If you have ever watched a sprint commitment dissolve by day three, you already know that effort estimation in software development is one of the most persistently broken processes in the industry. Teams estimate. Teams miss. Leadership loses trust. Engineers lose morale. And the cycle repeats.
This is not a discipline problem. It is not because engineers are bad at math or because product managers are unreasonable. The problem is structural, and until you address the root causes, no estimation framework will save you.
According to the Standish Group, 66% of software projects experience cost overruns. Not "a few unlucky ones." Two-thirds. That is not a failure of individual teams. That is a systemic pattern.
The Estimation Accuracy Problem
Let us look at the scale of the problem before we diagnose it.
The average software project overruns its initial estimate by 1.8x. That means a project estimated at six months will likely take closer to eleven. A feature estimated at two sprints will take nearly four. This ratio holds remarkably steady across industries, team sizes, and methodologies.
Research on effort estimation accuracy shows a mean effort overrun of approximately 30% across large samples of software projects. That 30% represents real cost: delayed launches, missed market windows, and eroded stakeholder confidence.
PMI puts it in dollar terms: for every $1 billion spent on projects, approximately $109 million is wasted due to poor project performance, with estimation inaccuracy as a primary driver. That is not a rounding error on the balance sheet.
The frustrating part is that awareness of the problem has not fixed it. The industry has known about estimation inaccuracy for decades. We have invented story points, planning poker, t-shirt sizing, and Monte Carlo simulations. And the overrun rate has barely moved. Maybe the problem is not the estimation technique.
Why Estimates Overrun
There are well-documented reasons why software estimates go wrong. Some are cognitive. Some are organizational. Most are both.
Optimism bias is universal.
Engineers are builders by nature. When evaluating a task, we instinctively think about the happy path: the code we will write, the tests that will pass, the clean architecture we will implement. We underweight the time spent on debugging, dependency issues, code review iterations, and the meeting that interrupts the flow state we needed.
This is not carelessness. It is how human brains work when evaluating future tasks. We anchor on the work itself and systematically discount the friction around it.
Complexity is non-linear.
A feature that touches one service is relatively predictable. A feature that touches three services introduces cross-team coordination, API contract negotiations, deployment sequencing, and integration testing. The effort does not scale linearly with the number of components. It scales closer to exponentially, and most estimation processes treat it as linear.
Unknown unknowns are, by definition, invisible.
Every codebase has dark corners. Code that looks straightforward until you discover a hidden dependency. A database schema that made sense three years ago but now creates performance bottlenecks under current load. An undocumented business rule baked into a conditional that no one remembers writing.
You cannot estimate what you cannot see. And in most codebases, there is a lot you cannot see.
Organizational pressure distorts estimates.
Even when engineers produce honest estimates, organizational dynamics compress them. "Can we do it in two sprints instead of three?" sounds like a question. It functions as a directive. Over time, teams learn to provide the estimates that will be accepted rather than the estimates that reflect reality.
For a closer look at how this plays out in sprint contexts, our post on sprint planning covers the mechanics in detail.
The Visibility Root Cause
If I had to identify a single root cause for estimation failure, it would be this: teams estimate work in systems they do not fully understand.
Think about what is required for an accurate effort estimate. You need to know:
- What code will be affected by the change
- What dependencies exist between affected components
- What the current state of the code is (clean, debt-laden, recently refactored)
- Who has context on the affected areas
- What hidden constraints or business rules exist
In most organizations, this information is distributed across codebases, documentation (if it exists), and the heads of individual engineers. The person doing the estimating rarely has access to all of it.
This is why senior engineers estimate better than junior engineers. It is not because they are smarter. It is because they have built mental models of the system through years of experience. They know where the dark corners are. They know which modules are fragile. They have been burned before by that one service that looks simple but is not.
The problem is that this knowledge is not scalable. It lives in people's heads, and it walks out the door when they leave. Every time a team member moves to a different project or leaves the company, the team's collective estimation accuracy drops.
What teams actually need is a way to make codebase knowledge visible, searchable, and shared. Not a better estimation technique. Better visibility into what they are estimating.
This is the problem Glue was built to solve. By using AI to map codebases, surface dependencies, identify ownership, and provide context about how different parts of the system relate, Glue gives teams the information they need before they estimate, not after they discover it during implementation.
See our effort estimation glossary entry for foundational definitions and frameworks.
Better Estimation Approaches
Given all of the above, here is what actually improves estimation accuracy. None of this is magic. All of it requires investment.
Approach 1: Estimate with data, not intuition.
Use historical data from your own team. How long did similar tasks actually take in the past? Not how long you estimated them at. How long they actually took. Velocity-based estimation that uses real throughput data consistently outperforms gut-feel approaches.
Approach 2: Decompose aggressively.
Large tasks are estimated less accurately than small tasks. That is a consistent finding across estimation research. Break work down until each piece is small enough that the unknowns are manageable. If a single task cannot be decomposed further and still feels uncertain, that uncertainty is a signal that you need to investigate before estimating.
Approach 3: Make codebase context available at estimation time.
This is where most teams have the biggest gap. When your team is estimating a feature that touches the payments service, do they have instant access to: the dependency graph for that service, the recent change history, the test coverage, the known technical debt, and the engineers who last worked on it? If the answer is no, you are asking them to estimate in the dark.
Glue provides this context automatically. During estimation, teams can query the codebase to understand complexity, dependencies, and ownership patterns before committing to timelines. This does not make estimates perfect. But it eliminates the class of surprises that comes from not knowing what you are working with.
Approach 4: Use ranges, not point estimates.
A single number creates false precision. "This will take 5 days" is almost certainly wrong. "This will take 3 to 8 days, with 5 being the most likely" communicates both the expectation and the uncertainty. Stakeholders can plan around ranges. They cannot plan around a number that everyone knows is fiction.
Approach 5: Track accuracy and calibrate.
After each sprint or project, compare estimates to actuals. Not to punish anyone, but to calibrate. Over time, this data reveals systematic patterns. Maybe your team consistently underestimates front-end work by 40%. Maybe back-end estimates are accurate but infrastructure work always takes twice as long. These patterns, once visible, can be corrected.
Approach 6: Separate estimation from commitment.
Estimates should be the engineering team's honest assessment of effort. Commitments should be a negotiation between engineering, product, and business that accounts for estimates plus risk, capacity, and priority. Conflating the two is how estimates get compressed and trust gets eroded.
The goal is not perfect estimation. Perfect estimation in software is a myth, and anyone selling it is not being honest. The goal is estimates that are accurate enough to make good decisions, with uncertainty that is visible enough to manage risk.
Explore Glue to give your team the codebase visibility that makes estimation less of a guessing game.
FAQ
Why are software effort estimates wrong?
Software estimates are wrong primarily because teams are estimating work in systems they do not fully understand. Optimism bias, non-linear complexity, and hidden dependencies all contribute. But the core issue is that the information needed for accurate estimation, such as codebase context, dependency maps, and historical data, is either unavailable or trapped in individual engineers' heads. Without that information, even experienced teams systematically underestimate.
What causes estimation overruns?
The most common causes are unknown dependencies discovered during implementation, underestimation of cross-team coordination overhead, optimism bias in initial assessments, and organizational pressure to compress timelines. Research shows that 66% of software projects experience cost overruns, with an average overrun ratio of 1.8x. These overruns are rarely caused by a single factor but by multiple sources of hidden complexity compounding during execution.
How do you improve estimation accuracy?
The most effective improvements come from: using historical data instead of intuition, decomposing work into smaller pieces, making codebase context visible at estimation time, using ranges instead of point estimates, and tracking accuracy to calibrate over time. Tools like Glue help by surfacing codebase dependencies, ownership, and complexity before estimation begins, reducing the "unknown unknowns" that cause the largest overruns.