README

Purpose of this repository

This repository stores codes written in Python 2.7.15 to manage a doctor surgery, providing the ability for patients to book/cancel an appointment or order a prescription, and for doctors to check their schedule or issue a prescription.

Environment

These codes were developed via the industry-grade PyCharm IDE (2021.2.4), which offers more capabilities than Codio to write productionised software in Python; nevertheless, the Codio environment was replicated and used in PyCharm. Full instructions to install the required dependencies and run the software are provided below.

To set up

• Install dependencies

Please install the required dependencies by running:

• either sudo pip install -r requirements.txt if on Codio (Linux VM)
• or pip install -r requirements.txt otherwise.

To run the software

To use the codes, please run the app.py file at doctor_surgery/src/doctor_surgery via python app.py on a Python console. Thereafter, follow the prompts on the console, type the relevant option and press 'Enter' to execute each step.

Design implementation

The main doctor_surgery directory follows a package structure, with an __init__.py file for every folder. Moreover, it facilitates testing, as the tests folder follows its structure.

The app.py file has the following wrapper functions that enables a user to consume the 'doctor surgery' application:

• landing_options, which enables to run the entire application, allowing a user to register a new patient, to log in as an existing patient or doctor;

• patient_selections that allows patients to check and order prescriptions, and to schedule or cancel appointments;

• doctor_selections, which enables doctors to view their schedule or issue prescriptions.

The business logic was implemented by adhering to Object-Oriented Programming (OOP)-related principles, thus being modular and decoupled into files that contain classes and their methods, under the doctor_surgery/src/doctor_surgery directory. Furthermore, constants.py and utils.py files were created to host fixed values and helper/utility-type of functions respectively, to enable re-usability and facilitate maintainability.

The instantiation of the objects adheres to the OOP-oriented design that the tutor provided, specified at the file system_design/uml_diagram.png. Salient elements of the application, i.e., the patients, doctors, prescriptions, and appointments, were built and leveraged as lists of dictionaries, as they are data structures that enable an efficient search and update of their values or objects based on a relevant key.

Testing approach

Functional testing was carried out during and after development to ensure the correct functionality of the application, as per the evidence at test_evidence/functional_testing_on_console.

Positive and negative test cases were created when relevant and unit testing was performed to notice and fix bugs proactively, and to improve the robustness of the software design.

To run the tests

All tests can be executed by running pytest tests. To visualise the full coverage report, run pytest --cov=doctor_surgery tests --cov-report term-missing.

Full (100%) test coverage was achieved as illustrated below.

Report with full test coverage

Limitations and future work

Having to code in Python 2.7.15 as per the Codio environment was a limiting factor, since Python 3.X has more functionalities, including f-strings and type hints on the function's definition, which would have accelerated development.

Using Python 2.7.15, the Python library sqlite3 could not be installed; thus, the main limitation of the proposed implementation lies in not persisting the data that are entered by the user after the application is closed, although data persistence was not a requirement of this OOP-related Python-focused assignment.

Moreover, as a future work, a GUI could be developed, e.g., via the Python library tkinter, for a better user experience.

References

Lian, V., Varoy, E., & Giacaman, N. (2022). Learning Object-Oriented Programming Concepts Through Visual Analogies. IEEE Transactions on Learning Technologies.

Lutz, M. (2010). Programming python: powerful object-oriented programming. " O'Reilly Media, Inc.".

Phillips, D. (2015). Python 3 object-oriented programming. Packt Publishing Ltd.