Test Effort Estimation: How to Calculate Testing Time
A practical, step-by-step method to estimate QA effort with confidence—covering WBS, Three-Point/PERT, capacity planning, buffers, and examples you can reuse. Now with TestScope Pro shortcuts and templates.
How long will testing take? The honest answer is: it depends—on scope, risk, data, environments, and the people available to do the work. The good news: you can turn uncertainty into a defensible range using a repeatable process. This guide walks you through a simple, proven workflow to calculate testing time that stakeholders can trust.
New to estimation techniques? Start with our pillar article, Test Estimation Techniques: Complete Guide (With Examples & Tools) , then come back here to apply the math.
The 6-Step Process (Overview)
- Create a WBS that lists all testing tasks (functional, non-functional, data, env, triage, reporting).
- Estimate each task with Optimistic (O), Most Likely (M), and Pessimistic (P) times, then compute a PERT average.
- Roll up hours across tasks; convert to calendar time using team capacity.
- Add buffers as confidence levels (P50 vs P80) instead of padding numbers.
- Include hidden work: regression, defect cycles, meetings, environment/data prep.
- Publish a range with assumptions/risks and re-estimate when scope changes.
In TestScope Pro: This flow is a guided wizard—import scope → estimate O/M/P → PERT rollup → capacity calendar → choose P50 vs P80 → export deck.
Step 1 — Build a WBS (Work Breakdown Structure)
Break the effort into 4–16 hour tasks. The granularity is important: too coarse and you’ll hide risk; too fine and you’ll drown in admin.
| Area | Typical Tasks |
|---|---|
| Planning & Strategy | Scope review, risk analysis, test plan, exit criteria |
| Test Design | Cases/charters, boundary/negative, API contracts, data design |
| Environment & Data | Provisioning, parity checks, anonymization/seed, test accounts |
| Execution | Functional UI/API, integration, exploratory sessions |
| Non-Functional | Performance (baseline/stress/soak), security scans, accessibility |
| Defect Triage | Repro, isolation, verification, retests, status meetings |
| Regression & Sign-Off | Regression passes, automation maintenance, release notes |
| Reporting | Dashboards, daily/weekly summaries, go/no-go deck |
Tip: Label dependencies (“needs staging data,” “waiting on API key”) directly on tasks so schedule risks are visible.
Step 2 — Add Three-Point/PERT Estimates
For each task, capture three numbers in hours:
- O — Optimistic: best case with no blockers
- M — Most Likely: typical outcome
- P — Pessimistic: realistic worst case (not catastrophe)
Compute a weighted average with PERT:
PERT = (O + 4M + P) / 6
Mini Example (single task)
| Task | O | M | P | PERT |
|---|---|---|---|---|
| Test design (Checkout) | 6 | 10 | 16 | (6+4×10+16)/6 = 10.7 |
Repeat for each WBS task. Sum the PERT column for your total effort hours. If you want a deeper dive into choosing techniques (PERT vs others), see our complete estimation guide .
Step 3 — Convert Hours to Calendar Time (Capacity Planning)
Total hours mean little without capacity. Estimate realistic focus hours per tester per week (usually 20–32 after meetings, context switches, and PTO).
Capacity Formula
Team Weekly Capacity (hours) = Testers × Focus Hours/Week
Example: 3 testers × 30 h/wk = 90 h/wk
Calendar Conversion
Duration (weeks) = Total PERT Hours / Team Weekly Capacity
Example: 215 h / 90 h/wk ≈ 2.4 weeks
Parallelization matters. If mobile and API can run in parallel with different people, show streams separately and then take the max.
Step 4 — Add Buffers the Right Way (Confidence Levels)
Don’t hide “padding.” Express uncertainty as confidence levels. A simple approach:
- P50: Sum of PERT means; “on time half the time.”
- P80: P50 plus contingency for high-variance tasks (often +10–20%).
- P90: Conservative plan for critical launches (P50 + 20–35%).
Stakeholder conversation: “P50 is 2.4 weeks with 3 testers; P80 is 2.9 weeks. Which confidence level do you prefer for this release?”
Step 5 — Don’t Forget Regression, Defects & Meetings
Regression
Always include at least one full pass of critical regression plus automation maintenance. New features often increase regression scope.
Defect Cycles
Budget time for repro, isolation, verification, and retests. Defect arrival is lumpy; triage cadence reduces thrash.
Meetings/Reporting
Stand-ups, triage, stakeholders, and status reporting typically consume 10–20% of QA focus time.
Step 6 — Worked Examples
A) Web App Release (WBS + PERT)
| Task | O | M | P | PERT |
|---|---|---|---|---|
| Plan & strategy | 6 | 10 | 16 | 10.7 |
| Test design (cart/checkout/API) | 24 | 36 | 60 | 38 |
| Functional execution | 60 | 90 | 135 | 92.5 |
| Non-functional (perf/a11y) | 10 | 18 | 30 | 18.7 |
| Triage & verification | 20 | 30 | 45 | 30.8 |
| Regression & sign-off | 16 | 24 | 36 | 24.7 |
| Total (PERT) | 215.4 h | |||
Calendar: With 3 testers at 30 focus h/wk → 215.4/90 ≈ 2.4 weeks (P50). P80 (+20%) ≈ 2.9 weeks.
B) Mobile Feature (Risk-Weighted)
| Module | Baseline | Risk | Factor | Adjusted |
|---|---|---|---|---|
| Payments | 48 | High | 1.3× | 62.4 |
| Profile | 24 | Low | 0.9× | 21.6 |
| Notifications | 20 | Medium | 1.0× | 20 |
| Total | 104 h | |||
Risk weighting points effort where failures are expensive. Combine with Three-Point inputs for each module if variance is high.
C) API Project (Analogous + Three-Point)
Historical: 50 endpoints took 160 QA hours (3.2 h/endpoint). New scope: 70 endpoints of similar complexity.
- Analogous baseline:
70 × 3.2 = 224 h - Three-Point for data-heavy endpoints (20 endpoints): O=3, M=4, P=7 → PERT=4.33 h × 20 = 86.6 h
- Remaining 50 endpoints at 3 h/endpoint = 150 h
Total ≈ 236.6 h (slightly above the simple analogous estimate due to data variance).
Tools & Template
- TestScope Pro — turn O/M/P inputs into P50/P80 ranges in minutes; WBS templates; risk multipliers; capacity planner; Monte Carlo (optional); Jira/CSV import; export stakeholder summaries (PDF/CSV).
- Spreadsheets (Excel/Sheets) — great for WBS and PERT math; watch version drift.
- Jira/issue trackers — capacity planning and burndown visualization.
- Perf/Sec tooling (k6/JMeter/Snyk/ZAP) — to anchor non-functional estimates to targets.
For more on choosing techniques (PERT, risk-based, Monte Carlo), read the Complete Guide to Test Estimation .
FAQ
How accurate should my estimate be?
Early estimates are directional (±30%). As requirements stabilize and data/environments are ready, you should converge to ±10–15%. Use P50/P80 to communicate confidence instead of pretending certainty.
How much time should I allocate to regression?
Common patterns: 20–40% of total test execution for a meaningful regression sweep, plus additional time if automation maintenance is due.
Do I count automation as separate effort?
Yes—creation and maintenance are distinct WBS lines. Automation reduces repetitive execution later but is not “free.”
When should I re-estimate?
When scope changes, major risks materialize, or acceptance criteria shift. Track deltas and publish v1.1, v1.2 etc.
Next Steps
- Draft your WBS and capture O/M/P for volatile tasks.
- Roll up PERT hours and convert to calendar time with realistic capacity.
- Publish P50 and P80 plans with assumptions and risks.
- Automate the math in a tool to iterate quickly.
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Deep dive on methods: Test Estimation Techniques — Complete Guide .