Monte Carlo Simulation for QA (P50–P90)
Monte Carlo is one of seven methods in the Testscope Estimator Workbench. Enable Risk Simulation to run 10,000+ trials (Beta-PERT, Triangular, Log-normal) and get P50/P80/P90 with a distribution, phase split, timeline, and cost in a shared results panel. Full Pro features with a free trial—no watered-down demo.
What is Monte Carlo?
Monte Carlo runs many randomized “what-if” scenarios to capture the full range of outcomes when inputs are uncertain. Instead of one number, you get a distribution of QA effort and percentiles (P50, P80, P90) that translate directly into risk-aware schedules and budgets.
- Captures uncertainty in requirements, environments, change, and experience.
- Produces percentiles (P50/P80/P90) from 10,000+ trials for stable results.
- Easy to communicate: “~80% chance QA completes in ≤ X hours.”
Where Monte Carlo fits in the Estimator Workbench
Unified methods
- Monte Carlo (risk) with P50/P80/P90
- Planning Poker • T-Shirt • WBS • PERT • Bottom-Up • Function Point
- All methods feed a shared results panel (phases, timeline, cost)
Trial → Pro
- Trial: full Monte Carlo and all 7 methods
- Pro: unlimited CSV/JSON/PDF exports, project history & advanced configs
- Your saved presets and share links keep working when you upgrade
Why Monte Carlo for QA
QA is variable
- Requirements evolve; defects cluster unpredictably
- Environments/integrations shift outside your control
- Experience and automation maturity are uneven
Better than a single number
- Percentiles expose risk the single number hides
- P80 gives a practical commitment level
- P50 ↔ P90 shows the confidence/speed trade-off
Inputs & Risk Modeling
Testscope blends your scope with quantified risk to simulate outcomes. Distribution choice and trial count are configurable.
Scope inputs
- Features / User stories
- Average complexity (1–5)
- Platforms / Environments
- System integrations
- Automation target (%)
- Non-functional: Performance • Security • Accessibility • Compliance
Risk sliders (1–5)
- Requirements clarity • Change volatility
- Environment stability • Team experience
Engine notes
| Setting | Options / Impact |
|---|---|
| Distribution | Beta-PERT (default), Triangular, Log-normal |
| Trials | 10,000+ for stable percentiles |
| Phase split | Preset-aware (Planning/Design/Execution/Automation/Reporting) |
| Normalization | Other methods convert to hours with P80/P90 guidance for apples-to-apples comparison |
Risk sliders combine into a multiplier on base effort; Monte Carlo adds statistical variation around that baseline.
Percentiles
- P50 (median): most likely internal planning point
- P80: recommended commitment level
- P90: conservative boundary for critical launches
The P50→P90 gap is your uncertainty band; reduce it with clearer requirements and stable environments.
Interpreting Results
Calendar & cost
- Daily capacity = testers × productive hrs/day
- Timeline (days/weeks) from P80 effort ÷ capacity
- QA cost @ blended rate
Practical guidance
- Plan to P80, track against P50
- Protect a buffer between P80 and P90
- Show phase split to align owners early
Illustrative Example: 2,000 Simulations
This on-page demo is illustrative only. The product runs 10,000+ trials and supports Beta-PERT (default), Triangular, and Log-normal.
Percentiles from the example
What it shows
- Most outcomes cluster near the median (P50)
- The right tail captures worst-case spikes (env issues, scope churn)
- P80 offers a defensible commitment level with buffer
Ready to try it with your inputs? Start the full Pro experience with a free trial.
Monte Carlo FAQ
How many trials does Testscope run?
10,000+ trials in both the free trial and Pro. You can tune trial count if needed.
Is there a separate demo?
No separate demo—just a full-featured Pro trial. Your work (presets, estimates, share links) carries forward when you upgrade.
Which distributions are supported?
Beta-PERT (default), Triangular, and Log-normal. Choose the one that best reflects your uncertainty.
How accurate are the estimates?
The math is sound; accuracy depends on honest inputs. Use P80 for commitments, and compare with other methods in the workbench for triangulation.
Why not a fixed 30% buffer?
Fixed buffers ignore project-specific risk. Monte Carlo adapts to your actual scope, environment stability, and change volatility.