Bug prediction for pull requests
Bring production bug impact to near zero
IDENTIFY
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.
ANTICIPATE
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.
REVIEW
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.
UNDERSTAND
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
31.0%
PRs with modified tests
49.3%
Avg Time to fix bugs
68 days
Avg time to identify bugs
23 days
IMPROVE
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.
Leverage
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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