The Estimation Conundrum: Why Your Estimates are Always Wrong

Estimating software development time is a bit like trying to predict the weather a year from now. You can use all the fancy tools and models you want, but at the end of the day, it’s still a guess. And guess what? Your guesses are probably wrong. But here’s the thing: that’s okay.

The Nature of Software Estimation

Software development is inherently unpredictable. Every project is unique, and even if you’ve done something similar before, there are always new variables to consider. This unpredictability makes estimation a challenging task. As Dave Farley points out, trying to increase the precision of estimates is often the wrong approach. Instead, we should focus on making our estimates more useful and realistic.

Cognitive Biases and Estimation

One of the primary reasons estimates go awry is due to cognitive biases. Developers often suffer from overconfidence, optimism, and anchoring biases. For instance, you might think you can complete a task in half the time it actually takes because you’re overconfident in your abilities. These biases can significantly skew your estimates, leading to unrealistic expectations.

The Problem with Precision

When we estimate, we tend to provide narrow ranges, suggesting we’re more confident in our abilities than we should be. This overconfidence is highlighted by the fact that people usually underestimate the complexity of tasks. For example, if you’re asked to estimate how many iPads were sold in 2019 and you want to be 90% certain, you should provide a wide range, like zero to one billion. However, most people would give a much narrower range, indicating their overconfidence.

Historical Data and Team Involvement

So, how can we improve our estimates? One effective approach is to use historical data. Look at past projects and analyze how long they took and how much they cost. This data can help you make more accurate predictions for future projects. Additionally, involving your team in the estimation process can provide more realistic estimates. Use their expertise and experience to create better-informed estimates.

The Cone of Uncertainty

The Cone of Uncertainty, introduced by Steve McConnell, illustrates the high uncertainty at the early stages of a project. As the project progresses, uncertainty decreases, but it’s crucial to acknowledge this uncertainty from the start. This concept can help you understand why your initial estimates might be far off and why it’s essential to adjust them as more information becomes available.

Laws of Software Development

Several laws and principles can help explain why estimates often fail:

  • Hofstadter’s Law: “It always takes longer than you expect, even when you consider Hofstadter’s Law.” This law highlights the recursive nature of estimation, where considering the complexity of a task and human optimism often leads to underestimation.
  • Brook’s Law: “Adding manpower to a late software project makes it later.” This law emphasizes the negative impact of increasing team size to speed up a project.
  • Planning Fallacy: People tend to focus on trivial details rather than critical aspects of a project, leading to an overemphasis on understandable tasks while underestimating more complex ones.
  • Parkinson’s Law: “Work expands to fill the time available for completion.” If a deadline is too generous, developers may spend more time on a task than necessary, leading to inefficiencies and delays.
  • Ninety-Ninety Rule: “The first 90% of the code accounts for the first 90% of the development time; the remaining 10% of the code accounts for the other 90% of the development time.” This rule highlights the difficulty of accurately estimating the time needed for bug fixing, optimization, and polishing.

A Practical Approach to Estimation

Given the inherent uncertainties, here’s a practical approach to estimation:

  1. Use Ranges Instead of Exact Numbers: Instead of providing a single estimate, use ranges to account for uncertainty. For example, if you estimate a task will take one day, consider a range of 3-6 days to complete.

  2. Involve the Team: Collaborative estimation involves the entire team, leveraging their collective experience and expertise to create more realistic estimates.

  3. Iterative Planning: Break down the project into smaller, manageable tasks. This approach allows for more accurate estimation and better handling of uncertainties.

  4. Feedback Loop: Provide feedback on past estimates to improve future ones. Studies show that when estimators receive feedback on their accuracy, they improve significantly over time.

Example: Estimating with Ranges

Let’s illustrate this with a simple example using Mermaid syntax:

graph LR A[Task_Estimation] -->|Provide Range| B[3-6 Days] B -->|Break Down Task| C[Smaller Tasks] C -->|Estimate Each Task| D[Iterative Planning] D -->|Review and Adjust| B[Feedback_Loop]

In this example, instead of estimating a task will take exactly one day, we provide a range (3-6 days). We then break down the task into smaller, more manageable parts and estimate each one iteratively. Finally, we review and adjust our estimates based on feedback.

Conclusion

Estimates are always wrong, but that’s okay. The key is to make them useful and realistic. By acknowledging the uncertainties, using historical data, involving the team, and providing ranges instead of exact numbers, you can improve the accuracy of your estimates. Remember, estimation is a forecast, and forecasts are inherently uncertain. So, the next time you’re asked to estimate, don’t be afraid to say, “It’s going to take between 3-6 days,” and mean it.

And if someone asks why your estimates are always wrong, just smile and say, “It’s not me, it’s the nature of software development.”