Manual Debugging and Testing of Python Applications #

Debugging and testing are essential processes in software development to ensure the reliability and high-quality performance of the application. In this article, we will explore how to manually debug and test Python applications. We will provide step-by-step instructions and examples for better understanding.

Debugging #

Debugging is the process of identifying and fixing errors or issues in the code. In Python, you can use the built-in pdb module for debugging, which provides a debugger to step through code and inspect variables.

  1. Import the pdb module at the beginning of your Python script:

    import pdb
  2. Insert a breakpoint in the code where you want to start debugging:

    pdb.set_trace()

    This will pause the program execution and give you control to interact with the debugger.

  3. Run your Python script, and it will halt at the breakpoint.

  4. Use various commands to navigate through the code:

    • n or next - Execute the next line of code.
    • s or step - Step into the next line, entering function calls.
    • c or continue - Continue execution until the next breakpoint or the program's end.
    • l or list - Display the source code around the current line of execution.
    • p <variable_name> - Print the value of a variable.
    • q or quit - Quit the debugger and terminate the program.
  5. Analyze the variables, flow of execution, and identify any errors or unexpected behavior.

  6. Make necessary changes to fix the issues.

  7. Remove the breakpoint and import pdb statement from the final version of your code.

Testing #

Testing is crucial to verify the correctness, reliability, and performance of your Python application. In manual testing, you execute the application's functionalities and observe the output for expected behavior.

  1. Identify the different aspects or components of your application that need testing. For example, input validation, edge cases, error handling, etc.

  2. Create a set of test cases that cover all possible scenarios. Each test case should have specific input values and expected output.

  3. Implement the test cases as individual functions or methods.

  4. Execute the test cases and compare the actual output with the expected output.

  5. Manually inspect the output and validate the correctness of the application against each test case.

  6. If any discrepancies are found, debug and fix the issues following the previous section's debugging process.

  7. Document the test results, including any failures or bugs found during testing.

Google Apps Script #

Google Apps Script is a JavaScript-based platform that allows you to automate tasks and enhance the functionality of various Google services, including Google Sheets, Docs, etc. While you cannot directly debug Python code within Google Apps Script, you can write test cases and debug JavaScript code that interacts with Python applications.

Here's an example of using Google Apps Script to manually test a Python application that interacts with a Google Sheet:

  1. Open the Google Sheet that the Python application interacts with.

  2. Create a new Google Apps Script by going to Tools > Script editor in the Google Sheet's menu.

  3. Write the necessary JavaScript code to execute the Python application's functionality from the Google Sheet.

  4. Use console.log() statements in JavaScript to print any outputs or variables that need to be tested.

  5. Save the script and execute it from the Google Sheet.

  6. Inspect the JavaScript console's output in the browser's developer tools to validate the correctness of the Python application's functionality.

  7. Debug any issues by analyzing the JavaScript code and the Python application.

Use Case Examples #

  1. Debugging: Suppose you have a Python script that reads data from a file and processes it. You can use the debugging techniques explained earlier to step through the code, inspect the variables, and understand why the processed data is not as expected.

  2. Testing: Consider a Python function that calculates the factorial of a given number. You can build test cases with different input values and expected output to validate the function's correctness.

  3. Google Apps Script: Suppose you have a Python application that fetches data from a REST API and updates a Google Sheet with the retrieved data. You can manually test this functionality by mocking the API response with a JavaScript function in Google Apps Script and observing the updates in the Google Sheet.

In conclusion, manual debugging and testing are crucial for ensuring the reliability and performance of Python applications. Following the systematic approach explained here, you can effectively identify and fix errors, as well as verify the correctness of your code. Additionally, using Google Apps Script, you can test the integration of Python applications with various Google services.