By Priya Shankar, Head of Product at Glue

I used to think bad estimates were an engineering problem. After two years of managing a 40-person engineering org's roadmap, I realized bad estimates are an everybody problem with roots that go much deeper than laziness or poor process. Software estimation accuracy has been studied for decades, and the findings are consistent: teams overestimate their ability to predict effort and underestimate the complexity of the work. The gap between estimated and actual effort is not a rounding error. It is a structural feature of how humans think about uncertain work.

This post digs into why software estimates are so consistently wrong, the cognitive biases that drive inaccuracy, the data on how large the variance actually is, and what evidence-based alternatives look like.

The most uncomfortable truth about software estimation accuracy is that the people making estimates genuinely believe they are accurate. This is not dishonesty. It is overconfidence, and it is a well-documented cognitive phenomenon.

Daniel Kahneman's research on the planning fallacy showed that people consistently underestimate the time, cost, and risk of future actions while overestimating their benefits. This applies to software estimation with particular force because software projects are inherently uncertain, and humans are especially bad at estimating uncertain work.

A study from QSM Associates analyzed over 7,000 software projects and found that fewer than 30% delivered within the original estimate. That means more than 70% overran. And this is not because project managers set aggressive targets. It is because the estimates themselves were systematically optimistic.

The Standish Group reports that 66% of software projects experience cost overruns. The PMI Pulse of the Profession puts the waste figure at $109 million per $1 billion invested. These are not fringe studies. They represent the accumulated evidence of the entire software industry.

Why does overconfidence persist despite decades of evidence? Because engineers estimate based on the best-case scenario in their heads. They picture the work going smoothly. They do not picture the undocumented dependency, the database migration that requires a two-hour maintenance window, the code review that uncovers an architectural concern, or the test suite that reveals a bug in the adjacent module.

Airfocus and Gitnux found that only 28% of PMs have an "optimized" product-development process. The other 72% are working with processes that accept estimation inaccuracy as a given, rather than addressing its root causes.

Software estimation is not just a technical challenge. It is a psychological one. Multiple cognitive biases work against accuracy, often simultaneously.

Anchoring bias. When someone suggests an initial estimate, whether it is the PM asking "could this be done in two sprints?" or an engineer offering a rough guess in standup, that number becomes an anchor. Subsequent discussion adjusts around the anchor rather than starting from an independent analysis. Research shows that anchoring effects persist even when participants are warned about them.

Optimism bias. People systematically overestimate positive outcomes and underestimate negative ones. In estimation, this means assuming the work will go smoothly, dependencies will be clear, the code will be clean, and no surprises will emerge. In reality, surprises are the norm.

The illusion of precision. Story points, t-shirt sizes, Fibonacci sequences. Estimation frameworks create a sense of scientific rigor that does not exist. Saying "this is an 8" feels more precise than "I don't really know, maybe a few weeks?" But the actual information content is similar. The framework provides confidence without providing accuracy.

Availability bias. Engineers estimate based on their most recent, most memorable experiences. If their last project in a similar area went smoothly, they estimate optimistically. If it went badly, they estimate conservatively. Neither approach accounts for the specific conditions of the current project.

Groupthink in estimation sessions. Planning poker and similar group estimation techniques are supposed to produce consensus. They often produce conformity. When the tech lead says "I think this is a 5," junior engineers are reluctant to say "I think this is a 13." The result is estimates that converge on a number rather than reflect genuine uncertainty. For why sprint planning is often broken, estimation dynamics are a major factor.

These biases are not fixable through willpower or awareness. They are features of human cognition. The solution is not to make humans better at estimating. It is to reduce the reliance on human estimation by introducing objective data into the process.

The data on estimation variance is sobering.

Research across multiple studies shows a consistent pattern: simple tasks are estimated with reasonable accuracy (within 20-30% of actual), while complex tasks are underestimated by 2x to 4x. Since roadmaps are disproportionately composed of complex work, the aggregate estimation error is significant.

The 4x variance at the high end deserves specific attention. A feature estimated at two weeks that actually takes eight weeks is not a planning inconvenience. It is a quarter-breaking event. When this happens to even one major initiative per quarter, the entire roadmap becomes fiction.

What drives the 4x variance? Three factors consistently emerge.

Unknown unknowns. For complex tasks, the estimation is wrong not because the known work was misjudged, but because significant work was invisible at estimation time. Hidden dependencies, undocumented module behaviors, and technical debt in the critical path add work that nobody anticipated because nobody knew it existed.

Integration complexity. Estimating individual components is relatively straightforward. Estimating how those components interact is where variance explodes. A feature that requires changes across three services, with data flowing between them and error handling at each boundary, has combinatorial complexity that linear estimation cannot capture.

Context switching costs. Engineers do not work on one thing at a time. Meeting loads, code review requests, incident response, and ad-hoc questions from teammates all fragment attention. Gloria Mark's research shows 23 minutes of recovery time per interruption. These costs are never included in estimates but can consume 30-40% of available work time.

For a detailed analysis of why effort estimation in software consistently fails at the task level, the data points to a fundamental mismatch between how humans estimate and how software work actually unfolds.

If traditional estimation is structurally flawed, what works better? The evidence points toward approaches that reduce reliance on human prediction and introduce objective data.

Reference class forecasting. Instead of estimating a task from first principles, look at how long similar tasks actually took in the past. This approach, championed by Kahneman and Bent Flyvbjerg, corrects for optimism bias by anchoring on historical outcomes rather than hypothetical projections. It requires tracking actual effort against estimates over time, which fewer teams do than you would expect.

Probabilistic estimation. Instead of a single number ("this will take two weeks"), produce a range with confidence levels ("there is an 80% chance this takes 2-4 weeks and a 95% chance it takes 2-6 weeks"). Ranges communicate uncertainty honestly and give stakeholders the information they need to plan around risk.

Throughput-based forecasting. Rather than estimating individual tasks, measure how many items of each size the team completes per sprint and use that throughput data to forecast delivery dates. This approach, rooted in velocity-based estimation, uses actual delivery data rather than predictions. It works well for teams with stable capacity and consistent work item sizing.

Codebase-informed estimation. This is the approach with the most room for improvement. When estimators can see the actual code they will be working with, including its complexity, dependencies, test coverage, and change history, their estimates improve significantly. The reason traditional estimation fails is that estimators lack context. Give them context, and accuracy increases.

Glue provides this context by surfacing codebase intelligence at the point of estimation. When an engineer is estimating a feature, they can ask "what modules does this touch?" and "what dependencies are involved?" and get answers grounded in the actual codebase. This does not eliminate estimation uncertainty, but it reduces the unknown unknowns that drive the 4x variance.

The path to better estimation is not better estimation techniques applied to the same limited information. It is better information applied to the estimation conversation. When the team can see the terrain before they start walking, they give you a more accurate arrival time. Not a perfect one, but one that is close enough to plan around.

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Why are software estimates so inaccurate?

Software estimates are inaccurate due to a combination of cognitive biases (optimism bias, anchoring, availability bias) and structural information gaps. Engineers estimate based on incomplete mental models of the codebase, missing hidden dependencies, undocumented module behaviors, and technical debt in the critical path. Research shows that complex tasks are underestimated by 2x to 4x, and fewer than 30% of projects deliver within original estimates. The root cause is insufficient information at the time of estimation, not poor estimation technique.

How do you improve software estimation accuracy?

Improve estimation accuracy by introducing objective data into the estimation process. Use reference class forecasting (historical data on similar tasks), probabilistic ranges instead of single-point estimates, and throughput-based forecasting. Most importantly, give estimators visibility into the actual codebase, including complexity metrics, dependency chains, and code health indicators for the modules they will be working in. Better information produces better estimates without requiring better estimation skills.

What is a reasonable estimation accuracy target?

For most software teams, achieving estimates within 30% of actual effort on a consistent basis represents strong performance. Expecting exact accuracy is unrealistic given the inherent uncertainty of software work. Focus on reducing the variance over time rather than hitting a specific accuracy target. Track your actual-versus-estimated ratio for each project and use that data to calibrate future estimates. Teams that track this metric consistently see their accuracy improve over quarters as they learn from the data.

Glue vs Productboard: Codebase Intelligence Meets Product Management

By Priya Shankar, Head of Product at Glue

If you are searching for a Productboard alternative, the first question to answer is what problem you are actually trying to solve. Productboard is a product management platform built for prioritization, roadmapping, and customer feedback aggregation. Glue is an AI codebase intelligence platform that gives product teams visibility into their software without reading code. These tools serve different functions, and understanding the distinction will save you from choosing the wrong one.

I have used Productboard as a PM at a Series C SaaS company, and I now lead product at Glue. I know both tools from the inside. This comparison is honest about where each one excels and where it falls short.

Productboard launched in 2014 and has grown into one of the most adopted product management platforms, used by over 6,000 companies. It focuses on capturing customer feedback, structuring product priorities, and communicating roadmaps. Its strength is the demand side of product management: understanding what customers want and deciding what to build next.

Glue launched to solve a problem Productboard was never designed to address: giving product teams visibility into the codebase itself. While Productboard collects customer signals and organizes them into a prioritized backlog, it has no connection to the software your team has built. The effort scores are manual. The feature lists are what someone typed in. The gap between "what customers want" and "what the system can actually deliver" remains wide.

The two tools sit at different points in the product management workflow. Productboard helps you decide what to build. Glue helps you understand what building it actually involves.

Productboard is one of the most polished product management platforms available, and it earned that reputation for good reasons.

Customer feedback management. Productboard's feedback portal and insight aggregation system are excellent. It captures feedback from multiple channels, links insights to features, and helps PMs see patterns across their user base. If understanding customer needs is your primary challenge, Productboard handles it well.

Prioritization and roadmapping. Productboard provides structured prioritization frameworks, customizable scoring, and roadmap views that align well with how product organizations communicate plans to stakeholders. The portal feature allows external stakeholders to see and vote on roadmap items.

Workflow maturity. Productboard has been in market long enough to have refined its workflows. The integration with Jira is solid. The UI is clean. The learning curve is manageable. For teams that need a traditional product management tool, it delivers.

Glue solves a fundamentally different problem than Productboard. Productboard helps you decide what to build based on customer input. Glue helps you understand what you have already built and what building something new actually involves.

Codebase visibility for product teams. Glue connects to your Git repository and uses AI to read, parse, and explain your codebase. Product managers can ask questions like "how does the checkout flow work?" or "what features do we have?" and get answers grounded in actual code. Productboard has no access to or understanding of your codebase.

Effort estimation grounded in code. When you prioritize a feature in Productboard, the effort score is typically a manual input, a guess from engineering. With Glue, you can surface the actual complexity: which files need to change, what dependencies are involved, and where technical debt exists in the affected modules. This transforms effort scoring from opinion to data.

Feature discovery. Productboard tracks features you explicitly add. Glue discovers features that exist in the codebase, including features the team has forgotten about or never documented. For organizations that have grown through acquisitions, inherited codebases, or years of accumulated development, this discovery capability fills a gap that traditional PM tools cannot address.

For more on how Glue helps product managers specifically, the use cases extend to spec writing, competitive gap analysis, and knowledge risk identification.

Choose Productboard when your primary challenge is customer feedback management, feature prioritization based on market input, and roadmap communication to stakeholders. If you need a system of record for product decisions that integrates with your customer-facing feedback channels, Productboard is purpose-built for that workflow.

Productboard is also the right choice if your team is small enough that codebase complexity is not yet a problem, or if your engineering team is closely aligned enough that codebase visibility is not a bottleneck.

Teams that have standardized their product workflow around Productboard's driver-based prioritization model will find its scoring frameworks genuinely useful for making trade-offs visible to stakeholders. The integration with tools like Intercom and Zendesk makes it particularly strong for teams that receive high volumes of customer feedback through support channels.

Choose Glue when your primary challenge is understanding your own software. If PMs are spending hours in Slack threads asking engineers how things work, if estimates are consistently wrong because nobody sees the full system, if roadmaps slip because hidden dependencies surface mid-sprint, Glue addresses the root cause.

Glue is especially valuable for organizations with large or complex codebases, teams that have experienced significant engineer turnover (and lost tribal knowledge), and PMs who make decisions about systems they cannot see. For a deeper understanding of how code intelligence platforms fit into the product tech stack, the category is broader than any single tool.

Yes, and for many teams, using both is the right approach. Productboard handles the demand side of product management: what customers want, how to prioritize it, and how to communicate the plan. Glue handles the supply side: what the system can deliver, what it already contains, and what building something new actually requires.

The combination closes the loop that neither tool closes alone. Productboard tells you what to build. Glue tells you what building it involves. Together, they give product teams a complete picture: customer demand matched against codebase reality.

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Is Glue a replacement for Productboard?

No. Glue and Productboard solve different problems. Productboard is a product management platform for prioritization, roadmapping, and customer feedback. Glue is a codebase intelligence platform that gives product teams visibility into their software. Many teams use both: Productboard for the demand side (what to build) and Glue for the supply side (what building it involves). They are complementary rather than competing.

What is the best Productboard alternative for technical teams?

The best alternative depends on what you need. If you need a product management tool with better Jira integration, consider Aha! or Airfocus. If your primary challenge is understanding your codebase and getting better estimation data, Glue addresses a different gap that Productboard does not cover. For teams where codebase visibility is the bottleneck, Glue provides the technical context that traditional PM tools lack.

Can product managers use Glue without technical knowledge?

Yes. Glue is specifically designed for non-technical stakeholders. You interact with it through natural language questions rather than code. Ask "what features do we have?" or "which files would change for this project?" and get answers in plain English with file-level references. No coding knowledge is required to extract value from the platform.