We don't just find problems. We tell you what to fix first.

Bayefix is a prescriptive modernization platform that tells engineering teams exactly which files to fix, how long it takes, and what they'll gain—without requiring full code access.

BEFORE 3,000 issues found AFTER P0 — FIX NOW PaymentService.cs · 4h P0 — FIX NOW CartValidator.ts · 3h P1 — THIS SPRINT OrderRouter.ts · 6h P1 — THIS SPRINT InventorySync.cs · 8h P2 — NEXT QUARTER LegacyAdapter.cs · 12h 5 action items

The Problem with Existing Tools

Traditional tools overwhelm you with issues but leave you guessing about what matters.

SonarQube

"You have 1,247 code smells"

Which ones matter? What to fix first?

NDepend

"Your complexity is high"

What exactly should I do about it?

CodeScene

"This file changes frequently"

So what? How much will fixing it save?

CAST Highlight

"Technical debt: $2M"

Which specific files? What order?

Bayefix is Different

"You have problems" → "Fix this file"
"High complexity detected" → "Extract these 3 methods (4h)"
"Technical debt: $500K" → "Spend 40h, save $15K/quarter"
"500 files need attention" → "Start with these 5 (today)"

The Trust Funnel: Three Stages of Analysis

Bayefix analyzes legacy systems through three progressively deeper stages. Each stage requires more data access but delivers more specific recommendations.

STAGE 1 Process Intelligence Tickets only STAGE 2 Architectural Intelligence Git metadata STAGE 3 Code Intelligence Selected files DATA ACCESS Minimal Targeted
Stage 1

Process Intelligence

Data Required: Ticket metadata only (Jira/ADO)
No code access required

What You Get:

  • Functional Hotspots — Which features have the most bugs
  • Production Escape Rate — % of bugs found in prod vs QA
  • Shadow Cost — e.g., $450K/quarter in bug-fixing time
  • Boomerang Rate — % of bugs reopened within 30 days
Stage 2

Architectural Intelligence

Data Required: Git metadata only (git log --stat)
Still no code content

What You Get:

  • Kill Zone Matrix — High churn + high coupling = risk
  • Bus Factor — Files only one person knows
  • Test Co-Evolution Index — Code changed but tests didn't
  • Bug-to-File Mapping — Which files cause which bugs
Stage 3

Code Intelligence

Data Required: Selected high-risk files only
Minimal exposure, encrypted

What You Get:

  • Complexity Analysis — Cyclomatic complexity + testability
  • Cloud-Readiness Blockers — Hardcoded IPs, local disk writes
  • Security Audit — CVEs, hardcoded secrets
  • Refactoring Roadmap — With hour estimates per fix

Why This Matters

Enterprises don't want to give source code to external vendors. Bayefix delivers 70% of value without seeing code. Start with Stage 1-2 (low friction), prove value with concrete metrics, then earn trust for Stage 3.

The Key Innovation: Ticket-to-File Mapping

Bayefix connects business symptoms (bug tickets) to technical causes (specific files). This is what enables prescriptive recommendations.

Traditional Tools

Jira says: "Checkout feature has 47 bugs this quarter"

SonarQube says: "payment.ts has high complexity"

(No connection between them)

BUSINESS SYMPTOMS CODE FILES Checkout Bugs 47 this quarter Payment Timeouts 12 this quarter Inventory Errors 8 this quarter PaymentService.cs CartValidator.ts InventorySync.cs OrderRouter.ts 80% of Checkout bugs → 3 files Fix PaymentService.cs (8h) → -40% bugs

Unique Capabilities

Kill Zone Matrix

Predicts where the next outage will come from

Safe Zone
Kill Zone
Low Risk
Refactor Later
High Coupling High Churn →

Files in the Kill Zone change frequently AND break other files when they change. Fix these first.

Bus Factor Analysis

Identifies knowledge risk

CRITICAL

PaymentGateway.cs

Last 12 months: 100% of commits by Dave

If Dave leaves, nobody can maintain this file.

Pair programming or documentation sprint (16h)

Production Escape Rate

Shows testing gaps by feature

Feature In Prod Escape
Checkout 12 of 30 40%
User Registration 2 of 25 8%
Inventory 8 of 20 40%
Reporting 1 of 15 7%

Three Pillars of Differentiation

1

Prescriptive

Not Just Diagnostic

Not: "PaymentService has high complexity"

But: "Extract calculateTax(), validateCard(), and processRefund() into separate classes (4h)"

2

Prioritized

Not Overwhelming

Tier 1 (Quick Wins) Do today — high ROI, low effort
Tier 2 (High Impact) Do this sprint — significant improvement
Tier 3 (Strategic) Plan for next quarter — architectural changes
3

Quantified

CFO-Ready

40 hours of work → 40% bug reduction
$4,500 investment → $15K/quarter savings
6 weeks to stable system

What a Bayefix Recommendation Looks Like

Every problem comes with a specific, actionable fix.

🔧

Add Retry Logic to ApiClientBase.cs

Effort 4 hours
Priority P0
Expected Result 60% reduction in timeout errors

Why:

7 timeout-related bugs in past 6 months, no retry logic

Steps:

  1. Add Polly NuGet package
  2. Implement WaitAndRetry policy (3 attempts, exponential backoff)
  3. Add circuit breaker for repeated failures
ROI: $1,500/month in prevented support tickets

How We Compare

Capability SonarQube CodeScene CAST vFunction Bayefix
Code access required Full Full Full Full (runtime) Minimal
Ticket/process analysis No No No No Yes
Git history analysis No Yes Partial No Yes
Bug-to-file correlation No No No No Yes
Hour estimates for fixes No No No No Yes
ROI calculations No No Limited No Yes
CFO-ready reports No No Yes No Yes
Works without code No No No No Yes (Stage 1-2)

From 3,000 issues to 5 action items.

Stop drowning in diagnostic reports. Get a prioritized roadmap with hour estimates and ROI projections.

Book a Demo →