The Confidence Model: How we weigh PR risk.
Pratrol transforms raw pull request data into actionable triage signals. By weighing contributor history against change complexity, we provide a structured first-pass assessment for every PR.
Primary Risk Vectors
01. Contributor Context
We analyze the author's history within the repository. Have they touched these specific files before? Are they a frequent contributor or a first-timer in a sensitive module?
02. Logic Risk (Mistral AI)
Mistral AI performs a deep semantic analysis of the diff. Unlike static analysis, it looks for logic traps, race conditions, and architectural misalignments that simple linters miss.
03. File Sensitivity
Changes to auth/, database/, or ci/ automatically trigger higher scrutiny weights in the final confidence score.
Confidence Calculation
* Weights are automatically adjusted based on repository-specific activity patterns.
Reviewer Playbook: From Tiers to Action
| Tier | Signal Indicator | Operational Action |
|---|---|---|
| HIGH | Trusted contributor profile. Low semantic risk detected in the diff. Standard change patterns. | Proceed with standard peer review. Optimized for speed. |
| MEDIUM | Mixed signals. Potential logic edge cases flagged by Mistral or sensitive file modification. | Assign a senior reviewer. Validate edge cases before merge. |
| LOW | Critical risk indicators. First-time contribution to sensitive core or logical inconsistencies detected. | Full architectural review required. Mandatory secondary sign-off. |