Give reviewers the cheat codes

Shepherdly flags high-risk PRs, allowing you to target precise remediation tactics like thorough review, QA involvement, test coverage, feature flags, and observability.


Know the number of incoming bug reports

By measuring the delta between predicted and fixed defects, you can now track latent bugs in your releases.


Bug-Fix Aware Review

AutoReview tracks the bug fix history per file and provides summarized context when bug prone files are modified. This gives reviewers a hint to where they should focus their attention.


Track quality metrics without data entry

With our NLP classifier, we track the bug fixes that aren’t entered into your bug tracker, giving you a more complete picture of the actual bug-fixing activity occurring in the repository.

PRs with bug fixes


PRs with modified tests


Avg Time to fix bugs

68 days

Avg time to identify bugs

23 days


Map Code Hotspots to Customer Pain

Take the bias out of finding the most fragile source files. Shepherdly tells you which source files impact customers the most.


Prioritize PR reviews by risk

Make it easy for engineers to target their review time on the PRs where they can add the most value.

Find the outliers
of risk within a change

Each risk score is broken down by its independent predictors so you can hone your review & remediation steps before it’s merged.

Uncover which areas
are driving bugs

Highlight which areas are unique to each team and repository, enabling them to pull forward the most impactful outcomes supported by actual production error telemetry.

Teams can scale decision-making by incorporating this context directly into the PR flow.

Quickly Identify Hotspots

Narrow down the modules and files that are the the most buggy.

Easily integrate within your existing tech stack

Shepherdly monitors GitHub activity, bug tracking systems, and your error observability stream to produce a risk score for each pull request.

Research driven

Derived from academic and company research spanning thousands
of papers from Computer Scientists and practitioners.

Code Reviews Do Not Find Bugs

Better Effort-Aware Just-ln- Time Defect Prediction

Implementations Through a Developer-informed Oracle

Customer spotlight

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