Deployment Intelligence
The Signals Behind Koalr's Deployment Risk Score
Every risk prediction is explained. No black box. Each signal below is grounded in peer-reviewed research on deployment failures and software defect prediction.
33
predictive signals
5
signal categories
0-100
composite risk score
How the Risk Score Is Calculated
01
Signal collection
When a PR opens, Koalr collects signals from GitHub, Jira, your coverage tool, and your incident management system.
02
ML scoring
A machine learning model weights each signal based on its predictive power and outputs a 0-100 risk score. Certain critical signals can act as hard gates.
03
Transparent explanation
The score appears on the PR with the top contributing signals highlighted — so your engineers know exactly why a PR scored high, not just that it did.
All Signals by Category
Signals are grouped by category. Within the model, each signal is weighted by its research-validated predictive power for deployment failures.
Code Complexity8 signals
Change EntropyCommits touching many unrelated subsystems increase the chance of unexpected interactions
PR Size / Lines ChangedLarge changesets are harder to review and more likely to contain defects
Cyclomatic Complexity DeltaSignificant increases in cyclomatic or cognitive complexity suggest harder-to-test code paths
Cross-Module Touch CountPRs spanning multiple services or top-level modules carry coordination risk
Comment/Code Coherence DecayDrift between inline comments and actual code behavior indicates stale documentation
Tangled Commit DetectionPRs mixing unrelated semantic concerns (bug fix + refactor + feature) that should have been separate
PR Size Outlier ScoreUnusually large change relative to the author's own historical baseline
Co-Change Graph EntropyFiles historically changed together are changed separately, suggesting an incomplete cross-cutting change
Team / Process9 signals
Author File ExpertiseAuthor has limited prior commit history for the specific files changed
Review Depth ScoreInsufficient reviewer engagement relative to the size and complexity of the change
Stale Branch AgeLong-lived branches diverge from main, accumulating context drift
CODEOWNERS CoverageChanged files lack assigned CODEOWNERS entries, reducing reviewer accountability
Commit Message QualityLow-clarity messages or hedging phrases suggest uncertainty about the change
Review LagLong elapsed time from PR open to first substantive review comment
Deployment Timing RiskPRs merged during historically higher-risk windows (weekends, late evenings)
Review Coverage DeficitA significant portion of changed files received no substantive inline review comments
Reviewer File-Familiarity DeficitAssigned reviewers have low prior experience with the specific files changed
Testing3 signals
Test Coverage DeltaCoverage percentage of changed lines drops compared to the pre-PR baseline
Test Flakiness IndexA high proportion of the test suite is flagged as flaky, degrading CI signal reliability
CI Test ReliabilityFiles touched by the PR have unreliable CI test results, undermining the quality gate
Infrastructure8 signals
DDL / Schema DetectionSQL schema changes detected in the diff — these carry outsized production risk
SLO Burn Rate at Deploy TimeError budget consumed faster than normal at the time of deployment
Concurrent Deploy CountMultiple deployments active in the same environment increase blast radius
Deploy Window RiskDeployment scheduled outside normal business hours or during low-staffing windows
Dependency FreshnessNewly introduced dependencies that are stale or involve major version bumps
Feature Flag CoverageNew code paths introduced without a feature flag guard, limiting safe rollout options
Deploy StalenessLong time since last successful deploy to this service — environment drift accumulates
Cross-Service Blast RadiusChanges to shared packages or platform utilities affect multiple downstream services — impact tier (LOW / MEDIUM / HIGH / CRITICAL) is scored from your service dependency graph
History5 signals
Author Tenure at FilesSignificant time elapsed since the author last committed to these specific files
Incident RecencyA recent production incident occurred on this service, elevating baseline risk
PR Revert Rate for AuthorAuthor's recent revert rate is elevated relative to the team average
Rollback FrequencyService has been rolled back multiple times in the recent past
Docs-to-Code RatioSignificant code changes accompanied by zero documentation updates
The Only Platform That Explains Its Predictions
Every competitor that does any form of “risk” scoring treats it as a black box. Koalr shows the exact signals that moved the score and by how much.
| Platform | Risk Prediction | Signal Explanation | Research-backed |
|---|---|---|---|
| Allstacks | Delivery Risk Agent | None — black box score | Not published |
| LinearB | None | N/A | N/A |
| Swarmia | None | N/A | N/A |
| Jellyfish | None | N/A | N/A |
| Koalr | 0-100 score per PR | Top signals ranked per PR | Peer-reviewed foundations |
Academic Foundation
- Kamei et al., “A Large-Scale Empirical Study of Just-in-Time Quality Assurance” — IEEE TSE 2013
- Microsoft Research: Code Ownership and Software Quality (Windows Vista, Eclipse, Firefox studies)
- Microsoft Research: Code Churn and Defect Density correlation research
- Google SRE Book: Embracing Risk — SLO-based deployment gating methodology
- Graph-based ML for JIT defect prediction — PMC 2023
- DORA State of DevOps 2024 — elite team deployment size benchmarks
See your next PR's risk score before it ships
Connect GitHub in 5 minutes. Risk scores appear on every new PR with full signal breakdown.