by raising an issue in the relevant project's GitHub repository, and take the first step towards implementing and testing your idea. If the maintainers of these tools accept your new feature then you will have helped to improve the experience of other people who use GatorGrade and GatorGrader!

Project Reflection

Once you have finished all of the previous technical tasks, you can use a text editor to answer all of the questions in the writing/ file. Since this is a source code survey, you should provide output from running each of the provided Python programs on your own laptop and then explain how the program's source code produced that output. A specific goal for this project is to ensure that you can explain the syntax and meaning of function signatures like def remove_duplicates(list_one: List[Any], list_two: List[Any]) -> Tuple[List[Any], List[Any]]. You should also be able to discuss what defect(s) you found in the remove_duplicates function and how you fixed them.

Project Assessment

Since this project is source code survey, it is aligned with the remembering and understanding levels of Bloom's taxonomy. You can learn more about how a proactive programming expert will assess your work by examining the assessment strategy. From the start to the end of this project you may make an unlimited number of reattempts at submitting source code and technical writing that meet the project's specification.


Before you finish all of the required deliverables required by this project is worth pausing to remember that the instructor will give advance feedback to any learner who requests it through GitHub and Discord at least 24 hours before the project's due date! Seriously, did you catch that? This policy means that you can have a thorough understanding of ways to improve your project before its final assessment! To learn more about this opportunity, please read the assessment strategy for this site.

Seeking Assistance

Emerging proactive programmers who have questions about this project are invited to ask them in either the GitHub discussions forum or the Proactive Programmers Discord server. Before you ask your question, please read the advice concerning how to best participate in the Proactive Programmers community. If you find a mistake in this project, please describe it and propose a solution by creating an issue in the GitHub Issue Tracker.

Updated: 2023-04-17   Created: 2021-09-16
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