Psychological Benefits Of Story Points Over Hour Estimates
Software development teams often struggle with making accurate estimates for completing work items and projects. Traditional time-based estimates using hours or days have several flaws that frequently lead to missed deadlines, rushed output, and subpar quality. Story points provide a superior alternative for teams to estimate relative complexity of product backlog items while supporting flexibility, learning, and continuous improvement.
The Flaws of Hour Estimates
Estimating work items in hours or days seems intuitive, but frequently causes more harm than good for development teams. Some key deficiencies with time-based estimates include:
Inaccuracy due to unknowns
Software projects contain inherent uncertainties regardless of the skills and experiences of the team. Complexities arise from vague requirements, evolving technologies, integration issues, testing obstacles, and more. By focusing strictly on time duration, hour estimates fail to account for these unknowns, leading to haphazard guesses that tend to be wildly incorrect.
Encourages padding estimates
The natural human tendency when faced with uncertainties is to overestimate to be “safe.” Software teams thus end up padding their hour estimates, leading to inflated timelines. Such padding causes loss of trust from business partners when teams consistently deliver earlier than estimated. Parkinson’s law also kicks in, with work expanding to fill the estimated hours even if unnecessary.
Less flexibility to change scope
Software projects exist in complex, fluid environments requiring nimble adaptations. Lengthy hour estimates fail to account for new user stories, changes in priority, spikes, or other mid-sprint alterations. Development teams feel locked into delivering the pre-estimated hours, unable to dynamically adjust scope to meet changing needs.
The Benefits of Story Points
Story points provide several key psychological and process benefits over hour estimate for development teams. Three major advantages include:
Focus on relative complexity, not time duration
Instead of estimating in hours or days, story points gauge the relative complexity of a task compared to other items. Typical scales include t-shirt sizing (S-M-L-XL) or Fibonacci sequences. This simplifies estimation conversations by removing time units. It also accounts for uncertainties by assessing knowns and unknowns then mutually calibrating them against other completed work.
More accuracy through historical data
Over multiple sprints, teams build up “velocity” data on how many story points they can reliably complete per sprint. This velocity acts as a baseline for assessing capacity for upcoming sprints. New items are estimated in story points then plugged into the team’s historical capacity. Such data-driven projections improve accuracy substantially compared to guessing hour estimates.
Adaptability to new information
With ongoing sprints, the development team’s understanding of the project evolves based on testing, stakeholders needs, technical challenges etc. Hour estimates lock in assumptions and don’t allow incorporation of new learnings. Story points are open to re-estimation at any time, allowing teams to update size assumptions as more knowledge becomes available.
Implementing Story Points
Adopting story point estimation requires some key steps to build team alignment. Critical actions include:
Define point scale
The team selects a story point scale based on project complexity, uncertainty levels, and team size. Common scales are Fibonacci (1, 2, 3, 5, 8, 13…) or t-shirt sizing (XS, S, M, L, XL). The scale should have enough granularity for differentiating item complexity but not be overly precise.
Estimate backlog items relatively
In sprint planning, teams estimate each user story or product backlog item based on its anticipated complexity compared to other stories. Members use “planning poker” to discuss assumptions and unknowns then align on relative size assessment against items already completed.
Track velocity over multiple sprints
By summing all story points completed per sprint, teams establish reliable velocity baselines. This velocity metric anchors future story point estimates into the team’s proven monthly/sprint capacity based on historical data.
Addressing Common Concerns
Certain objections arise frequently from teams new to story point estimation. Some can be mitigated by:
Handling part-time team members
For members working partial sprint hours, convert their capacity into a full-time equivalent figure based on past velocity. Estimate and plan sprint scope using the member’s full-time story point capacity to simplify tracking.
Avoiding misuse as productivity metrics
Inform all stakeholders that story point velocity measures forecasting accuracy, not individual productivity. Make clear that higher velocity comes from refining estimates – not pressuring team members to work longer hours. Maintain team focus on shipment value over velocity scoring.
Succeeding with Story Points
Teams new to story points often struggle in early sprints but rapidly improve through three key actions:
Anchor estimates with historical data
Leverage completed story points from past sprints to guide estimation of new items. Seek technical spikes of uncertain work to build benchmarks for future user stories containing similar complexities.
Re-estimate frequently
If new information emerges mid-sprint impacting scope or complexity, teams should update story point estimates of unfinished items. Such re-estimations prevent hidden inflations from prior guessing and enhance precision.
Empower teams to gauge own capacity
Trust the development team to assess how many story points they can complete per sprint rather than imposing targets. With time, the team’s velocity stabilizes, enabling them to pull in the optimal amount of work to maximize value delivery at a sustainable pace.
