Overcoming Estimation Bias: Techniques For Accurate Software Development Estimates

The Perils of Overconfidence

Software developers often struggle with creating accurate estimates for project duration and effort. Estimation bias leading to overconfidence is a major contributor to poor estimates. Developers may anchor on initial assessments, fail to account for unfamiliar tasks, or have cognitive biases causing them to focus on best-case scenarios.

The consequences of poor estimates are severe. Projects often go over budget and past deadlines due to inaccurate estimates. This erodes stakeholder trust, hurts developer credibility, and introduces unnecessary obstacles into the development process.

Developers must implement structured estimation techniques to overcome bias-induced overconfidence. Reference class forecasting, getting multiple perspectives, and accounting for unknowns are proven methods for creating realistic estimates.

Techniques to Improve Accuracy

Reference Class Forecasting

Reference class forecasting bases estimates on actual outcomes across a reference class of similar projects. Instead of considering internal factors only, it looks at historical data from external comparable projects. This circumvents cognitive biases related to overconfidence when estimating novel projects.

To utilize reference class forecasting:

• Identify key parameters for the project being estimated such as size, effort, duration etc.
• Find reference projects with similar characteristics and domains.
• Collect parameter data from the reference class projects.
• Use the distribution of values from the reference class to estimate your project.

Matching attributes between the current project and reference projects improves accuracy. Reference class forecasting outperforms unaided expert judgment for software development estimates.

Outside View Approach

The outside view approach avoids getting stuck in the details of a specific project. Instead of an inside view focusing on the unique attributes of a project, the outside view looks at the project from a higher level based on data from other projects.

It works by ignoring the specifics of the project, thinking about similar projects, and making estimates accordingly. This bypasses optimistic and pessimistic insider views that often lead to bias.

An example would be estimating a project’s duration. Rather than analyzing all the details of the requirements and making task estimates, look at historical data for similar sized projects completed by the team and use their average duration.

Getting Multiple Perspectives

Getting input from multiple experts mitigates individual biases and the tendency to anchor on initial values. Different people will apply varied estimating approaches and catch items that may have been overlooked.

Strategies to get multiple perspectives include:

• Expert teams: Have a group of experts discuss estimations and come to consensus estimates.
• Delphi method: Experts anonymously provide estimates then discuss results from the group before re-estimating.
• Playing Devil’s advocate: Assign someone to critique estimates and find weaknesses.

Aggregating estimates from even a handful of experts can greatly improve accuracy over individual views.

Accounting for Unknowns

Unknowns due to lack of knowledge about project particulars lead to optimism bias. Capturing known-unknowns and acknowledging unknown-unknowns helps overcome this.

Build known-unknowns directly into estimates:

• Explicitly call out unsure tasks and build in padding.
• Use wider effort ranges for unfamiliar work.
• Increase estimates for new technologies/methodologies.

Also formalize an unknown-unknowns allowance by reserving extra time/resources as a hedge against unexpected issues arising mid-project.

Managing Uncertainty

While techniques like reference class forecasting help, estimates still involve uncertainty. Quantifying uncertainty aids stakeholders in understanding the reliability of estimates.

Buffers

Buffers explicitly account for uncertainty by adding extra time or resources. Document estimated most likely, best case and worst case durations or effort levels based on data variability. Stakeholders can use these ranges for planning.

Confidence Levels

Specify confidence levels reflecting the probability that estimates will be met to communicate certainty. For example: “There is an 80% chance the project will complete within 110 person days of effort”. Confidence levels set expectations around adherence.

Monte Carlo Simulation

Monte Carlo simulation predicts estimate distribution rather than just a value. It models many scenarios by combining probability distributions for individual inputs. Running simulations shows the likelihood of outcomes.

For example, estimate task durations with PERT distributions showing optimistic, most likely and pessimistic values. Simulate schedules created by combining tasks to forecast project durations.

Example Code for Estimation Models

Advanced estimation techniques leverage simulation and probabilistic models for enhanced analysis. Python code examples demonstrate implementations.

Monte Carlo Simulation

import numpy as np 
import pandas as pd
from scipy.stats import uniform, triangular
 
num_iterations = 1000
task_estimates = {'Design': triangular(5, 8, 15), 
                  'Code': uniform(15, 30),
                  'Test': triangular(8, 12, 20)} 
 
results = []
for i in range(num_iterations):
    total = 0 
    for task in task_estimates:
        duration = np.random.default_rng().triangular(**task_estimates[task]) 
        total += duration
    results.append(total)
 
df = pd.DataFrame(results, columns=['Total Duration'])
print(df.describe())

Running this basic Monte Carlo simulation provides statistics including mean, percentiles, and standard deviation for the duration estimate distribution from combining estimated task durations.

Bayesian Networks

Bayesian networks represent probabilistic relationships between variables. They combine prior knowledge with observed evidence to calculate updated probability distributions.

  
import pandas as pd
from pomegranate import *

effort_dist = ConditionalProbabilityTable([
    [NormalDistribution(loc=50, scale=5)], 
    [NormalDistribution(loc=60, scale=10)],  
    [NormalDistribution(loc=75, scale=15)]], [0.3, 0.5, 0.2]) 

duration_dist = ConditionalProbabilityTable([
    [ExponentialDistribution(1 / 50)],
    [ExponentialDistribution(1 / 60)],
    [ExponentialDistribution(1 / 75)]], [0.3, 0.5, 0.2])
 
model = BayesianNetwork("EstimationModel") 

effort = Node(effort_dist, name="Effort")  
duration = Node(duration_dist, name="Duration")

model.add_nodes([effort, duration])

model.add_edge(effort, duration)

model.bake()

print(model.probability([effort, duration])) 

This Bayesian network models probabilistic dependence between level of effort and project duration. The code calculates joint probability distributions for effort level and duration estimates.

Continually Refining Estimates

Estimation is an ongoing process, not just an initial step. Continually get feedback, update estimates, and review accuracy as the project progresses.

Updating Estimates with New Data

Update estimates periodically as work is completed using quantitative data. Record actuals such as tasks completed, effort expended, velocities etc. and rerun estimation models.

Look for estimate vs. actual deviations signaling undiscovered work or unsupported assumptions. Refine probability distributions and logic in models.

Updating estimates aids corrective action like scope change when warranted and provides stakeholders a realistic picture of progress.

Reviewing Past Accuracy

Conduct periodic retrospective reviews of past estimate accuracy. Calculate error rates vs actuals and identify patterns related to sessions, models or experts that led estimates astray.

Feed findings back into improving estimation processes. Mistakes offer rich insights into enhancing approaches to be more empirical and fact based.

Ongoing accuracy reviews boost credibility with stakeholders seeing data-driven efforts to refine precision.