Testing Popular Python Repositories: Using GoCodeo

Introduction

Testing is a critical component of software development that ensures code stability, functionality, and reliability. Recently, we conducted an extensive testing exercise across several popular Python repositories, including Flask, YouTube-dl, Optuna, Keras, Django, Scrapy, Pipenv, and HTTPie, using Gocodeo. The AI-generated test cases covered both happy path and edge case scenarios, providing a comprehensive evaluation of the code.

In this blog, we present a detailed analysis of the results, highlighting key metrics such as code coverage, passing ratios, error rates, and the impact of AI-generated tests in uncovering potential issues in the codebase.

Overview of the Testing Process

Repositories and Tools Tested:

• Flask: A lightweight web framework for building web applications.
• YouTube-dl: A command-line tool for downloading videos from various platforms.
• Optuna: A hyperparameter optimization framework for machine learning.
• Keras: A deep learning library for building neural networks.
• Django: A high-level web framework designed for robust web applications.
• Scrapy: A framework for web scraping and data extraction.
• Pipenv: A tool for managing Python package dependencies and virtual environments.
• HTTPie: A command-line HTTP client designed for API testing and interaction.

Files Tested:

• A total of 45 files were tested across these repositories, ranging from core components to specific modules like sessions, configurations, utilities, crawlers, and backends.

Lines of Code Analyzed:

• The testing covered 14,177 lines of code, with an average of 315 lines of code per file. This highlighted a comprehensive scope that covered both standard workflows and boundary conditions within the code.‍

Key Findings from Testing

1. Overall Passing Ratio and Gocodeo's Bug Detection Capabilities :

• Out of all the test cases run, 169 tests passed, and 171 tests were flagged as error-prone by GoCodeo. 

• Assertion Mismatches: GoCodeo identified that 60% of the issues were linked to assertion mismatches, indicating that minor adjustments to the source code, such as refining conditions and validation checks, could resolve these logic inconsistencies.
• Breakdowns in Edge Scenarios: Approximately 30% of the identified issues were associated with the source code breaking under edge case scenarios. These areas require improvements in error handling to improve the code's robustness against exceptional conditions.
•  Configuration Issues: GoCodeo also detected that 10% of the challenges were connected to missing configurations, including absent settings and environment variables, which need to be addressed for smoother operation.

‍Examples of code breaking due to unhandled edge cases:

• Example 1: There is an AppConfig class defined in the source code. It tries to determine an app's filesystem path based on its module but fails when the module’s __path__ attribute is empty.
• Example 2: A function initializes a tensor with a specified data type (dtype). It works with integer types but breaks when passed a floating-point dtype (e.g., torch.float32), causing a TypeError.

These examples illustrate how insufficient handling of edge cases in the source code can lead to failures.

2. AI-Generated Test Coverage:
   1. GoCodeo’s AI-powered test generation achieved around 80% code coverage across the evaluated repositories after fixing the source code where bugs were detected. The tests were designed to cover both happy path scenarios, where the code behaves as expected, and edge case scenarios, which limit the boundaries of functionality.
   2. This comprehensive approach ensured that both typical usage patterns and potential edge cases were thoroughly tested, offering a well-rounded analysis of the code’s behavior.

3. Adaptability and Modifications Required:
   • Approximately 80% of the AI-generated test cases ran successfully without any manual intervention, showcasing the adaptability and accuracy of AI-generated tests.
   • The remaining 20% required minor manual adjustments, mainly to accommodate updates in dependencies, and address missing module imports in the test setup. 

Statistical Analysis of Test Results

1. Executing Test Cases Without Changes:
   • No Changes Required: Approximately 80% of the test cases were executed successfully without manual intervention, underscoring GoCodeo's robustness in generating accurate tests for common scenarios.
   • Changes Required: The remaining 20% of test cases required adjustments, which primarily involved developer-side refinements such as updating mocks or resolving import dependencies. While AI-generated tests provide a strong foundation, developers may need to ensure that test environments are fully compatible with the latest code changes in the source code.

2. Test Execution Time:
   • The average execution time for each test was approximately 0.9 seconds, demonstrating GoCodeo's efficiency of the test runs even when dealing with complex scenarios across large-scale codebases.

Conclusion

Testing with GoCodeo's AI-generated test cases across 45 files and 14,177 lines of code has provided valuable insights into code performance. 

By effectively addressing both happy path and edge case scenarios, GoCodeo demonstrated its ability to surface critical issues while ensuring robust test coverage. This exercise not only underscores the value of AI in enhancing test coverage but also serves as a guide for optimizing testing strategies to maintain high code quality in ever-evolving software landscapes.

Have you used AI to generate test cases for your projects? Share your experiences and insights in the comments below. Let’s discuss how AI can revolutionize the testing landscape and contribute to more resilient and reliable codebases!