"How long will it take to build this feature?"
"Hmm... probably 3 days?"
(Five days later)
"Still need a bit more time."
Why do our estimates consistently miss the mark?
I've been asked this countless times, and been wrong countless times. Then I realized: we're overlooking too much.
Five Reasons Time Estimates Fail
1. Only Counting Coding Time
"Login API? Should be 2 hours of coding"
Many developers think this way. But reality is different:
# What developer thinks
developer_estimate = {
"coding": 2 # hours
}
# What's actually needed
actual_time = {
"understanding requirements": 0.5,
"design thinking": 0.5,
"environment setup": 0.5,
"coding": 2,
"debugging": 1,
"writing tests": 1,
"code review": 0.5,
"addressing feedback": 0.5,
"documentation": 0.5,
"deployment": 0.5,
"monitoring check": 0.5
}
# Total 8 hours (4x!)
print(f"Estimate: {developer_estimate['coding']} hours")
print(f"Actual: {sum(actual_time.values())} hours")
print(f"Error: {sum(actual_time.values()) / developer_estimate['coding']}x")
In practice, "pure coding" is only 25-30% of total time.
2. Assuming Best-Case Scenarios
Our brains are surprisingly optimistic:
// Developer's ideal world
const ideal_world = {
bugs: 'Won't have any',
requirement_changes: 'Confirmed, won't change',
external_API: 'Will work as documented',
code_review: 'Will pass first try',
tests: 'All will pass',
deployment: 'Will go smoothly',
};
// Real world
const real_world = {
bugs: '5 unexpected edge cases discovered',
requirement_changes: 'Oh, can you add this too?',
external_API: 'Documentation was wrong? There's a rate limit?',
code_review: 'Please refactor this section',
tests: 'Weird bug that only fails in CI',
deployment: 'Rollback... fix... redeploy...',
};
3. Ignoring Context Switching
Do you think "8 hours of work per day means 8 hours of coding possible"?
def calculate_actual_productivity():
"""Calculate truly productive time"""
total_hours = 8
# Time thieves
time_thieves = {
"daily standup": 0.5,
"email/slack check": 1,
"unexpected questions/help": 0.5,
"PR review (others')": 0.5,
"meetings (suddenly scheduled)": 1,
"build/test wait": 0.5,
"focus recovery time": 0.5,
"coffee/bathroom/stretching": 0.5
}
productive_hours = total_hours - sum(time_thieves.values())
print(f"Work hours: {total_hours} hours")
print(f"Stolen time: {sum(time_thieves.values())} hours")
print(f"Actually productive: {productive_hours} hours")
return productive_hours
# Result: Out of 8 hours, only 3 hours are actually productive
Shocking fact: Most developers only code with focus for 2-4 hours per day.
4. Underestimating Integration and Debugging
"My code is perfect. It'll work on first try"
// Individual feature development time
const feature_time = {
'Payment module': 16, // hours
'Shopping cart': 12,
'Product list': 8,
Search: 10,
};
// Additional time when integrating
const integration_overhead = {
'Payment + Cart': 8, // Data sync issues
'Cart + Products': 4, // Inventory conflicts
'Search + Products': 3, // Indexing problems
'Full integration test': 12,
'Bug fixes': 16,
'Performance optimization': 8,
};
// Total time = 46 hours (features) + 51 hours (integration) = 97 hours!
// Integration takes longer than development
5. Missing Time to Understand Others' Code
"Just call that function and it'll work"
In reality:
# Time to understand external dependencies
dependency_overhead = {
"reading documentation": 2,
"finding example code": 1,
"testing locally": 2,
"debugging unexpected behavior": 3,
"wrapping to fit our code": 2,
"handling edge cases": 2
}
# "Just integrate Stripe" → Actually 12 hours
Hidden Work Checklist
Use this checklist when estimating development time:
Pre-Development Work (20% of total)
□ Requirements clarification meeting
□ Technology stack research
□ Design document writing
□ Development environment setup
□ Dummy data preparation
□ API spec definition
During Development Work (40% of total)
□ Actual coding
□ Unit test writing
□ Local testing
□ Debugging
□ Refactoring
□ Comments and documentation
Post-Development Work (40% of total)
□ Code review request
□ Addressing review feedback
□ Integration testing
□ Bug fixes
□ Performance optimization
□ Deployment preparation
□ Staging testing
□ Production deployment
□ Monitoring setup
□ Rollback plan
Multiplier Rules for Realistic Estimation
Developer Experience Coefficient
const experience_multiplier = {
'first time task': {
senior: 2.0, // Estimated time x 2
junior: 4.0, // Estimated time x 4
},
'similar experience': {
senior: 1.5,
junior: 2.5,
},
'done multiple times': {
senior: 1.2,
junior: 1.8,
},
};
// Usage example
const estimated = 8; // hours
const actual = estimated * experience_multiplier['first time task']['junior'];
// Actual: 32 hours
Task Complexity Coefficient
def get_complexity_multiplier(task):
"""Additional coefficient based on task complexity"""
multipliers = {
"Simple CRUD": 1.2,
"Business logic": 1.8,
"External API integration": 2.5,
"Payment/security": 3.0,
"Real-time processing": 3.5,
"Distributed system": 4.0
}
base_estimate = task["hours"]
complexity = task["type"]
return base_estimate * multipliers[complexity]
# Example: "Payment system 8 hours" → Actually 24 hours
Team-Level Hidden Work
Beyond individual work, there's team-level overhead:
team_overhead = {
"daily standup": "team size * 0.5 hours",
"weekly meeting": "team size * 1 hour",
"code review (others')": "number of PRs * 0.5 hours",
"knowledge sharing": "2 hours/week",
"documentation updates": "1 hour/week",
"urgent issue response": "3 hours/week (average)",
"deployment prep/monitoring": "2 hours/week"
}
# Weekly overhead for 10-person team
weekly_overhead_per_person = 12 # hours
# 30% of 40 hours/week is team activities
Buffer Strategy: Creating Realistic Schedules
Scientific Estimation with PERT Technique
def pert_estimation(optimistic, most_likely, pessimistic):
"""
Program Evaluation and Review Technique
Estimation method used by NASA
"""
# PERT formula
estimate = (optimistic + 4 * most_likely + pessimistic) / 6
# Standard deviation (uncertainty measure)
std_dev = (pessimistic - optimistic) / 6
return {
"expected": estimate,
"std_dev": std_dev,
"68%_confidence": (estimate - std_dev, estimate + std_dev),
"95%_confidence": (estimate - 2*std_dev, estimate + 2*std_dev),
"99%_confidence": (estimate - 3*std_dev, estimate + 3*std_dev)
}
# Actual usage example
login_feature = pert_estimation(
optimistic=8, # If everything is perfect
most_likely=12, # Probably
pessimistic=24 # Worst case
)
print(f"Expected: {login_feature['expected']:.1f} hours")
print(f"95% probability: {login_feature['95%_confidence']} hours")
Risk-Based Buffer
const calculate_buffer = (task) => {
let buffer_percentage = 10; // Base 10%
// Risk factors
if (task.has_external_dependency) buffer_percentage += 20;
if (task.uses_new_technology) buffer_percentage += 25;
if (task.requires_integration) buffer_percentage += 15;
if (task.has_unclear_requirements) buffer_percentage += 30;
if (task.assigned_to_junior) buffer_percentage += 20;
return task.estimated_hours * (1 + buffer_percentage / 100);
};
// High-risk task example
const risky_task = {
name: 'External payment API integration',
estimated_hours: 16,
has_external_dependency: true, // +20%
uses_new_technology: true, // +25%
requires_integration: true, // +15%
// Total buffer: 70%
};
const realistic_estimate = calculate_buffer(risky_task);
// 16 hours → 27.2 hours
Practical Tips for Better Estimation
1. Estimation Retrospective
## Sprint Retrospective Template
### This Sprint Estimation Accuracy
- Task A: Estimated 8h → Actual 12h (150%)
- Task B: Estimated 16h → Actual 14h (87%)
- Task C: Estimated 4h → Actual 10h (250%)
### Causes of Differences
- Task A: Unexpected refactoring needed
- Task C: External API documentation inaccurate
### Next Sprint Improvements
- Tasks with external dependencies: existing estimate x2
- Legacy code with refactoring possibility: +50% buffer
2. Using Historical Data
# Estimation based on past data
historical_data = {
"Login feature": [8, 12, 10, 14], # Actual times from 4 past instances
"CRUD API": [16, 20, 18, 22],
"External API integration": [24, 40, 32, 48]
}
def estimate_based_on_history(task_type):
if task_type in historical_data:
past_times = historical_data[task_type]
avg = sum(past_times) / len(past_times)
max_time = max(past_times)
# Use midpoint between average and worst
return (avg + max_time) / 2
else:
return None # If no history, use PERT
3. Using Planning Poker
// Team estimation session
const planning_poker = {
task: 'User profile page development',
estimates: {
DeveloperA: 8, // Senior, lots of similar experience
DeveloperB: 16, // Junior, first time
DeveloperC: 12, // Mid-level
Designer: 20, // Considering design complexity
},
discussion: ['A: Can reuse existing components', 'B: State management seems complex', 'D: Responsive design needs time'],
final_estimate: 14, // Agreed after discussion
};
Conclusion: Honest Estimation Wins
There's a saying: "Developers are optimists."
But honest estimation ultimately saves everyone:
- PMs can set realistic schedules
- Developers can avoid overtime
- Customers can receive trustworthy promises
Next time you estimate, try this:
- Estimate pure development time
- x3 (consider hidden work)
- Add risk buffer
- Validate with team
At first, you might hear "Isn't that too conservative?"
But after accurately estimating just 3 times, everyone will trust you.
Estimation is science, not art. Collect data, find patterns, improve.
Need accurate project estimation and management? Check out Plexo.
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