codepass 0.3.5

A CLI tool to evaluate the quality of your code

Codepass

Overview

The Codepass CLI tool evaluates the maintainability of code and provides recommendations. It generates two main scores:

• a-score: A complexity assessment using a Large Language Model (LLM).
• b-score: Based on Single Level of Abstraction (SLA) principle popularized.

A-Score

The a-score leverages an LLM to assess your code's complexity. It considers various factors, with more complex code typically resulting in a higher score. Major contributors to high complexity include complicated algorithms, heavy use of third-party libraries, or advanced coding techniques like functional programming that require deep technical expertise to understand. Complicated business logic that demands specific domain knowledge also adds to the complexity.Issues like deeply nested control structures, long methods, or even inconsistent naming can contribute to a higher complexity rating.

It's also sensitive to code quality best practices—things like avoiding global variables, reducing recursion, or staying away from "magic numbers." The a-score helps you get a clearer picture of how maintainable or complicated your codebase is from a technical perspective.

The overall score for a project should ideally be below 3 to ensure the code is maintainable and easy to understand. However, some files that involve more advanced or domain-specific logic may range between 3 and 4. The default threshold for the a-score across the whole project is 3, meaning the codebase should strive to keep its complexity below this level, but you can adjust this based on your team's needs.

B-Score [BETA]

The b-score evaluates your code’s according to the Single Level of Abstraction (SLA) principle promoted by Uncle Bob’s Clean Code. The tool analyzes the abstraction levels in your code and assigns a score based on whether functions are mixing high- and low-level operations.

According to the SLA principle, each function should ideally operate at a single level of abstraction. However, having just one level per function is not always a practical or desirable goal. The b-score reflects the average number of abstraction layers per function in your codebase. A good target is to aim for around 2 abstraction layers per function, allowing for some flexibility while maintaining clarity. The default threshold for the b-score is 2, which means if your project’s average abstraction level exceeds this value, refactoring may be necessary to make the code more readable and maintainable.

CLI Configuration

The CLI uses environment variables and accepts multiple arguments and flags.

codepass your_code/**/*.c 

Environment Variable

• CODEPASS_OPEN_AI_KEY: Required for OpenAI API requests.

CLI Arguments and Flags

The CLI follows a specific convention for enabling and disabling boolean flags:

• Use -f / --flag to enable a flag.
• Use --no-f / --no-flag to disable the flag.

This applies to all boolean flags in the tool.

-a, --a-score                           # Enable A score (default: True), disable with --no-a-score
-b, --b-score                           # Enable B score (default: False), disable with --no-b-score
-ps, --print-improvement-suggestions    # Enable print of improvement suggestions (default: false), disable with --no-print-improvement-suggestions 
-at, --a-score-threshold                # Set A score threshold (default: 3.3)
-bt, --b-score-threshold                # Set B score threshold (default: 2.5)
-c,  --clear                            # Clear existing reports and re-evaluate all files (default: False)
-d, --details                           # Add details for every function into report file (default: False)
-e, --error-info                        # Add debug error message to report file (default: False)
-i, --improvement-suggestions           # Add improvement suggestion to report file (default: True)
-mc, --max-context-size                 # OpenAI model max context size (default: 32 000)
-t,  --token-rate-limit                 # OpenAI token rate limit per minute (RPM) (default: 200 000)
-m,  --model                            # OpenAI model name (default: gpt-4o-mini)

Configuration File

The tool supports a codepass.config.yaml file for configuration. It includes the same options as CLI flags and an additional option:

• ignore_files - An array of glob patterns to exclude certain files from evaluation.

Poetry Setup

The project is managed using Poetry, a dependency management and packaging tool for Python. Follow these steps to set up and work with the project.

Installing Dependencies

To install all project dependencies defined in the pyproject.toml file, run:

poetry install

This will create a virtual environment and install all the necessary packages, including development dependencies.

Activating the Virtual Environment

To use the project’s virtual environment created by Poetry, activate it with:

poetry shell

Alternatively, you can run commands within the environment without explicitly activating it:

poetry run <command>

For example, to run the CLI tool:

poetry run codepass --a-score --b-score --recommendation

Install locally

You can install CLI locally by building with poetry build and installing package from a dist/codepass-[x.y.z].tag.gz with pip install dist/codepass-[x.y.x].tar.gz where [x.y.z] - represents version of the package.

Adding New Dependencies

To add new dependencies to the project, use the following command:

poetry add <package>

For development dependencies, append --dev:

poetry add --dev <package>

Features

• Configurable thresholds: You can set thresholds for a/b-scores to fail a CI job based on code quality.
• Progressive mode: By default, the tool evaluates only new or changed files. Use the --clear flag to clear the report and re-evaluate all files.

Example Usage

Terminal Output

Analyzing files: 62
Changed files: 62
Token count: 161467
Progress 100% 

A score: 3.53
B score: 2.08
Done: 78.2s

Improvement Suggestions: 

File: astrogpt/handler/llm_reasoning/handle_main_menu.py

Suggestion: Refactor the multiple `if-elif` statements that handle different `selected_action.selected_action` values into a dictionary that maps actions to their corresponding handler functions, which will simplify the control structure and improve readability.

Refactor the `handle_main_menu` function by extracting the action handling logic into a new function, `handle_selected_action`, which takes `selected_action`, `chat`, `user`, and `session` as parameters and returns the corresponding action results. This will reduce the complexity of the main function and encapsulate the specific behaviors related to each action type.

A score is too high

Example JSON Report

{
    "file_count": 1,
    "version": "0.2.0",
    "a_score": 2.98,
    "recommendation_count": 0,
    "b_score": 1.0,
    "files": [
        {
            "file_path": "astrogpt/handler/llm_reasoning/collect_data.py",
            "hash": "e62be0853d65c71a2600c8a614566ba4",
            "line_count": 86,
            "a_score": 2.98,
            "b_score": 1.0,
        }
    ]
}