A

COBOL Conversion Test Generator

3.90

Derivation Chain

Step 1 AI COBOL conversion trend
→
Step 2 COBOL-to-modern-language automated conversion tools
→
Step 3 Automated equivalence verification test generation for converted code

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

After converting COBOL to Java/Python with AI, manually writing test cases to verify behavioral equivalence between the original and converted code takes 2-3x longer than the conversion itself. Test gaps causing production incidents at financial institutions can result in losses of tens of thousands of dollars per incident.

Solution

Input COBOL source and converted code to automatically generate test cases based on I/O boundary values and branch coverage. Provides a regression test pipeline that runs the original COBOL and converted code in parallel for comparison. Visualizes coverage reports and pinpoints discrepancies.

Target: SI company development teams (10-50 members) performing COBOL modernization projects, financial institution IT quality assurance managers
Revenue Model: Per Transaction model: ~$22 per test generation/1KLOC, Monthly Subscription ~$367/unlimited (5-member team)
Ecosystem Role: Supplier
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
5.0/5
M Market
4.0/5
R Realizability
3.0/5
V Validation
4.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (74%)

Tech Complexity
29.3/40
Data Availability
24.4/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (59/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
15.0/15
Solo Buildability
5.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Backend [medium] AI/ML [medium] Infrastructure [low]
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