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!

Code Survey¶

If you change into the source/ directory of your GitHub repository, you will see one Python file called perform-container-cloning.py. 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:

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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]")
else:
    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.

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/reflection.md 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.

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.