.NET  

Building AI-Powered Pull Request Summarization Systems with .NET

Introduction

As development teams grow and repositories become larger, pull requests (PRs) often contain hundreds of lines of code changes spread across multiple files. Reviewing every change manually can be time-consuming, especially for engineering managers, senior developers, and DevOps teams responsible for maintaining delivery quality.

Artificial Intelligence is transforming software engineering workflows by automating repetitive tasks and improving developer productivity. One practical application is AI-powered pull request summarization, where an AI model analyzes code changes and automatically generates concise summaries, highlights risks, and explains modifications in natural language.

In this article, we'll explore how to build an AI-powered pull request summarization system using .NET, GitHub APIs, and Azure OpenAI.

Why Pull Request Summarization Matters

Modern development teams face several challenges during code reviews:

  • Large pull requests are difficult to understand quickly.

  • Reviewers may miss critical changes.

  • Engineering managers often need high-level summaries.

  • New team members require additional context.

  • Release notes creation becomes time-consuming.

An AI-powered summarization engine can automatically generate:

  • Change summaries

  • Risk assessments

  • Affected modules

  • Testing recommendations

  • Release note drafts

This significantly reduces review effort while improving collaboration.

Solution Architecture

A typical AI-powered PR summarization platform consists of the following components:

Components

  1. GitHub Repository

  2. GitHub Webhook

  3. ASP.NET Core API

  4. Azure OpenAI Service

  5. Summary Storage Layer

  6. Notification Service

Workflow

  1. Developer creates a pull request.

  2. GitHub webhook sends event data.

  3. ASP.NET Core service retrieves changed files.

  4. Code diffs are sent to Azure OpenAI.

  5. AI generates a summary.

  6. Summary is posted back to GitHub comments.

Creating the ASP.NET Core Project

Create a new Web API project.

dotnet new webapi -n PRSummarizer

Install required packages.

dotnet add package Octokit
dotnet add package Azure.AI.OpenAI

Retrieving Pull Request Data

GitHub provides APIs to access pull request information.

Example service:

public class GitHubService
{
    private readonly GitHubClient _client;

    public GitHubService(string token)
    {
        _client = new GitHubClient(
            new ProductHeaderValue("PRSummarizer"));

        _client.Credentials =
            new Credentials(token);
    }

    public async Task<IReadOnlyList<PullRequestFile>>
        GetFiles(string owner,
                 string repo,
                 int pullRequestNumber)
    {
        return await _client
            .PullRequest
            .Files(owner, repo, pullRequestNumber);
    }
}

This service retrieves modified files and code differences from GitHub.

Preparing Code Changes for AI Analysis

Raw pull request data should be transformed into a structured format before sending it to an LLM.

Example:

public string BuildPrompt(
    IReadOnlyList<PullRequestFile> files)
{
    StringBuilder builder = new();

    foreach(var file in files)
    {
        builder.AppendLine(
            $"File: {file.FileName}");

        builder.AppendLine(file.Patch);
    }

    return builder.ToString();
}

The generated prompt contains all code changes needed for summarization.

Integrating Azure OpenAI

Create a service for AI communication.

public class AISummaryService
{
    private readonly OpenAIClient _client;

    public AISummaryService(
        OpenAIClient client)
    {
        _client = client;
    }

    public async Task<string> GenerateSummary(
        string codeChanges)
    {
        var prompt = $"""
        Analyze this pull request and generate:

        - Summary
        - Key changes
        - Risks
        - Testing recommendations

        {codeChanges}
        """;

        var response =
            await _client.GetChatCompletionsAsync(
                "gpt-4o",
                new ChatCompletionsOptions
                {
                    Messages =
                    {
                        new ChatMessage(
                            ChatRole.User,
                            prompt)
                    }
                });

        return response.Value
            .Choices[0]
            .Message
            .Content;
    }
}

The AI model converts complex code changes into human-readable insights.

Example AI Output

Input:

Added JWT authentication
Updated UserController
Created RefreshToken endpoint

Generated Summary:

Summary:
Implemented secure JWT authentication.

Key Changes:
- Added authentication middleware.
- Created token refresh endpoint.
- Updated authorization handling.

Risks:
- Token expiration configuration should be validated.

Testing:
- Verify login flow.
- Test refresh token expiration.

This output provides reviewers with immediate context.

Posting Results Back to GitHub

After generating the summary, publish it automatically as a pull request comment.

await _client.Issue.Comment.Create(
    owner,
    repo,
    pullRequestNumber,
    summary);

Reviewers can see AI-generated insights directly within GitHub.

Advanced Enhancements

Production systems often include additional intelligence.

Risk Scoring

Assign risk levels:

  • Low

  • Medium

  • High

  • Critical

Based on:

  • Files changed

  • Security-sensitive modules

  • Database schema modifications

Security Analysis

Detect:

  • Authentication changes

  • Authorization updates

  • Hardcoded credentials

  • Dependency vulnerabilities

Release Note Generation

Automatically create:

New Features
Bug Fixes
Performance Improvements
Breaking Changes

This reduces release management effort.

Team Knowledge Integration

Connect AI with:

  • Internal documentation

  • Architecture diagrams

  • Coding standards

  • Previous pull requests

This enables context-aware summaries.

Best Practices

Keep Pull Requests Small

AI performs better when analyzing focused changes rather than massive code merges.

Protect Sensitive Data

Remove:

  • API keys

  • Secrets

  • Credentials

before sending content to external AI services.

Use Structured Prompts

Explicit instructions improve output quality and consistency.

Validate AI Responses

AI-generated summaries should assist reviewers, not replace human judgment.

Monitor Token Usage

Large repositories can generate significant AI costs. Implement batching and token optimization strategies.

Benefits for Engineering Teams

Organizations implementing AI-powered PR summarization typically achieve:

  • Faster code reviews

  • Improved reviewer productivity

  • Better release documentation

  • Reduced onboarding time

  • Increased engineering visibility

  • Consistent pull request quality

As repositories continue to grow, automated summarization becomes an increasingly valuable capability.

Conclusion

AI-powered pull request summarization is one of the most practical applications of Generative AI in modern software development. By combining ASP.NET Core, GitHub APIs, and Azure OpenAI, organizations can automate code review insights, improve collaboration, and accelerate software delivery.

Rather than replacing developers, these systems augment engineering teams by transforming complex code changes into concise, actionable information. As AI-assisted development continues to evolve throughout 2026, intelligent pull request summarization will become a standard capability in enterprise DevOps pipelines.

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