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Comprehensive Bug Fixing with Artemis Changesets

Videos: See Artemis in Action

Overview

This guide demonstrates how to use Artemis Changesets to systematically identify and repair code defects across an entire project. We'll work with the andela/buggy-python repository, which contains deliberately flawed Python code perfect for showcasing Artemis's automated debugging capabilities.

Important: To create pull requests, you'll need to fork the andela/buggy-python repository to your own GitHub account first. For your own repositories, you can work directly without forking.

What You'll Learn

  • How to set up a project for comprehensive bug analysis
  • Creating and configuring changesets for automated bug fixing
  • Understanding AI-powered code analysis and repair
  • Validating fixes with custom runners
  • Integrating fixes into your development workflow via pull requests

Prerequisites

  • Access to Artemis platform
  • GitHub repository access
  • Basic understanding of Python programming concepts

Step 1: Project Setup

For project setup Steps visit Project Setup.


Step 2: Understanding Changesets

What are Artemis Changesets?

For more information and details on Changeset visit Changset Guide.

Why Use Changesets for Bug Fixing?

The Changeset feature with the "Fix all bugs" template utilizes sophisticated AI algorithms to systematically identify and repair coding issues throughout your entire project, making it ideal for:

  • Learning environments with intentionally broken code
  • Legacy codebases requiring systematic cleanup
  • Code quality improvements across large projects
  • Educational debugging exercises

Step 3: Creating a Comprehensive Bug-Fix Changeset

Initiating the Changeset

Navigate to the Changesets section and create a new changeset specifically designed for comprehensive bug detection.

New Changeset Creation

  1. Click "New Changeset"
  2. Configure the task description

Task Configuration

In the task description field, enter the following comprehensive prompt:

Please fix all the bugs in this project.
Ensure the corrected code maintains the original intended functionality while running cleanly without errors.

Changeset Configuration

This configuration tells the Changeset Agent to systematically address every type of bug in the educational repository, making it an ideal testbed for demonstrating comprehensive automated debugging capabilities.


Step 4: AI-Powered Analysis Process

The Changeset Agent processes each Python file systematically using machine learning algorithms to:

  • Detect syntax errors and incorrect operators
  • Identify variable naming issues and undefined references
  • Analyze logic flow problems and function definitions
  • Validate import statements and cross-file dependencies
  • Process all files simultaneously for consistency

Step 5: Comprehensive Bug Detection Results

Analyzing the Results

After analysis completion, the changeset presents comprehensive solutions with detailed categorization.

Changeset Analysis results

Types of Fixes Identified

Common Fix Categories:

  • Syntax Problems: Incorrect operators, missing brackets, indentation issues
  • Variable Issues: Naming inconsistencies, undefined references, scope problems
  • Logic Flaws: Faulty conditionals, incorrect loops, function definition errors
  • Import Problems: Missing declarations, circular imports, unused imports

Example Fixes

Here are typical transformations the changeset performs:

# Before (Broken)
if my_var = 5: # Syntax error: assignment in condition
print("Value is 5")

# After (Fixed)
if my_var == 5: # Corrected to comparison operator
print("Value is 5")

Step 6: Changeset Validation and Review

Examining Proposed Changes

The changeset interface allows comprehensive review of all proposed modifications:

  1. Examine the file change summary
  2. Review specific fixes across Python files
  3. Compare before/after code samples
  4. Validate that original functionality is preserved

This transforms the repository from broken to functional while maintaining consistency and preserving original programming intent.


Step 7: Validation with Artemis Runners

Setting Up Custom Validation

Validate your fixes using Artemis's built-in validation system:

Validations Tab

  1. Navigate to the "Validations" tab
  2. Set up custom validation commands
  3. Configure the runner environment
  4. Click "Run Validation"

Validation Commands

Configure appropriate validation commands for Python:

# Example validation commands
python -m py_compile *.py # Syntax validation
python -m pytest tests/ # Run unit tests
python main.py # Execute main script

Validation Results

The validation system provides confidence that automated repairs have successfully transformed the broken codebase into working, executable Python code without introducing new issues.

Validation Results


Step 8: Branch Publishing and Pull Request Creation

Publishing Changes

Seamlessly integrate fixes back into your development workflow.

Creating the Pull Request

Pull Request Creation

  1. Click "Publish & Create Pull Request"
  2. Review the automatically generated pull request summary
  3. Verify the comprehensive change documentation

Pull Request Benefits

Artemis automatically generates:

  • Descriptive branch names that clearly communicate fixes
  • Comprehensive commit messages explaining what was resolved
  • Detailed PR summaries with before-and-after code comparisons
  • Complete documentation of all detected and resolved bugs

GitHub Integration

View your published pull request in GitHub:

GitHub Pull Request View

The pull request is now ready for team review and integration into the main codebase.


Key Benefits Recap

Comprehensive Analysis & Systematic Correction

  • Simultaneous bug detection across all files with cross-file dependency awareness
  • Consistent fix application across the codebase while preserving original functionality

Efficient Batch Processing & Validation

  • Apply multiple fixes consistently across the entire codebase
  • Built-in testing ensures fixes work correctly without introducing new issues

Seamless Workflow Integration & Educational Value

  • Automated branch publishing and pull request creation with professional documentation
  • Perfect for debugging training, code quality improvement, and systematic learning

Conclusion

Artemis Changesets revolutionize the debugging and code quality workflow by transforming what used to be hours of manual debugging into an intelligent, automated process. Whether you're:

  • A student learning to debug Python code
  • An educator teaching coding best practices
  • A development team maintaining large codebases
  • Working with legacy code requiring systematic cleanup

Artemis makes code quality effortless and educational, turning broken code into production-ready fixes in just minutes.