Upgrade Readiness & Risk Analyzer

Stop treating upgrades like gambling. This capability quantifies upgrade risk with evidence, generates fix lists, and produces disciplined runbooks so upgrades become routine - not heroic. You know what will break, why, and what to do about it before downtime.

Designed for production. Built to reduce fear, control risk, and prove rollback readiness.

Core promise
Predictable upgrades
Risk scoring + fix lists + runbooks, backed by evidence.
Primary surface
Preflight + runbook
Block unsafe upgrades; generate disciplined execution steps.
Operator outcome
Confidence
Know what changes, verify outcomes, and rollback safely if needed.
Problem

ERPNext upgrades fail for repeatable reasons - and teams still guess

Most upgrade pain is predictable: customization drift, app pins, environment mismatches, and untested rollback. Guessing makes downtime expensive.

Risk is invisible

Teams don’t know what will break until after the upgrade, when the business is already affected.

  • No quantified drift
  • No evidence-based compatibility view
  • No fix list before downtime
App pins block progress

Third-party apps and custom code silently hold upgrades hostage with dependency constraints and removed APIs.

  • Dependency conflicts late
  • Deprecated API usage unknown
  • Blocked upgrades become security risk
Rollback is folklore

Rollback plans exist on paper, but restores aren’t tested. Under pressure, teams improvise and lose time.

  • Backups exist but restores aren’t verified
  • No evidence of readiness
  • No disciplined runbook ownership
How it solves it

Preflight, score risk, generate fix lists - then run upgrades with discipline

We analyze code + apps + environment, translate findings into risk and fix lists, and generate runbooks with verification and rollback.

Preflight checks that actually block bad upgrades

We run structured checks across apps, code, schema, and environment so you know whether an upgrade is safe - before downtime.

Included
  • Framework + app dependency compatibility matrix
  • Runtime prerequisites: Python/Node/Redis/MariaDB checks
  • Disk headroom and migration time risk estimation
  • Backup/restore verification gating (optional hard-block)
Risk scoring with evidence

We convert messy upgrade risk into an operator-grade score backed by concrete findings and file-level evidence.

Included
  • Customization drift scoring (weighted by blast radius)
  • Breaking API usage detection by target version
  • Hook/override audit: what you’ve changed and where
  • Risk summary: what breaks, why, and how to fix
Generated fix list (actionable, not vague)

Instead of “upgrade carefully,” you get a prioritized fix list mapped to risk and ownership.

Included
  • Fix list by severity: blocker / high / medium / low
  • Ownership mapping: app owner / module owner / infra owner
  • Links to evidence: file, line, method, affected flows
  • Estimated effort buckets (S/M/L) for planning
Upgrade runbooks that reduce chaos

A predictable upgrade is a runbooked upgrade. We generate disciplined steps with verification and rollback.

Included
  • Step-by-step procedure: preflight → backup → upgrade → verify
  • Verification checklist: business-critical flows
  • Known pitfalls and environment-specific notes
  • Rollback plan tied to tested restore evidence
Regression detection across upgrades

Upgrades regress quietly. We track what changed and what broke compared to the last cycle.

Included
  • New vs recurring risks (trend line across cycles)
  • App pin drift tracking and dependency churn
  • Diff of customizations since last upgrade
  • Post-upgrade evidence capture and sign-off
Signals

Upgrade safety signals the platform tracks

Operators need measurable risk and evidence - not opinions. These signals drive readiness gating and planning.

Signal
Customization drift score
Definition

How far your custom scripts, reports, patches, and overrides diverge from upstream behavior - weighted by risk (hooks, monkey patches, overrides, schema changes).

Why it matters

Most upgrade breakage is self-inflicted drift. A quantified drift score turns “we’ll see” into “here’s what will break and why.”

Example operator gate

Flag as high-risk if drift score increases > 20% since last release cycle, or if any override touches critical paths (stock, accounts, payroll).

Signal
App compatibility matrix
Definition

Compatibility checks for installed apps: version pins, required framework versions, dependency constraints, and known breaking API usage.

Why it matters

Third-party apps silently block upgrades. You need a matrix that says who is compatible, who isn’t, and what must change first.

Signal
Breaking API usage count
Definition

Count of usages of deprecated/removed APIs across custom apps and scripts (by version target) with file + line evidence.

Why it matters

A single removed API can break critical flows. Counting and listing them yields a concrete fix list before the upgrade.

Example operator gate

Alert if any critical module has > 0 breaking usages for the target major/minor version.

Signal
Patch/fixture safety
Definition

Checks whether patches are idempotent, re-runnable, and version-gated; detects patches that mutate data without guards.

Why it matters

Non-idempotent patches are upgrade landmines. They fail halfway or corrupt data on reruns during rollback/restore cycles.

Signal
Database & schema readiness
Definition

Schema health checks: missing indexes, heavy migrations risk, table size hotspots, expected migration duration estimation.

Why it matters

Upgrades fail under timeouts and long locks. Schema readiness avoids downtime surprises and migration disasters.

Signal
Environment readiness
Definition

OS + runtime prerequisites: Python/node versions, Redis/MariaDB compatibility, disk headroom, and backup/restore verification status.

Why it matters

Most “upgrade failures” are actually environment failures. Readiness prevents hard stops mid-upgrade.

Signal
Rollback readiness evidence
Definition

Whether rollback prerequisites exist: recent verified backups, tested restore, deploy artifact retention, and rollback runbook completeness.

Why it matters

Rollback isn’t a plan if it wasn’t tested. Evidence-based rollback readiness reduces fear and makes upgrades routine.

Example operator gate

Block upgrade if last verified restore test is older than 30 days or if no verified backup exists within 24 hours of the change window.

Failure modes

Common upgrade failures - and how we prevent them

These failures are predictable. The analyzer is built to surface them early and convert them into fixable work.

Failure mode

Upgrade blocked by third-party app pins

Symptom: Bench upgrade fails or refuses to proceed; dependency conflicts appear.

Root cause: Third-party apps pin Frappe/ERPNext versions or depend on removed APIs; compatibility isn’t tracked.

How we detect it
  • Compatibility matrix highlights pinned apps
  • Dependency solver conflicts surfaced preflight
  • Breaking API usage attributed to specific apps
How we fix it safely
  • Generate remediation plan: upgrade/patch/replace app
  • Isolate or disable incompatible modules temporarily (if safe)
  • Create a verified path: test branch + staging upgrade + sign-off
Failure mode

Customization drift breaks core flows

Symptom: Invoices, stock, payroll, or integrations break after upgrade.

Root cause: Overrides/hook changes depend on internal behavior that changed upstream.

How we detect it
  • Drift score flags high-risk overrides
  • Hook map shows which core modules are touched
  • Breaking API detector identifies removed internals
How we fix it safely
  • Provide fix list with exact override points and alternatives
  • Refactor unsafe monkey patches into supported extension points
  • Add verification steps and automated smoke checks for those flows
Failure mode

Migration downtime exceeds window

Symptom: Database migration runs too long; locks cause outage beyond planned window.

Root cause: Large tables, missing indexes, heavy schema changes; disk and I/O constraints.

How we detect it
  • Schema readiness hotspots (table size + index gaps)
  • Estimated migration risk and duration
  • Disk headroom and I/O health checks
How we fix it safely
  • Pre-migration fixes: indexes and cleanup tasks
  • Staged migration plan: run heavy steps off-peak (where possible)
  • Rollback plan with verified restore evidence
Technical design

Designed for evidence, not vibes

Upgrade safety requires proof: what changed, what will break, what to do, how to verify, and how to rollback.

Evidence-first analysis

Findings include concrete evidence: file paths, entry points, and affected flows. No vague warnings.

  • Hook + override map with blast radius
  • Breaking API usage with traceability
  • Compatibility matrix by app/version
Gating and guardrails

Optionally block upgrades unless prerequisites are met - including verified backups and restore evidence.

  • Readiness gating policies
  • Restore verification requirement
  • Environment prerequisites checks
Runbooked execution

Runbooks include verification and rollback steps. Upgrades become repeatable operations, not hero work.

  • Verification checklist by critical flow
  • Ownership and sign-off steps
  • Rollback procedure tied to evidence
What operators can rely on

Fix lists that survive real production complexity

The platform turns upgrade risk into a prioritized fix list with evidence and ownership. No guesswork.

Risk scoreCompatibility matrixFix list (prioritized)Verification checklistRollback planEvidence capture
Next step

Want upgrades that don’t feel like a crisis?

We’ll assess your current version, customizations, installed apps, and environment - then deliver a risk score, fix list, and a runbook you can execute with confidence.