/project-sizing-guide

name: project-sizing-guide
description: "Software project effort estimation assistant. Outputs three-point estimates (optimistic/most-likely/pessimistic values with confidence intervals), T-shirt sizes, or Function Point Analysis (FPA) counts. Triggered when users ask 'how long will this feature take,' need to assess project workload, perform PERT estimation, T-shirt sizing, FPA, sprint planning, or quote-based effort breakdowns."
license: MIT

Project Sizing Guide — Software Project Effort Estimation

Helps teams produce scientifically grounded effort estimates for software projects, based on three major methodologies: Three-Point Estimation (PERT), T-shirt Sizing, and Function Point Analysis (FPA). Outputs optimistic, most-likely, and pessimistic values along with risk intervals.

Quick Start

1. **User provides a requirements description** → Agent identifies functional modules and breaks them into a Work Breakdown Structure (WBS) 2. **Select an estimation method** → Choose the best-fit approach based on project stage and available information 3. **Estimate each item** → Assign O/M/P (Optimistic / Most Likely / Pessimistic) values to every work package 4. **Aggregate and report** → Generate an estimation report with risk analysis and confidence intervals

A calculation helper is available:

python3 scripts/estimate_calculator.py --method pert --tasks '[{"name":"User Login","O":2,"M":3,"P":8}]'

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Method Selection Guide

| Scenario | Recommended Method | Rationale | |----------|-------------------|-----------| | Early feasibility study, rough budgeting | T-shirt Sizing | Little information available; quickly align on order of magnitude | | Sprint planning, iteration estimation | Three-Point Estimation (PERT) | Good granularity with confidence intervals | | Contract bidding, large-project RFPs | Function Point Analysis (FPA) | Most rigorous; industry-comparable | | Team has historical data | PERT + historical calibration | Combines empirical correction with data |

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Method 1: Three-Point Estimation (PERT)

Core Formulas

| Metric | Formula | Meaning | |--------|---------|---------| | Expected Value E | (O + 4M + P) / 6 | Weighted average effort | | Standard Deviation σ | (P − O) / 6 | Estimation uncertainty | | Variance V | σ² | Used to aggregate across tasks | | Project Total Expected | ΣE | Sum of individual expected values | | Project Total Std Dev | √(ΣV) | Square root of summed variances |

Where:

• **O** (Optimistic): Shortest duration assuming everything goes smoothly
• **M** (Most Likely): Duration under normal circumstances
• **P** (Pessimistic): Longest duration when significant difficulties arise

Confidence Intervals

| Confidence Level | Interval | Use Case | |-----------------|----------|----------| | 68.3% | E ± 1σ | Internal rough estimates | | 90% | E ± 1.645σ | Project planning | | 95% | E ± 2σ | External quotes | | 99.7% | E ± 3σ | Contractual commitments |

Steps

1. **Build the WBS**: Decompose requirements into the smallest independently estimable units (recommended ≤ 5 person-days each) 2. **Three-point estimation**: For each work package, provide O / M / P values (use consistent units: person-hours or person-days) 3. **Calculate per-task expected value and standard deviation** 4. **Aggregate project-level metrics**: Total Expected = ΣE, Total Std Dev = √(Σσ²) 5. **Output confidence intervals**: Choose a confidence level based on risk appetite

O/M/P Estimation Rules of Thumb

• O should not be less than 30% of M (overly optimistic suggests essential steps were overlooked)
• P should not exceed 5× M (overly pessimistic suggests unclear requirements that need clarification first)
• If O ≈ M ≈ P, the task is either extremely well-understood or the estimator hasn't seriously considered risks
• The P/O ratio (spread ratio) reflects uncertainty: < 2 = low risk, 2–4 = medium risk, > 4 = high risk

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Method 2: T-shirt Sizing

Size Reference Table

| Size | Typical Range (person-days) | Typical Story Points | Suitable For | |------|----------------------------|---------------------|--------------| | XS | 0.25 – 0.5 | 1 | Config changes, copy edits, simple bug fixes | | S | 0.5 – 2 | 2 – 3 | Single-component development, simple API, minor UI tweaks | | M | 2 – 5 | 5 – 8 | Complete feature module, moderately complex API | | L | 5 – 15 | 13 – 21 | Cross-module features requiring integration | | XL | 15 – 40 | 34 – 55 | Subsystem-level development requiring architecture design | | XXL | 40+ | 89+ | Should be split across multiple iterations; not recommended as a single estimation unit |

Converting T-shirt Sizes to Three-Point Estimates

When more precise numbers are needed, T-shirt sizes can be converted to three-point estimates:

| Size | O (person-days) | M (person-days) | P (person-days) | |------|-----------------|-----------------|-----------------| | XS | 0.25 | 0.5 | 1 | | S | 0.5 | 1 | 2.5 | | M | 2 | 3.5 | 7 | | L | 5 | 10 | 20 | | XL | 15 | 25 | 50 | | XXL | 40 | 70 | 150 |

Steps

1. **Team alignment**: Confirm what each size means (the table above is a reference; teams may customize) 2. **Independent assessment**: Each person assigns a size independently to avoid anchoring bias 3. **Discuss discrepancies**: When estimates differ by more than 2 sizes, a discussion is mandatory 4. **Reach consensus**: Adopt the team consensus value 5. **Convert to numbers** (optional): Use the table above to derive O/M/P values

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Method 3: Function Point Analysis (FPA)

Five Function Component Types

| Component Type | Abbreviation | Definition | Example | |---------------|-------------|-----------|---------| | Internal Logical File | ILF | Logical data group maintained by the application | Users table, Orders table | | External Interface File | EIF | Data group referenced but not maintained by the application | Third-party exchange rate data | | External Input | EI | Data processing entering the syste