Introduction
As software organizations grow, repositories become larger, teams become more distributed, and application architectures become increasingly complex. In many enterprises, thousands of source files are spread across multiple repositories, microservices, and development teams. When a bug occurs, a security issue is discovered, or a feature requires modification, developers often face a common challenge:
Who owns this code?
While tools like CODEOWNERS files and repository permissions provide some guidance, they are often incomplete, outdated, or difficult to maintain. As engineering organizations scale, manually tracking code ownership becomes increasingly difficult.
Artificial Intelligence offers a smarter approach. By analyzing commit history, pull requests, code contributions, repository activity, documentation, and team structures, AI can automatically identify the most likely owners of a codebase, service, or feature.
In this article, we'll explore how to build an intelligent code ownership discovery system using .NET, GitHub APIs, and Azure OpenAI.
Why Code Ownership Matters
Code ownership plays a critical role in software delivery.
When ownership information is unavailable, teams experience:
Slower bug resolution
Delayed code reviews
Inefficient incident response
Knowledge silos
Increased onboarding time
Poor collaboration between teams
Consider the following scenario:
A production issue is detected in the payment service. The original developers have changed teams, and the documentation is outdated.
Without clear ownership, engineers may spend hours identifying the correct people to contact.
An AI-powered ownership discovery platform can solve this problem automatically.
Understanding Intelligent Ownership Discovery
Traditional ownership systems rely on static rules.
Example:
PaymentService/*
Team-Payments
While useful, these rules fail when:
Teams change
Repositories evolve
Developers move roles
Services become shared
AI systems analyze multiple signals to determine ownership dynamically.
These signals may include:
The result is a much more accurate ownership model.
Solution Architecture
A modern ownership discovery platform consists of the following layers:
Data Collection Layer
Collect information from:
GitHub
Azure DevOps
Jira
Internal Wikis
Incident Platforms
Analysis Layer
An ASP.NET Core service processes contribution data.
AI Layer
Azure OpenAI analyzes contribution patterns and ownership relationships.
Recommendation Layer
Ownership recommendations are delivered through APIs, dashboards, or developer tools.
Creating the ASP.NET Core Project
Create a new Web API project.
dotnet new webapi -n OwnershipDiscovery
Install required packages.
dotnet add package Octokit
dotnet add package Azure.AI.OpenAI
These packages provide GitHub integration and AI capabilities.
Collecting Repository Contribution Data
GitHub APIs provide valuable ownership signals.
Create a contributor model.
public class ContributorInfo
{
public string UserName { get; set; }
public int CommitCount { get; set; }
public int PullRequests { get; set; }
public int Reviews { get; set; }
}
This information forms the foundation of ownership analysis.
Retrieving Commit History
Commit activity is one of the strongest ownership indicators.
Example service:
public class GitHubRepositoryService
{
private readonly GitHubClient _client;
public GitHubRepositoryService(string token)
{
_client = new GitHubClient(
new ProductHeaderValue(
"OwnershipDiscovery"));
_client.Credentials =
new Credentials(token);
}
}
The service retrieves repository contributors and commit history.
Example ownership signal:
PaymentService.cs
Contributor A:
450 commits
Contributor B:
25 commits
Contributor C:
12 commits
Contributor A is likely the primary owner.
Analyzing Pull Request Activity
Commits alone do not tell the entire story.
Developers frequently review and approve changes without making commits.
Example model:
public class PullRequestActivity
{
public string UserName { get; set; }
public int ReviewsCompleted { get; set; }
public int ApprovalsGiven { get; set; }
}
Review activity often identifies technical leaders and subject matter experts.
Creating Ownership Profiles
Ownership recommendations should combine multiple signals.
Example profile:
public class OwnershipProfile
{
public string Developer { get; set; }
public int Commits { get; set; }
public int Reviews { get; set; }
public int Deployments { get; set; }
public int IncidentsHandled { get; set; }
}
This provides a richer understanding of ownership.
Integrating Azure OpenAI
The AI model evaluates repository activity and generates ownership recommendations.
Example service:
public class OwnershipAIService
{
private readonly OpenAIClient _client;
public OwnershipAIService(
OpenAIClient client)
{
_client = client;
}
public async Task<string> AnalyzeAsync(
string ownershipData)
{
var prompt = $"""
Analyze repository activity.
Determine:
1. Primary owner
2. Secondary owner
3. Subject matter experts
4. Ownership confidence
Data:
{ownershipData}
""";
var response =
await _client.GetChatCompletionsAsync(
"gpt-4o",
new ChatCompletionsOptions
{
Messages =
{
new ChatMessage(
ChatRole.User,
prompt)
}
});
return response.Value
.Choices[0]
.Message
.Content;
}
}
The model converts contribution data into meaningful ownership insights.
Example AI Output
Input:
Repository:
PaymentService
Developer A:
420 commits
85 reviews
Developer B:
140 commits
55 reviews
Developer C:
25 commits
10 reviews
Generated recommendation:
Primary Owner:
Developer A
Secondary Owner:
Developer B
Ownership Confidence:
94%
Reason:
Developer A consistently contributes,
reviews, and maintains deployment activities.
This information can be used throughout the engineering organization.
Building Ownership Confidence Scores
Ownership recommendations should include confidence levels.
Example:
Primary Owner:
Developer A
Confidence:
92%
Supporting Signals:
- Highest commit count
- Most reviews completed
- Frequent deployment activity
Confidence scoring helps teams evaluate recommendation quality.
Discovering Service Ownership
Microservice environments often lack clear ownership documentation.
AI systems can analyze:
Example output:
Service:
Order Processing
Owner:
Platform Team
Secondary Owner:
Commerce Team
This enables faster incident routing and operational response.
Integrating Incident Data
Production incidents provide valuable ownership insights.
Example model:
public class IncidentActivity
{
public string ServiceName { get; set; }
public string Responder { get; set; }
public int IncidentCount { get; set; }
}
Developers who repeatedly resolve incidents often possess deep service knowledge.
Advanced Enterprise Features
Large organizations often extend ownership discovery with additional capabilities.
Knowledge Graph Analysis
Create relationships between:
Developers
Services
Repositories
Features
Documentation
This enables deeper ownership analysis.
Team Ownership Detection
Instead of identifying individuals, determine responsible teams.
Example:
Inventory Service
Primary Team:
Platform Engineering
Confidence:
96%
Documentation Correlation
Analyze:
Internal Wikis
Runbooks
Architecture documents
to improve ownership accuracy.
Automated Review Assignment
Automatically route pull requests to likely owners.
Benefits include:
Faster reviews
Reduced delays
Improved accountability
Best Practices
Use Multiple Ownership Signals
Avoid relying solely on commit counts.
Combine:
for more accurate results.
Continuously Refresh Ownership Data
Engineering teams evolve frequently.
Schedule regular ownership recalculations.
Preserve Historical Ownership
Store ownership history to understand organizational changes over time.
Validate Recommendations
Allow teams to confirm or override AI-generated ownership assignments.
Integrate with Developer Workflows
Expose ownership recommendations within:
Pull requests
Internal portals
Developer dashboards
This maximizes adoption and value.
Benefits of AI-Powered Ownership Discovery
Organizations implementing intelligent ownership systems often achieve:
Faster incident resolution
Improved code review efficiency
Reduced onboarding time
Better engineering collaboration
Stronger accountability
Increased operational reliability
Instead of searching for the right person, teams can immediately identify who is most knowledgeable about a specific component.
Conclusion
As software systems continue to grow in size and complexity, maintaining accurate ownership information becomes increasingly challenging. Static ownership models often fail to keep pace with organizational changes, resulting in slower troubleshooting, delayed reviews, and operational inefficiencies.
By combining .NET, GitHub APIs, contribution analytics, and Azure OpenAI, organizations can build intelligent code ownership discovery systems that automatically identify responsible developers, teams, and subject matter experts. These AI-powered platforms help engineering teams move faster, improve collaboration, and maintain clear accountability across modern software ecosystems.