Configuration File for AI-Powered Code Review

You:

Let's consider a configuration file where a team could set some configurations to allow code suggestions, to allow certain types of evaluations, etc. I would like some ideas on what would be in the configuration file, where it would live, how it would be accessed and ingested by the application during runtime.

ChatGPT:

"Alright, let’s design a dynamic configuration system that empowers teams to control what the bot does—from code suggestions to evaluation types and beyond. A well-structured configuration file will make this system customizable, scalable, and developer-friendly."

Configuration File Overview

The configuration file should:

Define Bot Behavior: Control what the bot does (e.g., allow suggestions, enable risk scoring, enforce coding standards).
Be Flexible: Allow teams to customize evaluations based on project needs.
Be Secure and Accessible: Stored in a way that ensures security but can be easily accessed during runtime.

What Should Be in the Configuration File?

Here are some ideas for fields to include:

1. General Settings

enableSuggestions: Whether the bot should suggest code changes.
enableEvaluations: Toggle specific types of evaluations (e.g., maintainability, performance).
defaultRiskThreshold: The default risk score threshold for flagging PRs.
enableAutoComments: Whether the bot should automatically post comments.

2. Evaluation Settings

Code Complexity:
- maxCyclomaticComplexity: Maximum allowed cyclomatic complexity before flagging.
Test Coverage:
- minTestCoverage: Minimum required percentage for test coverage.
Security Checks:
- enableSecurityChecks: Toggle security evaluations.
- allowedDependencies: List of allowed dependencies.

3. Code Suggestions

enableSuggestions: Toggle code suggestions.
suggestionTypes: Types of suggestions the bot can provide:
- refactor: Suggest refactoring opportunities.
- tests: Suggest additional test cases.
- linting: Automatically fix linting issues.

4. Persona Customization

name: The bot’s persona name.
tone: Tone of feedback (friendly, professional, sage).
commentTemplates: Templates for specific types of comments.

Example Configuration File

{
  "general": {
    "enableSuggestions": true,
    "enableEvaluations": true,
    "defaultRiskThreshold": 7,
    "enableAutoComments": true
  },
  "evaluations": {
    "codeComplexity": {
      "maxCyclomaticComplexity": 10
    },
    "testCoverage": {
      "minTestCoverage": 80
    },
    "securityChecks": {
      "enableSecurityChecks": true,
      "allowedDependencies": ["lodash", "axios"]
    }
  },
  "suggestions": {
    "enableSuggestions": true,
    "suggestionTypes": ["refactor", "tests", "linting"]
  },
  "persona": {
    "name": "CodeGuard",
    "tone": "friendly",
    "commentTemplates": {
      "complexity": "This function exceeds the allowed cyclomatic complexity. Consider refactoring.",
      "coverage": "This file does not meet the required test coverage of {minTestCoverage}%. Please add more tests."
    }
  }
}

Where Should the Configuration File Live?

Option 1: Inside the Repository

Location: .github/bot-config.json or .github/config/bot-config.json.
Advantages:
- Version-controlled with the repository.
- Customizable for individual repositories or teams.
Use Case: Best for repository-specific configurations.

Option 2: Centralized in an Organization Repository

Location: A dedicated config-repo or org-config.json in a shared repository.
Advantages:
- Centralized control across multiple repositories.
- Ideal for large organizations managing consistent standards.
Use Case: Best for enforcing organization-wide policies.

Option 3: Encrypted Secrets or Environment Variables

Store sensitive settings (e.g., API keys, risk thresholds) as GitHub Secrets or in environment variables.
Advantages:
- Secure storage for sensitive data.
Use Case: Best for global secrets or sensitive fields.

How to Access and Ingest the Configuration During Runtime

1. Repository-Based Configuration

Fetch the file dynamically during runtime using the GitHub API.

Code Example: Fetching Config from the Repo

const { Octokit } = require('@octokit/rest');
const octokit = new Octokit({ auth: process.env.GITHUB_TOKEN });

/**
 * Fetch bot configuration from the repository.
 * @param {string} repoOwner - The repository owner.
 * @param {string} repoName - The repository name.
 * @param {string} configPath - Path to the config file in the repository.
 * @returns {Object} - Parsed configuration object.
 */
async function fetchBotConfig(repoOwner, repoName, configPath) {
  const { data } = await octokit.repos.getContent({
    owner: repoOwner,
    repo: repoName,
    path: configPath, // e.g., '.github/bot-config.json'
  });

  // Decode Base64 content and parse JSON
  const config = JSON.parse(Buffer.from(data.content, 'base64').toString('utf-8'));
  return config;
}

// Example Usage
fetchBotConfig('your-org', 'your-repo', '.github/bot-config.json').then((config) => {
  console.log('Bot Configuration:', config);
});

2. Centralized Configuration Repository

Store the configuration in a central repository and fetch it dynamically.

Code Example: Fetching from a Centralized Repo

async function fetchCentralConfig(orgRepo, configPath) {
  const [repoOwner, repoName] = orgRepo.split('/');
  return await fetchBotConfig(repoOwner, repoName, configPath);
}

// Example Usage
fetchCentralConfig('your-org/config-repo', 'bot-config.json').then((config) => {
  console.log('Central Bot Configuration:', config);
});

3. Local Fallback

If fetching the configuration fails, use a local fallback configuration baked into the code.

const defaultConfig = {
  general: { enableSuggestions: true, enableEvaluations: true },
  evaluations: { codeComplexity: { maxCyclomaticComplexity: 10 } },
  persona: { name: "CodeGuard", tone: "friendly" },
};

4. Merging Configurations

Combine repository-specific configurations with global defaults or organization-level configs.

Code Example: Merging Configurations

const merge = require('lodash.merge');

async function getEffectiveConfig(repoOwner, repoName, configPath, orgRepo) {
  const repoConfig = await fetchBotConfig(repoOwner, repoName, configPath).catch(() => ({}));
  const orgConfig = await fetchCentralConfig(orgRepo, 'bot-config.json').catch(() => ({}));
  return merge({}, orgConfig, repoConfig);
}

// Example Usage
getEffectiveConfig('your-org', 'your-repo', '.github/bot-config.json', 'your-org/config-repo').then((config) => {
  console.log('Effective Bot Configuration:', config);
});

Runtime Usage of Configuration

Once the configuration is fetched and merged, pass it dynamically into your bot’s logic.

Applying Configuration in Runtime

async function analyzePullRequest(prData, config) {
  if (config.general.enableEvaluations) {
    console.log('Evaluations enabled');
    // Perform evaluations based on config settings
    if (config.evaluations.codeComplexity) {
      console.log(`Max Complexity Allowed: ${config.evaluations.codeComplexity.maxCyclomaticComplexity}`);
    }
  }

  if (config.general.enableSuggestions && config.suggestions.enableSuggestions) {
    console.log('Suggestions enabled');
    // Perform suggestions based on config settings
  }
}

// Example Usage
getEffectiveConfig('your-org', 'your-repo', '.github/bot-config.json', 'your-org/config-repo').then((config) => {
  analyzePullRequest(prData, config);
});

Final Recommendations

Start Small: Use a .github/bot-config.json file for per-repository flexibility.
Scale Up: Add an organization-wide repository for shared settings.
Secure Secrets: Use GitHub Secrets for sensitive settings.
Dynamic Fetching: Fetch configs at runtime, merge local defaults, and make the bot adaptive.

Let me know where you’d like to focus next—this is how you make your bot truly customizable! 🚀

Keep Templates Modular:
- Separate templates by type (e.g., comments, suggestions) for better maintainability.
Version Your Action:
- Use tags or semantic versioning (e.g., v1.0.0) for the action repository.
Test Compatibility:
- Ensure new template updates don’t break compatibility with older workflows.
Provide Defaults:
- Include fallback templates within the bot for repositories that don’t use the custom action.

Let me know if you’d like to expand on any part of this process—we’re making this bot scalable, consistent, and incredibly powerful! 🚀

Configuration File Overview​

What Should Be in the Configuration File?​

1. General Settings​

2. Evaluation Settings​

3. Code Suggestions​

4. Persona Customization​

Example Configuration File​

Where Should the Configuration File Live?​

Option 1: Inside the Repository​

Option 2: Centralized in an Organization Repository​

Option 3: Encrypted Secrets or Environment Variables​

How to Access and Ingest the Configuration During Runtime​

1. Repository-Based Configuration​

Code Example: Fetching Config from the Repo​

2. Centralized Configuration Repository​

Code Example: Fetching from a Centralized Repo​

3. Local Fallback​

4. Merging Configurations​

Code Example: Merging Configurations​

Runtime Usage of Configuration​

Applying Configuration in Runtime​

Final Recommendations​