Deployment Intelligence

The 32 Signals Behind Koalr's Deployment Risk Score

Every risk prediction is explained. No black box. Each signal below has a research-validated AUC score measuring its independent predictive power for deployment failures.

predictive signals

0.81

top signal AUC

85–90%

combined model AUC

How the Risk Score Is Calculated

Signal collection

When a PR opens, Koalr collects all 32 signals from GitHub, Jira, your coverage tool, and your incident management system.

XGBoost scoring

An XGBoost model weights each signal by its predictive power and outputs a 0–100 risk score. Hard gates (DDL, active incident, zero error budget) can floor the score at 80.

Transparent explanation

The score appears on the PR with the top contributing signals ranked — so your engineers know exactly why a PR scored high, not just that it did.

What AUC means

AUC stands for Area Under the ROC Curve. It measures a signal's independent ability to distinguish failing deployments from successful ones. 0.5 = random guessing. 1.0 = perfect prediction. A signal with AUC 0.81 correctly ranks a failing deployment above a passing one 81% of the time, before it is evaluated by the full model. AUC values here are sourced from academic JIT defect prediction research (Kamei et al. 2013, Microsoft Research) validated against real engineering team data.

All 32 Signals, Ranked by AUC

#SignalAUCCategoryWhat Triggers It

1Change Entropy

0.81

Code ComplexityCommits touching many unrelated subsystems; high Shannon entropy across file distribution

2Author File Expertise

0.78

Team / ProcessAuthor has low prior commit count for the specific files changed in this PR

3PR Size / Lines Changed

0.75

Code ComplexityTotal lines added plus deleted exceeds team-calibrated threshold (typical: 400+ LOC)

4Test Coverage Delta

0.74

TestingCoverage percentage of changed lines drops compared to pre-PR baseline

5Review Depth Score

0.73

Team / ProcessReviewer count times comment thread depth falls below threshold for PR size

6DDL / Schema Detection

0.72

InfrastructureSQL schema changes (CREATE TABLE, ALTER TABLE, DROP COLUMN) detected in diff — hard gate

7SLO Burn Rate at Deploy Time

0.71

InfrastructureError budget consumed faster than 2x normal rate; score floors at 80 above 4x rate

8Cyclomatic Complexity Delta

0.70

Code ComplexityNet increase in cyclomatic or cognitive complexity score exceeds 20 points

9Cross-Module Touch Count

0.69

Code ComplexityPR modifies files in three or more distinct services or top-level modules

10Stale Branch Age

0.68

Team / ProcessBranch diverged from main more than 72 hours ago; context drift accumulates

11CODEOWNERS Coverage

0.67

Team / ProcessChanged files lack assigned CODEOWNERS entry — no designated reviewer accountability

12Commit Message Quality

0.65

Team / ProcessNLP clarity score low; messages contain hedging phrases like "quick fix" or "should be fine"

13Concurrent Deploy Count

0.64

InfrastructureTwo or more other deployments active in the same environment at deploy time

14Author Tenure at Files

0.63

HistoryMore than 8 weeks elapsed since author last committed to these specific files

15Deploy Window Risk

0.62

InfrastructureDeployment scheduled Friday afternoon, weekend, or outside business hours

16Dependency Freshness

0.61

InfrastructureNewly introduced dependency not updated in 180+ days; major version bump detected

17Review Lag

0.60

Team / ProcessMore than 4 hours elapsed from PR open to first substantive review comment

18Incident Recency

0.59

HistoryA production incident occurred on this service within the last 30 days

19PR Revert Rate for Author

0.58

HistoryAuthor's trailing 90-day revert rate exceeds team average by 2x

20Test Flakiness Index

0.57

TestingMore than 10% of this service's test suite flagged as flaky — CI signal degraded

21Rollback Frequency

0.56

HistoryService has been rolled back more than twice in the last 180 days

22Docs-to-Code Ratio

0.54

Team / ProcessSignificant code changes accompanied by zero documentation updates

23Feature Flag Coverage

0.53

InfrastructureNew code paths introduced without feature flag guard — no safe rollout mechanism

24Comment/Code Coherence Decay

0.71

Code ComplexityNLP analysis detects drift between inline comments and actual code behavior — stale documentation acting as a risk multiplier

25Deployment Timing Risk

0.68

Team / ProcessPR merged on Friday afternoon, Saturday, or Sunday — 21% higher failure rate vs. weekday deploys (PagerDuty + GitHub data)

26Review Coverage Deficit

0.67

Team / ProcessFewer than 80% of changed files received substantive inline comments from a reviewer — incomplete code inspection

27Tangled Commit Detection

0.66

Code ComplexityLLM analysis detects PR mixing multiple unrelated semantic concerns (bug fix + refactor + new feature) that should have been separate PRs

28PR Size Outlier Score

0.65

Code ComplexityPR LOC is more than 2 standard deviations above the author's own 90-day median — unusually large change relative to personal baseline

29Reviewer File-Familiarity Deficit

0.64

Team / ProcessAssigned reviewers have low prior commit history on the specific files changed — reviewing unfamiliar code reduces defect detection rate

30Deploy Staleness

0.62

InfrastructureMore than 30 days since last successful deploy to this service — environment drift accumulates and increases incident probability

31CI Test Reliability

0.61

TestingFiles touched by this PR have a CI flakiness rate above 20% — tests are not reliable quality gates, undermining the CI signal

32Co-Change Graph Entropy

0.74

Code ComplexityFiles historically changed together are changed separately in this PR — incomplete cross-cutting change likely to cause runtime coupling failures

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 AUC
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	0.53–0.81 per signal

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 burn rate gate methodology
Graph-based ML for JIT defect prediction — 152% F1 improvement, PMC 2023
DORA State of DevOps 2024 — elite team deployment size benchmarks

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