Workshop: Implementing Loan Audit Log With Copilot

by Alex Johnson 51 views

In this workshop, we will explore how to design and implement an audit log for loan operations using GitHub Copilot Chat. This initiative aims to enhance our understanding of utilizing Copilot for various stages of software development, from initial analysis and design to issue creation and code implementation. The primary goal is not just to write code, but to leverage Copilot to its full potential in exploring requirements, proposing technical designs, breaking down work into manageable issues, and implementing solutions with AI assistance. This article will guide you through the process, highlighting key steps and technical considerations.

High-Level Goal: Audit Log for Loan Operations

The main objective is to add an "audit log" feature that diligently records crucial actions performed on loans. These actions include loan creation, status changes (such as approval or rejection), and auto-decision events. The audit log should be designed to be easily queryable, facilitating display in the user interface and aiding in debugging efforts. Simplicity is key, ensuring the log remains straightforward and efficient.

Key Actions to Log

  • Loan Creation: Recording when a new loan application is initiated.
  • Status Changes: Capturing changes in loan status, whether approved, rejected, or pending.
  • Auto-Decision Events: Logging instances where automated systems make decisions regarding loan applications.

Querying and Display

The audit log should be easily searchable, allowing for quick retrieval of specific events. This functionality is crucial for:

  • User Interface Display: Presenting a clear history of actions related to a loan within the UI.
  • Debugging: Providing detailed information to diagnose and resolve issues.

Simplicity in Design

The audit log must be straightforward to maintain and understand. This simplicity ensures:

  • Ease of Use: Developers can quickly grasp the log's structure and usage.
  • Maintainability: The log can be easily updated and extended as needed.

Workshop Steps: A Practical Guide

This workshop is structured into several steps, each designed to maximize your learning and practical application of Copilot. By following these steps, you will gain a comprehensive understanding of how to use Copilot to enhance your development workflow.

1. Explore the Current Repository Using Copilot Chat

Start by familiarizing yourself with the existing codebase. Use Copilot Chat to gain insights into the current system, focusing on the loan service and related functionalities. This step is crucial for understanding the context in which the audit log will be implemented.

Key Tasks

  • Explain Loan Service: Ask Copilot to explain how the loanService functions. This will give you a high-level overview of the service's architecture and key components.
  • Identify State-Changing Functions: Inquire which functions modify the state of a loan and when these functions are invoked. Understanding these state transitions is vital for determining what events to log.

To effectively utilize Copilot, you can phrase your queries as follows:

  • "How does the loanService work in this repository?"
  • "Which functions change the state of a loan, and under what conditions are they called?"

By exploring the repository, you'll gain a deeper understanding of the existing codebase and identify critical areas for audit log implementation.

2. Use Copilot Chat to Propose an Audit Log Design

Once you have a solid understanding of the codebase, the next step is to design the audit log. Utilize Copilot Chat to brainstorm and propose a design that meets the requirements while adhering to the technical constraints. This stage involves defining the structure of audit log entries, determining the storage mechanism, and ensuring the design aligns with the existing application architecture.

Key Design Considerations

  • Information to Include: Determine what data each audit log entry should contain. Essential information might include a timestamp, the type of action performed, the loan ID, and the previous and new status of the loan.
  • Storage Options: Decide where to store the audit log entries. Options include in-memory storage, local storage, or a separate module. Each option has its trade-offs in terms of performance, persistence, and complexity.
  • Implementation Simplicity: Ensure the implementation remains simple and consistent with the existing application. This will make the audit log easier to maintain and understand.

To guide Copilot in this process, you can ask questions like:

  • "What information should an audit log entry contain? Consider factors like timestamp, action type, loan ID, and status changes."
  • "Where should we store these audit log entries? Should we use in-memory storage, local storage, or a separate module?"
  • "How can we keep the implementation simple and consistent with the current application architecture?"

By leveraging Copilot's suggestions, you can create a robust and efficient audit log design tailored to your specific needs.

3. Use Copilot Chat to Generate a List of Sub-Issues

With a design in place, the next step is to break down the implementation into smaller, manageable tasks. Use Copilot Chat to generate a list of sub-issues, each representing a specific piece of work required to implement the audit log feature. This approach facilitates incremental development and makes it easier to track progress.

Suggested Sub-Issues

Copilot can help you identify the sub-issues by asking it to suggest concrete, small issues needed to implement the audit log feature. Examples of sub-issues include:

  • Create Audit Log Data Model and Storage: Define the structure of the audit log entries and set up the storage mechanism.
  • Record Loan Creation Events: Implement the logic to log the creation of new loans.
  • Record Status Changes: Capture changes in loan status, such as approvals, rejections, and auto-decisions.
  • Display Audit Log Entries in the UI: Develop the user interface components to display the audit log.
  • Add Tests for Audit Log Behavior: Write unit tests to ensure the audit log functions correctly.

To prompt Copilot, use queries such as:

  • "Suggest a set of concrete, small issues needed to implement the audit log feature."
  • "What sub-tasks are involved in creating the audit log data model and storage?"
  • "How can we ensure we capture all relevant loan status changes in the audit log?"

4. Refine and Atomize the Issues

Once Copilot has generated a list of sub-issues, it's crucial to review and refine them. This step involves ensuring each issue is small, focused, and has clear acceptance criteria. By atomizing the issues, you make them easier to manage and implement. Each issue should be achievable within a short timeframe, promoting steady progress and reducing the risk of overwhelming developers.

Refining Sub-Issues

  • Clarity and Action-Oriented Titles: Ensure each issue has a clear, concise title that accurately reflects the task at hand. Use action verbs to make the title more actionable (e.g., "Implement Loan Creation Logging" instead of "Loan Creation Logging").
  • Scope and Size: Verify that each issue is small enough to be completed in a reasonable amount of time. If an issue seems too large, break it down further into smaller sub-tasks.
  • Acceptance Criteria: Define clear and simple acceptance criteria for each issue. These criteria specify what needs to be done for the issue to be considered complete. For example, for the "Implement Loan Creation Logging" issue, the acceptance criteria might be "A log entry is created in the audit log whenever a new loan is created."

Steps for Refinement

  1. Edit and Rewrite Suggestions: Modify Copilot's suggestions to create clear, actionable titles.
  2. Keep Scope Small: Ensure each issue focuses on a specific task and is achievable in a short period.
  3. Include Acceptance Criteria: Add simple acceptance criteria to each issue, providing a clear definition of done.
  4. Create Issues in GitHub: Once you're satisfied with the refined issues, create them in your GitHub repository, assigning them to the appropriate team members.

5. (Optional) Implement the Audit Log Using Copilot

After creating the sub-issues, the next step is to implement them. This is where Copilot can be particularly helpful in generating code, suggesting solutions, and writing tests. While Copilot can significantly speed up the implementation process, it's crucial to review all changes manually to ensure code quality and correctness. This hands-on approach allows you to catch any potential issues and maintain control over the codebase.

Leveraging Copilot for Implementation

  • Code Generation: Use Copilot to generate code snippets based on the issue requirements. You can provide natural language instructions, and Copilot will suggest code implementations.
  • Test Creation: Copilot can assist in writing unit tests for the new functionality. This helps ensure the audit log works as expected and that any changes don't introduce regressions.
  • Pull Request (PR) Descriptions and Summaries: Utilize Copilot to generate descriptions and summaries for your pull requests. This saves time and ensures your PRs are well-documented.

Implementation Best Practices

  1. AI-Assisted Implementation: Use Copilot to generate code, but always review the suggestions thoroughly.
  2. Manual Review: Ensure all changes are manually reviewed to maintain code quality and catch potential issues.
  3. Test Coverage: Write comprehensive tests to ensure the audit log functions correctly.
  4. Document Changes: Use Copilot to help generate PR descriptions and summaries, making it easier for others to understand the changes.

Technical Constraints: Guiding Principles

To ensure the audit log implementation remains manageable and aligned with the existing system, certain technical constraints must be adhered to. These constraints act as guiding principles throughout the workshop and implementation process. By following these guidelines, we can ensure a cohesive and maintainable solution.

1. Adhere to Existing Architecture

  • No External Backend: The audit log should be implemented within the existing application architecture without introducing external dependencies.
  • No External Database: Avoid using an external database. Instead, leverage in-memory data structures or local storage for persistence.

2. Simplicity in Design and Code

  • Data Structure: Keep the audit log data structure simple and easy to understand. This will make it easier to query and maintain.
  • Code Clarity: Write clear, concise code that is easy to read and modify. Avoid unnecessary complexity.

3. Prefer Pure Functions

  • Audit Log Logic: Use pure functions for audit log logic wherever possible. Pure functions are easier to test and reason about, as they have no side effects.

Acceptance Criteria for the Workshop

The success of this workshop is measured by how effectively participants use Copilot to understand, design, and plan the implementation of the audit log feature. Specific acceptance criteria have been established to ensure the workshop achieves its objectives.

1. Copilot Chat Utilization

  • Code Exploration: Participants should effectively use Copilot Chat to explore and understand the current codebase.
  • Design Proposal: Participants should leverage Copilot to propose a data model and design for the audit log.
  • Sub-Issue Generation: Participants should generate an initial set of sub-issues using Copilot Chat.

2. Refined Sub-Issues

  • Clear Titles: Each sub-issue should have a clear, concise, and action-oriented title.
  • Focused Scope: The scope of each sub-issue should be small and focused, ensuring it can be completed within a reasonable timeframe.
  • Basic Acceptance Criteria: Each sub-issue should have basic acceptance criteria defined, providing a clear understanding of what needs to be done.

3. Optional Implementation

  • AI-Assisted Implementation: If time allows, participants should implement at least one audit log-related issue using AI-assisted workflows.
  • PR Creation: A pull request should be created for the implemented issue, using AI to generate descriptions and summaries.

Conclusion

This workshop provides a comprehensive guide to implementing an audit log for loan operations using GitHub Copilot Chat. By following the steps outlined in this article, you'll gain valuable experience in leveraging AI to enhance your development workflow. From exploring the codebase and designing the audit log to generating sub-issues and implementing code, Copilot can be a powerful tool in your development arsenal. Remember, the key is to use Copilot as an assistant, always reviewing its suggestions to ensure code quality and correctness.

For further reading on best practices in software development and audit logging, check out this OWASP resource.