WBS Estimation Reference Tool

Quick API / Endpoint Estimator

Complexity	Description	Effort Range (h)	Effort Range (days)	Typical (median h)	Typical (days)
Low	Simple CRUD, single table, standard auth	8–16	1–2	12	1.5
Medium	Multi-table joins, business logic, validation	16–40	2–5	28	3.5
High	Complex workflows, multiple integrations, heavy processing	40–80+	5–10+	80	10

Per-endpoint tasks: design/spec (10–25%), implementation, unit tests, integration tests, e2e test hooks, doc + swagger, code review, deployment config.

Example: 10 Low, 5 Medium, 2 High → 10×12 + 5×28 + 2×80 = 420 hours.

Backend Components (Recommended Ranges)

Component	Typical tasks	Low (h)	Low (days)	Typical (h)	Typical (days)	High (h)	High (days)
Authentication & Authorization	OAuth/JWT, RBAC, sessions, tokens, tests	8	1	24	3	64	8
Core Business Logic	Rules, workflows, orchestrations, domain services	32	4	140	17.5	400	50
Data Access Layer	Repos, ORM mapping, pooling, caching	16	2	52	6.5	160	20
Integration Layer	External APIs, MQ, event streams, adapters	24	3	100	12.5	320	40
Observability & Monitoring	Logging, metrics, tracing, health checks	8	1	32	4	120	15

Database Design & Implementation (Ranges)

Area	Tasks	Low (h)	Low (days)	Typical (h)	Typical (days)	High (h)	High (days)
Schema design	Entity modeling, relationships, indexes	8	1	24	3	80	10
Migration scripts	Forward/rollback, seeding	4	0.5	16	2	64	8
Performance optimization	Query plan, indexing, partitioning	8	1	40	5	160	20
Backup & recovery	Backups, PITR, recovery testing	4	0.5	16	2	64	8

Batch Jobs & Data Processing

Complexity scoring: Use weighted score from matrix. Map factor level → numeric: Low=1, Medium=2, High=3.
Weights: DataVolume 0.30, Transformations 0.25, Dependencies 0.20, SLA 0.15, ErrorHandling 0.10.
Normalized score = Σ( (level-1)/2 * weight ) → value between 0.0 and 1.0.
<0.35: Low, 0.35–0.7: Medium, >0.7: High

Component	Low (h)	Low (days)	Medium (h)	Medium (days)	High (h)	High (days)
Data Extraction	8–16	1–2	16–40	2–5	40–120	5–15
Data Transformation	8–24	1–3	40–120	5–15	120–400	15–50
Data Loading	4–12	0.5–1.5	16–40	2–5	40–120	5–15
Error handling & retries	4–8	0.5–1	8–24	1–3	24–80	3–10
Monitoring & alerting	4–8	0.5–1	8–24	1–3	24–80	3–10
Backfill capability	8–16	1–2	24–80	3–10	80–240	10–30

Frontend / UI Development

Complexity	Components	States	Effort (h per screen)	Effort (days per screen)
Simple	1–5, static	2–3	8–24	1–3
Medium	6–15, forms/tables	4–8	24–60	3–8
Complex	16+, charts, animations	9+	60–120+	8–15+

UI/UX Work (Recommended Ranges)

Activity	Typical (h)	Typical (days)
Research & discovery	16–80	2–10
Information architecture & wireframes	8–40	1–5
Visual design (design system, mocks)	24–120	3–15
Frontend implementation (per medium screen)	24–60	3–8
Accessibility & testing	8–40	1–5

Infrastructure & DevOps (Ranges)

Component	Tasks	Small (h)	Small (days)	Typical (h)	Typical (days)	Large (h)	Large (days)
Environment setup	dev/stage/prod infra + IaC	8–24	1–3	40–120	5–15	120–400	15–50
CI/CD pipeline	build/test/deploy automation	16–40	2–5	80–200	10–25	200–600	25–75
Monitoring & observability	app + infra + logs	8–24	1–3	40–120	5–15	120–400	15–50
Security implementation	TLS, secrets, scanning	8–24	1–3	40–120	5–15	120–400	15–50

Cloud Migration (AWS) — Quick Reference

Strategy	Effort multiplier
Rehost (lift-and-shift)	1.0×
Replatform	1.5×
Repurchase (SaaS)	0.5×
Refactor	3–5×
Retire	0.2×
Retain	0×
Relocate	0.8×

App size	Baseline (h)
Small single-service app	40–120
Medium multi-service app	320–800
Large complex system	1200–4000

AWS Migration Phases (Person-Weeks Guidance)

Phase	Small	Medium	Large
Assess	0.5–2 pw	2–6 pw	6–12+ pw
Mobilize	1–2 pw	2–4 pw	4–8 pw
Migrate & Modernize	1–4 pw	8–24 pw	24–72+ pw

AWS Services Effort (Approx per Workload Size)

Service category	Small (h)	Medium (h)	Large (h)
Compute (EC2/ECS/Lambda)	8–40	40–160	160–640
Database (RDS/DynamoDB)	8–40	40–160	160–640
Storage (S3/EFS)	4–16	16–64	64–256
Networking (VPC/ALB/CloudFront)	8–32	32–128	128–512
Security (IAM/KMS/WAF)	8–32	32–128	128–512
Monitoring (CloudWatch/X-Ray)	8–32	32–128	128–512

Three-point Estimation (PERT) — Example & Template

Formula: PERT = (Optimistic + 4×MostLikely + Pessimistic) ÷ 6

Component	Opt (h)	ML (h)	Pess (h)	PERT (h)
Backend APIs	120	200	360	213.33
Frontend/UI	80	160	320	173.33
Database	40	80	160	86.67
Batch Jobs	40	160	480	193.33
Infrastructure	60	200	400	210.00
Testing	80	160	320	173.33
TOTAL (PERT)				1050.00

Risk Assessment & Risk-adjusted Hours

Label	Probability (p)	Impact (i)
High (H)	0.60	0.60
Medium (M)	0.30	0.30
Low (L)	0.10	0.10

Risk adjusted extra hours (per risk) = PERT_hours_for_related_component × p × i

Risk Category	Probability	Impact	Related component	Base PERT (h)	Added (h)
Technical Complexity	H	H	Backend APIs	213.33	76.80
Team Experience	M	M	Frontend/UI	173.33	15.60
Requirements Changes	H	M	Batch Jobs	193.33	34.80
Integration Complexity	M	H	Infrastructure	210.00	37.80
Third-party Dependencies	M	M	Database	86.67	7.80
Sum added hours					172.80

Risk-adjusted total = PERT total + sum(added) = 1050.00 + 172.80 = 1,222.80 h (≈ 1,223 h).

Convert to FTE / Schedule

• Full-time capacity per week per engineer = 40 h.
• Total weeks for N FTEs = Total_hours / (N × 40).
• Required FTEs = Total_hours / (duration_weeks × 40).

Example: 1,223 h over 10 weeks → 1,223 / (10×40) = 3.06 FTE → plan 3–4 FTE.

AI Productivity Gain

Tool	Typical Productivity Gain (%)
Copilot	5–20%
Cursor	5–20%
Windsurf	5–20%
Codeium	5–15%
Tabnine	5–10%
Other	0–10%

AI productivity tools can reduce total effort. Apply a realistic gain (typically 5–20%) based on team adoption and workflow integration. This is applied after all other adjustments.

Testing, QA, Performance, Learning Curve & Adjustment Factors

Factor	Typical %	Purpose
Functional Testing	10–20%	Manual/automated test case writing, execution, validation
QA & Bug Fixing	5–15%	Defect triage, bug fixing, regression, retesting
Performance Testing	3–10%	Load, stress, soak, profiling, tuning
Learning Curve	3–10%	Team ramp-up, onboarding, new tech/processes
Other Factors	0–10%	Compliance, documentation, handover, etc.

Apply these as percentage multipliers to the subtotal (before risk/contingency) for a more realistic estimate.

How to Apply This Reference (Practical Steps)

1. Inventory everything (endpoints, screens, jobs, infra items).
2. Classify each item (Low/Medium/High) using the tables above.
3. Fill OPT/ML/PESS for big components (or use typical ranges).
4. Compute PERT per component and sum.
5. Score risks and compute added hours per risk (method above).
6. Produce final plan: risk-adjusted hours → convert to FTE and schedule → add contingency (10–25% depending on appetite).

Deliverables / Checklist to Include with an Estimate

• Inventory list (endpoints/screens/jobs/services) with complexity tags
• PERT table (opt/ml/pess + computed)
• Risk register (prob/impact + mitigation + added hours)
• Migration strategy (7 Rs mapping if cloud work)
• Resource plan (FTEs by role & timeline)
• Assumptions & exclusions (explicit)