cs102-F2020-lab4-starter

Table of Contents

• Objectives
• Introduction
• Continuous Learning
• Assignment Reminders
• Accessing the Assignment
• Laboratory Assignment Tasks
  • Installing Programs that Support Python Programming
  • Calculating the Mean of a List of Floating Point Numbers Input from the File System
  • Running a Pytest Test Suite to Establish a Confidence in Function Correctness
  • Reflecting on the Laboratory Assignment
  • Transferring Your Source code and Technical Writing to GitHub
• Automated Checks with GatorGrader
• Assignment Assessment
• Advance Feedback on an Assignment
• Discussion of a Graded Assignment
• Additional Resources
  • System Commands
  • Using Docker
• Commands for an Interactive Docker Shell
• Upgrading the Docker Container
  • Downloading Project Updates
  • Using GitHub Actions
  • System Requirements
  • Reporting Problems
  • Receiving Assistance

This assignment is due by 2:30 pm on Monday, October 12, 2020.

Objectives

The learning objectives for this laboratory assignment are as follows:

• To configure Git and GitHub on your laptop and on the GitHub servers
• To transfer files from your laptop to your GitHub repository
• To use a Docker container to run the automated checks performed by GatorGrader
• To install the programs that you need to support the creation of a Python program
• To use a terminal window to run a Python program and observe its output
• To use conditional logic to avoid an incorrect division by zero
• To use iteration constructs and functions to transform the type of data
• To use iteration constructs and functions to calculate the mean of a sequence of floating-point numbers
• To combine the use of iteration, conditional logic, and lists of floating-point numbers in a Python program
• To use your text editor and a terminal window to run a test suite for a Python program

Introduction

Designed for use with GitHub Classroom and GatorGrader, this repository contains a laboratory assignment for an introductory computer science class that uses the Python programming language. The source code and technical writing for this assignment must pass tests set by the GatorGrader tool. When you use the git commit and git push commands to transfer your source code to your GitHub repository, GitHub Actions will initialize a build of your assignment, checking to see if it meets all of the requirements. If both your source code and writing meet all of the established requirements, then you will see a green ✔ in the listing of commits in GitHub. If your submission does not meet the requirements, a red ❌ will appear instead. Please note that, at the option of the course instructor, some checks may be run in GitHub Actions that are not run locally by the GatorGrader tool.

Continuous Learning

If you have not done so already, please read all of the relevant GitHub Guides that explain how to use many of the features that GitHub provides. In particular, please make sure that you have read the following GitHub guides: Mastering Markdown, Hello World, and Documenting Your Projects on GitHub. Each of these guides will help you to understand how to use both GitHub and GitHub Classroom.

Students who want to learn more about how to use Docker should review the Docker Documentation. Students are also encouraged to review the documentation for their text editor, which is available in the VS Code docs. You should also review the Git documentation to learn more about how to use the Git command-line client. In addition to talking with the instructor and technical leader for your course, students are encouraged to search StackOverflow for answers to their technical questions.

As outlined in the course schedule in the course planning repository, students should also read all of the assigned readings for up to and including the week of the semester on which this laboratory assignment was assigned.

Assignment Reminders

• Follow each step carefully. Slowly read each sentence in this document, making sure that you precisely follow each instruction. Take notes about each step that you attempt, recording your questions and ideas and the challenges that you faced. If you are stuck, then please tell a technical leader or the course instructor what assignment step you recently completed.

• Regularly ask and answer questions. Please log into Slack at the start of the laboratory session and then join the appropriate channel. If you have a question about one of the steps in an assignment, then you can post it to the designated channel, discussing your questions through both Slack and the Google Meet designated for the class.

• Store your files in GitHub. Starting with this laboratory assignment, you will be responsible for storing all of your files (e.g., Python source code and Markdown-based writing) in a Git repository using GitHub Classroom. Please verify that you have saved your source code in your Git repository by using git status to ensure that everything is updated. You can see if your assignment submission meets the established correctness requirements by using the provided checking tools on your local computer and by checking the commits in GitHub.

• Keep all of your files. Don't delete your programs, output files, and written reports after you submit them through GitHub; you will need them again when you study for the course assessments and work on the other laboratory, practical, and technical challenge assignments.

• Hone your technical writing skills. Computer science assignments require to you write technical documentation and descriptions of your experiences when completing each task. Take extra care to ensure that your writing is interesting and both grammatically and technically correct, remembering that computer scientists must effectively communicate and collaborate with their team members and the student technical leaders and course instructor.

• Review the Honor Code on the syllabus. While you may discuss your assignments with others, copying source code or technical writing is a violation of Allegheny College's Honor Code.

Accessing the Assignment

To access this assignment, you should go into the #announcements channel in our Slack workspace and find the announcement that provides a link for it. Copy this link and paste it into your web browser. Now, you should accept the laboratory assignment and see that GitHub Classroom created a new GitHub repository for you to access the assignment's starting materials and to store the completed version of your assignment. Specifically, to access your new GitHub repository for this assignment, please click the green "Accept" button and then click the link that is prefaced with the label "Your assignment has been created here". If you accepted the assignment and correctly followed these steps, you should have created a GitHub repository with a name like Allegheny-Computer-Science-102-Fall-2020/computer-science-102-fall-2020-lab-4-gkapfham. Unless you provide the course instructor with documentation of the extenuating circumstances that you are facing, not accepting the assignment means that you automatically receive a failing grade for all of its components.

Before you move to the next step of this laboratory assignment, please make sure that you read all of the content on the web site for your new GitHub repository, paying close attention to the technical details about the commands that you will type and the output that your program must produce. Now you are ready to download the starting materials to your laboratory computer. Click the "Clone or download" button and, after ensuring that you have selected "Clone with SSH", please copy this command to your clipboard. To enter into your course directory directory you should now type cd cs102F2020. Next, you can type the either ls (on either MacOS or Linux) or dir (on Windows 10 Pro or Windows 10 Enterprise) and see that there are no files or directories inside of this directory. By typing git clone in your terminal and then pasting in the string that you copied from the GitHub site you will "download" all of the code for this assignment. For instance, if the course instructor ran the git clone command in the terminal, it would look like:

git clone [email protected]:Allegheny-Computer-Science-102-F2020/computer-science-102-fall-2020-lab-4-gkapfham.git

After this command finishes, you can use cd to change into the new directory. If you want to "go back" one directory from your current location, then you can type the command cd ... Finally, please continue to use the cd and ls commands to explore the files that you automatically downloaded from GitHub. If one of the aforementioned commands does not work correctly, then it is possible that your terminal window is not up-to-date or not configured correctly. In this case, please share your specific error messages with the instructor, ultimately working to master the use of terminal commands. What files and directories do you see? What do you think is their purpose? Spend some time exploring, telling your discoveries to a student technical leader.

Laboratory Assignment Tasks

Installing Programs that Support Python Programming

If you have not already done so, you need to install the Poetry tool for dependency management and packaging of Python programs. After ensuring that you have Python 3.8 installed on your laptop, please follow the installation instructions for Poetry. For instance, you are using either MacOS or Linux you need to type the following command in your terminal window curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python. Importantly, this command will only work if you have already installed a program called curl. If you are using Windows 10 Pro then you will need to follow the PowerShell installation instructions on Poetry's web site.

Now, making sure that you are in your "home base" directory for this laboratory assignment, you need to install the dependencies for the datasummarizer program that you will implement, debug, and observe. To complete this step you need to type cd datasummarizer and then poetry install. What output did this command produce? What do you think that this step did? Why is important to type these commands? Make sure that you know the answers to these question before moving onto the next step of the assignment.

Finally, it is important to note that if you are not able to correctly install Python 3.8.5 and Poetry on your laptop, you can opt to use Python and the Poetry tool in the Docker container provided by the course instructor. Please refer to the content in this assignment sheet about how to run an interactive shell in a Docker container and then use the Poetry commands mentioned in both this section and the next sections of the assignment sheet.

Calculating the Mean of a List of Floating Point Numbers Input from the File System

Please make sure that you investigate all of the Python source code files provided in the assignment repository. You should notice that there are TODO markers indicating that you need to add Python source code that will, for instance, read in data values from a file, convert textual data values to floating point numbers, and compute the arithmetic mean of the numbers. To get started, make sure that you investigate the type of data that the inputs/data.txt file contains. Here is a sample of the first ten lines of this file:

2.5169521900e+0
1.8703141360e+0
-3.4505452520e-2
2.3580068020e+0
1.5516879500e+0
-4.1540414750e-1
-3.3579431230e-1
7.5974451890e-1
8.5628077950e-1
2.5559602400e+0

Before you continue to work on this assignment, please make sure that you understand the meaning of the scientific notation in which these floating-point values are encoded. For instance, can you write 2.5169521900e+0 or 7.5974451890e-1 as a decimal value that does not use the notation of e+0 or e-1$a, respectively?

Now, the best way to understand the expected behavior of this program is to observe the output of a completed version of the program. For instance, the command poetry run python datasummarizer --data-file inputs/data.txt produces the following output:

The data file contains 100 data values in it! Let's get summarizing!
The computed mean is 0.9919614640914002!

You will know that your program is working correctly if it computes the arithmetic mean of the provided input data as 0.9919614640914002. Since the datasummarizer program takes as input textual values from an input file, you will need to implement a data transformation function that can take as input a string that contains a numerical value on each line and returns a list of floating-point values suitable for input into a mathematical computation. Here is the signature of the transform_string_to_number_list function:

def transform_string_to_number_list(data_text: str) -> List[float]

You program also needs to contain a data summarization function that can take as input a list of floating-point values and then return a single floating-point value that corresponds to the arithmetic mean of the values in the list. As you are implementing this function, please ensure that your function can handle without crashing an empty list of numerical values, returning a "not a number" (nan) designator in this situation. Here is the signature of the compute_mean function:

def compute_mean(numbers: List[float]) -> float

In summary, you must follow all of the instructions next to the TODO markers in the provided source code to implement a program that can correctly compute the arithmetic mean of the provided data values in the datasummarizer/inputs/data.txt file. In addition to ensuring that your program is adequately documented, has the correct format, and adheres to all of the industry best practices for the chosen Python source code constructs, you must implement functions that pass a provided Pytest test suite, as explained next.

Running a Pytest Test Suite to Establish a Confidence in Function Correctness

Your GitHub repository for this assignment also contains a small Pytest test suite that provides a function under test with inputs and then checks to see if it produces the expected output. You can run the four tests in the test suite by typing the following command in your terminal window: poetry run pytest. If your program is implemented correctly, then it should produce output like the following:

================================================================== test session starts ==================================================================
platform linux -- Python 3.8.5, pytest-5.4.3, py-1.9.0, pluggy-0.13.1
rootdir: /home/gkapfham/working/teaching/github-classroom/Allegheny-Computer-Science-102-F2020/solutions/cs102-F2020-lab4-solution/datasummarizer
collected 6 items

tests/test_summarize.py ....                                                                                                                      [ 66%]
tests/test_transform.py ..                                                                                                                        [100%]

=================================================================== 6 passed in 0.02s ===================================================================

If your test suite has failing test cases in it, then you need to keep working to enhance your program, ultimately ensuring that the test suites passes and that all runs of the program produce the expected output. If you are interested in doing so, please add more test cases to either the tests/test_summarize.py test suite or the tests/test_transform.py test suite so as to better ensure that the functions under test work correctly. Since it is also important to ensure that your program's source code adheres to all industry-standard best practices, you should run the command poetry run pylint datasummarizer tests, fixing any issues that it raises. Finally, you can ensure that your program's source code has an industry-standard format by running the command poetry run black datasummarizer tests.

Reflecting on the Laboratory Assignment

Once you have finished both of the previous technical tasks, use your text editor to answer all of the questions in the writing/reflection.md file. For instance, you should provide the output of the Python program in a fenced code block, explain the meaning of the source code segments, and answer all of the other questions about your experiences in completing this laboratory assignment.

Transferring Your Source code and Technical Writing to GitHub

As you are working on your laboratory assignment, please make sure that you use VSCode to regularly save your work and transfer it to the GitHub servers. For instance, please use the git commit command in your terminal window or use the similar feature in VSCode to "stage" your changes in your repository. Once you have committed your source code to your repository, you can use the git push command to transfer your work to your GitHub repository, making it available for the course instructor to assess. Please make sure that you regularly commit your source code and technical writing, using descriptive commit messages to explain how each commit changes the contents of the repository. Please do not use vacuous commit messages that do not explain how your commit changes the contents of the repository!

Automated Checks with GatorGrader

In addition to meeting all of the requirements outlined in this assignment sheet, your submission must pass the following checks that GatorGrader automatically assesses:

If GatorGrader's automated checks pass correctly, the tool will produce the output like the following in addition to returning a zero exit code (which you can access by typing the command echo $?). You will need to run GatorGrader in a Docker container by following the steps in the Using Docker section.

• The command cd datasummarizer; poetry install; poetry run python datasummarizer --data-file inputs/data.txt; cd .. executes correctly
• The file __main__.py exists in the datasummarizer/datasummarizer directory
• The file reflection.md exists in the writing directory
• The file summarize.py exists in the datasummarizer/datasummarizer directory
• The file test_summarize.py exists in the datasummarizer/tests directory
• The file test_transform.py exists in the datasummarizer/tests directory
• The file transform.py exists in the datasummarizer/datasummarizer directory
• The __main__.py in datasummarizer/datasummarizer has at least 2 multiple-line Python comment(s)
• The __main__.py in datasummarizer/datasummarizer has at least 4 single-line Python comment(s)
• The __main__.py in datasummarizer/datasummarizer has exactly 0 of the TODO fragment
• The __main__.py in datasummarizer/datasummarizer has exactly 1 of the summarize.compute_mean fragment
• The __main__.py in datasummarizer/datasummarizer has exactly 1 of the transform.transform_string_to_number_list fragment
• The reflection.md in writing has at least 1 of the code tag
• The reflection.md in writing has at least 3 of the code_block tag
• The reflection.md in writing has at least 500 word(s) in total
• The reflection.md in writing has exactly 0 of the Add Your Name Here fragment
• The reflection.md in writing has exactly 0 of the TODO fragment
• The reflection.md in writing has exactly 8 of the heading tag
• The repository has at least 5 commit(s)
• The summarize.py in datasummarizer/datasummarizer has at least 2 multiple-line Python comment(s)
• The summarize.py in datasummarizer/datasummarizer has exactly 0 of the TODO fragment
• The summarize.py in datasummarizer/datasummarizer has exactly 1 of the -> float fragment
• The summarize.py in datasummarizer/datasummarizer has exactly 1 of the from typing import List fragment
• The summarize.py in datasummarizer/datasummarizer has exactly 1 of the List[float] fragment
• The test_summarize.py in datasummarizer/tests has at least 5 multiple-line Python comment(s)
• The test_summarize.py in datasummarizer/tests has exactly 0 of the TODO fragment
• The test_transform.py in datasummarizer/tests has at least 3 multiple-line Python comment(s)
• The test_transform.py in datasummarizer/tests has exactly 0 of the TODO fragment
• The transform.py in datasummarizer/datasummarizer has at least 2 multiple-line Python comment(s)
• The transform.py in datasummarizer/datasummarizer has exactly 0 of the TODO fragment

        ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
        ┃ Passed 23/23 (100%) of checks for cs102-F2020-lab3! ┃
        ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛

Assignment Assessment

Taking inspiration from the principles of specification-based grading, the grade that a student receives on a laboratory assignment will be based on whether or not it meets the standards for technical work in the fields of software engineering and discrete structures. Instead of receiving a single numerical or letter grade for this assignment, your grade will have the following components:

• Percentage of Correct GatorGrader Checks Ranging Between 0 and 100: Your submitted Python program must pass all of GatorGrader's checks by, for instance, ensuring that it produces the correct output and has all of the required characteristics. Your technical writing must pass all of GatorGrader's checks about, for instance, the length of its output and its use of the required Markdown language features for technical writing. For this component of a laboratory assignment's grade, your work will receive a percentage, ranging from 0 to 100, that corresponds to the percentage of GatorGrader checks that automatically pass inside of a GitHub Actions build.

• GitHub Actions Build Status of Either ✔ or ❌: Since additional checks on the Python source code and/or technical writing are encoded in GitHub Action workflows and, moreover, all of the GatorGrader checks are also run in GitHub Actions, your work will receive a checkmark grade if the last before-the-deadline build in GitHub Actions passes and a ✔ appears in the GitHub commit log instead of an ❌. The build status reported by GitHub Actions will only be a ✔ if the source code and technical writing in the GitHub repository pass all of both the GatorGrader checks and the additional checks.

• Technical Writing Mastery of Either ✔ or ❌: Students will also receive a ✔ grade when the responses to the technical writing questions presented in the writing/reflection.md reveal a mastery of technical writing skills. To receive a checkmark grade, the submitted writing should have correct spelling, grammar, punctuation, and formatting in addition to following the rules of the Markdown language. Your work will receive a ✔ grade for this component if the build report from GitHub Actions reveals that there are no detected mistakes in the technical writing.

• Technical Knowledge and Skill Mastery of Either ✔ or ❌: Students will also receive a checkmark grade when the GitHub repository reveals that they have mastered all of the technical knowledge and skills developed during the completion of the laboratory assignment. As a part of this grade, the instructor will assess aspects of the project including, but not limited to, the use of effective Python source code comments, correct Git commit messages, and accurate responses to the technical writing questions.

Advance Feedback on an Assignment

Students who wish to receive feedback on their work for any course assignment should first open an issue on the issue tracker for their assignment's GitHub repository, giving an appropriate title and description for the type of feedback that you would like the course instructor to provide. After creating this issue, you will see that GitHub has created a unique web site that references it. To alert the course instructor to the fact that the issue was created and that you want feedback on your work, please send it to him by a Slack direct message at least 24 hours in advance of the project's due date. After the instructor responds to the issue, please resolve all of the stated concerns and participate in the discussion until the issue is resolved and ultimately marked as closed.

Discussion of a Graded Assignment

Students who wish to receive feedback on their work for any graded course assignment should leave question in the same region of Github where the course instructor submitted the assignment's grade. For example, if the instructor submits your grade to a pull request in your repository for a laboratory assignment, then you should ask questions about your grade in that pull request, bearing in mind the need to @-mention the course instructor in the body of your comment. Students can continue to discuss the graded assignment with the course instructor until they understand all the technical topics that were the focus of the particular assignment.

Additional Resources

System Commands

This project invites students to enter system commands into a terminal window. This assignment uses Docker to deliver programs, such as gradle and the source code and packages needed to run GatorGrader, to a students' computer, thereby eliminating the need for a programmer to install them on their development workstation. Individuals who do not want to install Docker can optionally install of the programs mentioned in the Project Requirements section of this document.

Using Docker

Once you have installed Docker Desktop, with MacOS and Linux you can use the following docker run command to start gradle grade as a containerized application, using the DockaGator Docker image available on DockerHub.

docker run --rm --name dockagator \
  -v "$(pwd)":/project \
  -v "$HOME/.dockagator":/root/.local/share \
  gatoreducator/dockagator

Please note that not all version of the Windows terminal window will correctly recognize the use of the %cd% and %HomeDrive%%HomePath% variables. In this case, you should substitute the actual directory for a specific course assignment for the %cd% variable and the drive letter that contains the .dockagator directory for the %HomeDrive%%HomePath% variable. Finally, the Windows terminal window may not work correctly when you attempt to run a multi-line command. In this case, you should break up the aforementioned four-line command into separate lines, like docker run --rm --name dockagator and -v "%cd%:/project" and then connect them into a single long line that you separate by a single space. Here is an example of what the long command would look like, again assuming that the Windows cmd terminal correctly interprets the %cd% and %HomeDrive%%HomePath% variables:

docker run -it --rm --name dockagator -v "%cd%:/project" -v "%HomeDrive%%HomePath%/.dockagator:/root/.local/share" gatoreducator/dockagator /bin/bash

Here are some additional commands that you may need to run when using Docker:

• docker info: display information about how Docker runs on your workstation
• docker images: show the Docker images installed on your workstation
• docker container list: list the active images running on your workstation
• docker system prune: remove many types of "dangling" components from your workstation
• docker image prune: remove all "dangling" docker images from your workstation
• docker container prune: remove all stopped docker containers from your workstation
• docker rmi $(docker images -q) --force: remove all docker images from your workstation

Commands for an Interactive Docker Shell

Since the above docker run command uses a Docker images that, by default, runs gradle grade and then exits the Docker container, you may want to instead run the following command so that you enter an "interactive terminal" that will allow you to repeatedly run commands within the Docker container. Don't forget that, if you are using the Windows operating system, then you will need to use a different command to run Docker, as explained previously in this document. Importantly, the command that you type if you are a Windows user should still contain the -it at the start of the docker run and the /bin/bash at the end of the command. However, the other components of this command need to be customized for the Windows operating system.

If you use either MacOS or Linux, then this is the command that you would run to enter into the interactive terminal provided by a Docker container:

docker run -it --rm --name dockagator \
  -v "$(pwd)":/project \
  -v "$HOME/.dockagator":/root/.local/share \
  gatoreducator/dockagator /bin/bash

If you use Windows, then this is the command that you would run to enter into the interactive terminal provided by a Docker container:

docker run -t --rm --name dockagator \
  -v "%cd%:/project" \
  -v "%HomeDrive%%HomePath%/.dockagator:/root/.local/share" \
  gatoreducator/dockagator /bin/bash

Once you have typed this command, you can use the GatorGrader tool in the Docker container by typing the command gradle grade in your terminal. Running this command will produce a lot of output that you should carefully inspect. If GatorGrader's output shows that there are no mistakes in a course assignment, then your source code and technical writing are passing all of the automated baseline checks. However, if the output indicates that there are mistakes, then you will need to understand what they are and then try to fix them.

Remember, to correctly run any of the commands mentioned in this guide, you must be in the main (i.e., "home base") directory for this assignment where the build.gradle file is located.

Upgrading the Docker Container

If the course instructor provides a new version of the Docker container called gatoreducator/dockagator and you want to receive it immediately, you must first delete the existing Docker container on your laptop by running the command docker rmi gatoreducator/dockagator. Next, you can re-run one of the aforementioned Docker commands, like the following one, which would work on MacOS or Linux:

docker run -it --rm --name dockagator \
  -v "$(pwd)":/project \
  -v "$HOME/.dockagator":/root/.local/share \
  gatoreducator/dockagator /bin/bash

Please note that if you attempt to run gradle grade in an updated Docker container it is possible that the command will execute incorrectly if you previously used GatorGrader with a Docker container that featured a different version of the Python programming language. In this situation, you should delete the directories inside of the .dockagator directory and then again attempt to run the gradle grade command inside of the Docker container. Specifically, you will need to delete directories in .dockagator that are normally called gatorgrader, virtualenv, and virtualenvs.

Downloading Project Updates

If GatorGrader's maintainers push updates to this sample assignment and you received it through GitHub Classroom and you would like to also receive these updates, then you can type this command in the main directory for this assignment:

git remote add download [email protected]:Allegheny-Computer-Science-102-F2020/cs102-F2020-lab3-starter/

You should only need to type this command once; running the command additional times may yield an error message but will not negatively influence the state of your Git repository. Now, you are ready to download the updates provided by the GatorGrader maintainers by typing this command:

git pull download master

This second command can be run whenever the maintainers needs to provide you with new source code for this assignment. However, please note that, if you have edited the files that we updated, running the previous command may lead to Git merge conflicts. If this happens, you may need to manually resolve them with the help of the instructor or a student technical leader. Finally, please note that the Gradle plugin for GatorGrader will automatically download the newest version of GatorGrader.

Using GitHub Actions

This assignment uses GitHub Actions to automatically run GatorGrader and additional checking programs every time you commit to your GitHub repository. The checking will start as soon as you have accepted the assignment — thus creating your own private repository — and the course instructor and/or GitHub enables GitHub Actions on it. If you do not see either a yellow ● or a green ✔ or a red ❌ in your listing of commits, then please ask the instructor to see whether or not GitHub Actions was correctly enabled.

System Requirements

This assignment was developed to work with the following software and versions:

• Docker Desktop
• Operating Systems
  • Linux
  • MacOS
  • Windows 10 Pro
  • Windows 10 Enterprise
• Programming Language Tools
  • Gradle 6.6
  • MDL 0.5.0
  • Python 3.7 or 3.8

Reporting Problems

If you have found a problem with this assignment's provided source code or documentation, then you can go to the Computer Science 102 Fall 2020 Planning Repository and raise an issue. If you have found a problem with the GatorGrader tool and the way that it checks your assignment, then you can also raise an issue in that repository. To ensure that your issue is properly resolved, please provide as many details as is possible about the problem that you experienced. Individuals who find, and use the appropriate GitHub issue tracker to correctly document, a mistake in any aspect of this assignment will receive extra credit towards their grade for the course.

Receiving Assistance

If you are having trouble completing any part of this project, then please talk with either the course instructor or a student technical leader during the course session. Alternatively, you may ask questions in the Slack workspace for this course. Finally, you can schedule a meeting during the course instructor's office hours.