Estimating development time: from gut feel to method

May 13, 2025

When will it be ready?

When someone in a meeting asks, “When will it be done?”, you’ve entered the most high-stakes part of any software initiative: time estimation. Even today—with mature agile frameworks and automation tools at every step—deadlines slip, budgets swell, and trust between teams, clients, and investors erodes.

This article is your quickstart guide for:

• New developers who need to put an hourly price on their first ideas.
• Solo founders juggling their runway against every ticking hour.
• Anyone stepping into project management without losing their technical edge.

The goal: go from “I think it’ll take two weeks” to “we’re 90% confident it’ll ship on June 7.”

Let’s start by understanding why this is such a challenge.

Knowing how long a software project will take isn’t a luxury—it’s essential for budgeting, aligning teams, and hitting launch dates that can make or break success. Yet software is a moving target: requirements shift, hidden dependencies pop up, and our natural optimism leads us to undershoot reality. The result? Delays, cost overruns, and damaged credibility all around.

Still, we need firm dates. Without a timeline, there’s no roadmap, no peace of mind for investors, and no sprint planning. The art lies in transforming vague “around two weeks” estimates into defensible ranges (“June 7, with 90% confidence”)—without turning estimation into an endless ritual. Just remember: in an environment of shifting requirements, even the best estimate won’t save you unless you nail down clear specs and know exactly what you’re building.

Back in the day, estimation was guesswork: A short history of chaos

Just twenty years ago, things were even worse. Historical data was scarce, integrations were wired together “by hand,” and many of today’s tools didn’t exist. Without issue trackers or omnipresent Git, every release was a “Big Bang,” and delays were measured in months. It wasn’t until the rise of agile methods, automated CI/CD pipelines, and a culture of continuous measurement that estimation stopped feeling like flipping a coin.

• 1968 — “Software Crisis” (Wikipedia)
  Coined at the NATO Software Engineering Conference in Garmisch-Partenkirchen, the term described defense projects like IBM OS/360 and missile-control systems running up to 10× over time and budget, exposing the limits of then-current practices.

• 1995 — Denver International Airport Baggage System (Calleam)
  Originally planned for a 9-month rollout, the baggage system dragged on for nearly 3 years, adding about $560 M to the bill. After repeated tests where luggage flew off conveyors or got crushed, the system was scaled back and ultimately dismantled in 2005.

• 2002–2011 — NHS NPfIT (UK) (The Guardian)
  The National Programme for IT aimed to unify electronic health records across the UK’s NHS. Budgeted at £6 bn, it was scrapped after costing £12–13 bn, amid massive contractual disputes and negligible clinical benefits.

These—and many other—fiascos proved that we desperately needed a method to turn gut feelings into defendable, realistic numbers. While delays often stem from politics, contracts, or logistics, most of the pain lives in the technical phase, and that’s precisely where we’ll focus: the software development itself.

Estimation techniques: Turning gut feel into numbers

Whether you’re building a web app, mobile client, desktop tool, or embedded system, most software projects share common building blocks alongside their unique bits. When estimating, it’s crucial to distinguish between routine tasks you know inside out and those that demand research or learning.

This is especially important for solo developers: unknown components will always crop up (a new payment gateway, a notification service, real-time data sync, etc.). Make sure to explicitly allocate research hours in your breakdown. Whether you bill those hours to the client or absorb them as personal investment is up to your own policy and agreement.

Below are some of the most popular estimation methods in use today. We won’t cover project management frameworks here—just the techniques that translate requirements into hours or points: from absolute approaches (PERT/hours) to relative sizing (story points) and data-driven models.

1. Absolute estimation (Hours & Three-Point / PERT)

You start by defining three scenarios for each task: Optimistic (O) when everything goes smoothly; Most Likely (M) for your realistic best guess; and Pessimistic (P) to cover potential setbacks. Then use the formula (O + 4·M + P) / 6 to calculate a weighted average, softening the extremes and giving you a balanced estimate. This approach shines when you’re dealing with moderate uncertainty—not entirely familiar territory, but not brand-new either—and when your team budgets or bills by the hour.

2. Relative estimation (Story points & velocity)

Rather than hours, you assign Story Points (1, 2, 3, 5, 8…) to each user story—a concise description of a feature—based on how it compares in complexity to the others. You then track your team’s velocity, i.e., the number of points completed per sprint, to forecast how many points you can tackle in upcoming cycles. This method works best for established agile teams with reliable velocity history.

3. T-Shirt sizing

For a quick, high-level view, you label each task or story with a size (XS, S, M, L, XL) according to its scale. Later, you map those sizes to rough hour or point ranges (e.g., M = 5–8 hours). T-Shirt Sizing speeds up early estimation when your backlog is massive and you don’t want to deep-dive on every item up front.

There are countless estimation techniques, but in my experience with small teams and mid-sized projects, simplicity wins. Early on, I used a larger safety buffer because I underestimated many tasks; over time and with practice, that buffer has shrunk. Plus, time-tracking tools like WorkIO make it easy to log actual hours spent, compare against past data, and refine future estimates with far greater accuracy.

My personal approach: Simple, practical, and data-driven

My go-to method is straightforward—no magic, just break the project down into the smallest possible tasks and estimate each one in hours. From setting up the database to building a form or a REST endpoint, everything gets written down and totaled. Finally, I apply a contingency buffer (15–20%) to cover research, tweaks, and minor surprises. No complex formulas or Planning Poker sessions—just a solid breakdown and the discipline to track every work item.

For larger projects involving multiple teams or departments, advanced estimation frameworks may be necessary (and appropriate). But for small to mid-sized efforts, there’s no need to overengineer: a good task list, hourly estimates, and a simple buffer go a long way.

Practical example: Estimating a simple e-Commerce with payments

Let’s say we’re building an online store that includes payment processing—using a gateway like Stripe, for instance:

Why this breakdown helps:

• By splitting each form and CRUD action into distinct tasks, you pinpoint areas requiring extra research or complexity.
• This allows you to tailor the contingency buffer per block (e.g., extra research time for Stripe).
• It makes it easier to track real effort in tools like WorkIO and compare actuals against estimates.

This level of granularity is ideal for small to mid-sized projects where every hour matters and clients demand transparency.

Better estimates are possible (and measurable)

This example is purely illustrative—in real-world projects you’ll likely need to account for additional factors (extra integrations, legal compliance checks, design reviews, stakeholder coordination, etc.). The key is mastering the process:

1. Break your project into the smallest tasks.
2. Estimate each one in hours.
3. Add up and apply a contingency buffer.
4. Measure the actual time spent.

Keeping a rigorous time-tracking practice is the best way to sharpen future estimates. Tools like WorkIO make it easy to compare your estimates against real data, spot deviations, and refine your buffer multipliers—closing the loop between prediction and reality.

Happy coding!