Managing Uncertainty In Agile Story Point Estimation
The Challenge of Accuracy in Story Point Estimation
Agile estimation using story points has become a common technique for predicting effort and delivery capability in software development. By assigning a dimensionless story point value to each user story in the backlog, agile teams estimate the overall relative level of effort required to complete each item. However, while necessary for planning iterations and releases, accurately estimating at the story level can be extremely difficult.
Unlike time-based estimation approaches, story points do not promise accuracy in an absolute sense. Rather, they aim to gauge effort using relative units that depend heavily on analogy-based reasoning with little concrete data behind each estimate. With user stories often representing functionality that has never been built before, unpredictability reigns supreme.
Without historical references to ground assumptions, agile teams must embrace uncertainty as part of the fundamental limitation in forecasting a future based primarily on expert judgment. Understanding the inherent challenges is the first step toward improving story point estimates over time.
Inherent Unpredictability
By their very nature, user stories capture requirements at a high level, often articulated primarily for discussion with product owners and users more than engineering implementation. As such, much unforeseen effort lives below the surface until deeper investigation occurs during sprint execution.
Areas like technical spikes, investigation into third party libraries, environment setup, testing coverage, and addressing bugs and defects all represent work that often goes underestimated upfront. Yet these tasks can easily consume more than 50% of overall effort to deliver the stated user value.
Missing Knowledge
In rapidly changing technical landscapes, few agile practitioners ever achieve omniscience. Even industry experts find it challenging to become fully knowledgeable across the bewildering array of technologies and patterns that continue to emerge. With constant churn in languages, frameworks, platforms, and paradigms, unpredictability prevails.
Throw people together on an agile team with varied backgrounds, knowledge gaps only compound. What appears simple to one engineer based on past experience may require extensive scoping analysis from another less familiar. Such uncertainties mean story points carry intrinsic subjectivity during estimation sessions.
Changing Requirements
While agile methodologies espouse building software iteratively in a flexible manner, the reality remains that changing requirements frequently inject uncertainty into delivery confidence. Even user stories expressing clear needs upfront get reprioritized or modified mid-stream, altering initial assumptions.
Various agile practices aim to freeze scope on user stories planned into a time-boxed sprint. But over longer time horizons for roadmaps and releases, change remains the only constant. What gets defined as negotiable today may shift tomorrow as business objectives evolve or technical hurdles emerge.
Common Sources of Inaccuracy
In building knowledge to enhance accuracy, agile teams can identify the most common areas today where estimates prove unreliable based on historical data:
- Lack of knowledge or expertise with new languages or frameworks
- Missing non-functional requirements like scalability or security
- Unidentified dependencies on other teams or shared services
- Under estimation of testing needs for coverage and confidence
- Insufficient work allocated for environment configuration
- Failing to size defects or invest in technical debt reduction
While unknowns always loom as estimates form initially, tracking sources of inaccuracy over time provides crucial feedback for improving future predictions and planning.
Gaps in Team Experience
When organizations adopt new technologies like blockchain or machine learning, experts rarely exist on staff at first. Ramp up on new languages and frameworks thus consumes effort unseen. Technical spikes into uncharted territory easily undermine initial estimates as teams build competency.
Over time, recording when inadequate knowledge of tools or techniques impacted velocity allows organizations toquantify levels of uncertainty versus stable domains. Such data points to when investing more in training or hiring ahead of need may accelerate outcomes.
Non-Functional Surprises
By focusing chiefly on user value, agile teams often overlook non-functional aspects of software until later in delivery lifecycles. However, huge effort can hide within stories to scale systems, secure data, or ensure regulatory compliance.
Retrospectively tracking when non-functional needs exceeded estimates provides crucial insights for planning. Such data can inform both relative story point allocations and overall team capacity predictions based on historical velocity in these domains.
Dependency Risks
With engineering teams relying ever more heavily on both internal and external services to integrate solutions, unidentified dependencies inject uncertainty into many initiatives. Unknown bottlenecks in shared databases, APIs, or vendor contracts frequently hamper agile teams, consuming unplanned cycles.
Watching dependency risks over time provides ammunition to secure service level agreements internally between teams while also budgeting additional time for external integrations. Such buffering into estimates counters surprises down the line.
Strategies to Improve Estimation
While uncertainty forever challenges story point predictive power, agile teams can employ various strategies to enhance accuracy over time:
- Leverage statistical models based on historical team delivery data
- Break down epics and themes into smaller, testable stories
- Explicitly plan for defect resolution and technical debt repayment
- Allocate time proportional to risk levels and unknowns
- Baseline initial estimates then refine over multiple iterations
- Use ranges or distributions rather than absolute numbers
Such techniques inject both science and flexibility into the art of estimation. Over iterations, weeks, and months, accuracy improves as uncertainties shrink.
Historical Analytics
By recording story level estimates along with actual effort consumed for each sprint, teams gather crucial reference data for honing predictions. Sophisticated models can derive probability distributions and uncertainty ranges relative to historical velocity across the domains delivered.
Referencing such quantitatively derived estimates provides starting positions grounded more in reality than blind guesswork. Over time, tuning model inputs against overruns or changing team composition allows for tightening statistical reliability.
Story Decomposition
Large, complex stories with many unknown details provide little accuracy in sizing initially. Breaking down such epics into multiple small, testable stories over time incrementally reduces uncertainty as implementation begins.
Structured story mapping workshops can help identify underlying needs and scope boundaries to support decomposition. Additionally, applying story splitting patterns with success criteria checklists ensures cleanly separated concerns to limit unexpected entanglement across stories down the line.
Embrace Uncertainty
No amount of diligence in scoping unknowns upfront prevents surprises down the line. Changing human needs coupled with emerging technical realities ensure continual change and uncertainty. Attempts at perfection are futile.
Rather than solidify estimates based on initial analysis alone, progressive refinement across multiple estimation sessions incorporates new learnings over time. Ranges and probability models also flex as volatility metrics confirm levels of chaos versus predictable project segments.
Managing Uncertainty with Estimation Ranges
To counter inherent ambiguity in predicting level of effort, agile teams increasingly express estimates as ranges rather than discrete story point values. Such practices communicate uncertainty dimensions right in relative sizing.
Common techniques include using intervals like 1-5 points or 2-3 weeks to denote imprecision levels. Likewise hypothetical ideal days counter uncertainty with planned buffers: for example, 5 ideal days +/- 2 days.
Modeling Distributions
For mathematically inclined teams, modeled probability distributions take ranges further to predict likelihood across potential outcomes. Popular approaches include triangular, beta, and gamma distributions.
Such computational techniques require historical sprint data fitting story level actuals to size parameters. But the rigor imposes discipline around tracking error rates, allowing for narrowing uncertainty percentages over time as inputs fine tune.
Updating Models
Whether using basic ranges or probabilistic forecasts, estimation models require continuous feeding of new facts from each sprint. Comparing recent reality against predicted distributions highlights needed adjustments in spread widths.
Over consecutive iterations, uncertainty quantification metrics like standard deviation or confidence intervals may tighten or expand from initial baselines. Such statistics demonstrate progress in precision while also revealing persistent volatility in particular domains.
Tracking Velocity Trends
While individual story point accuracy carries intrinsic limitations, velocity metrics tracking overall team throughput over iterations provide crucial data for estimating releases. By taking average delivery rates across sprints, smoothed predictability transcends isolated uncertainties.
Velocity lines the foundation for quantified planning. The therapies number of points a team historically completes in a timeframe offers data-driven inputs for projecting schedule estimates based on backlogs. Such empirically grounded forecasts bound chaos with plausible delivery ranges.
Updating for Process Changes
However, teams rarely sustain static velocity over long horizons. Process improvements or deteriorations along with team member changes inevitably impact average throughput. Thus tracking velocity requires continuous updating for shifts up or down.
Other factors like company reorganizations, technology upgrades, and rising technical debt also influence deliverability overtime. Only quantifying such impacts keeps velocity relevant as a planning aid rather than regressing to old assumptions.
Guide Ranges
While velocity lends intuitive aid for release train planning by sizing backlogs against reliability rates, curiosity best couples such forecasts with uncertainty ranges. After all, variation lives within even steady-state systems.
Using ranges like 10-15 points per sprint or 7-9 months for a hundred point backlog communicates pragmatism amidst variability. Such calibrated equations of ranges bound volatility for stakeholders while allowing flexibility for agile teams as learning unfolds.
Ongoing Refinement
Agile estimation leverages progressive refinement of fuzzy upfront guesses via fast feedback loops. By baselining initial story points then revisiting estimates multiple times prior to sprint commitment, accuracy improves through successive approximation.
Just as iterative development teases out deeper truths over incremental constructions, so iterative estimation reveals unseen realities and dependencies over time. Embracing such learning curves counteracts human inclinations towards assumed completeness too early.
Timebox Analysis
Build repeating analysis blocks into iteration planning workflows ensures necessary revisiting of early estimates as understanding deepens. Whether designated refinement sessions or informaldesk checks, such purposeful activities convert estimates from one-time guesses into evolving models.
Timeboxing tasks also prevents endless theoretical debate devoid of sprint priority. Estimation accuracy proves no value unless supporting predictable deliverables to stakeholders anchored in reality not fantasy.
Update with New Facts
As sprints expose hidden scope through task breakdowns, such empirical data feeds back into story point reassessments. Any architectural spikes, prototype experiments, or design workshop learnings all supply anchor points for successive approximation.
Updating estimates then provides velocity calculations grounded in technical feasibility rather than just product priorities. Such realism better aligns capability with desires across the constellation of teams, technologies, and techniques that deliver value.
Example Code for Probability Calculations
For teams taking advanced quantitative approaches to estimating software engineering effort, probability density functions provide rigorous models for predicting likelihood across ranges. Popular open source stats languages like R or Python suit such analytical needs:
# Sample Python Code for Triangle Distribution from scipy import stats import matplotlib.pyplot as plt # Input lowest, most likely, and highest effort low = 4 mode = 8 high = 16 dist = stats.triang(c=mode, loc=low, scale=high-low) # Generate probability distribution x = [i*.1 for i in range(low*10,high*10+1)] y = dist.pdf(x) # Plot pdf plt.plot(x, y) plt.show()
Such computational techniques enable quantitative rigor and visual inspection of the uncertainty ranges and probability curves for weighting estimates.
Summary
While no silver bullet eliminates inherent ambiguity in predicting unseen software engineering efforts, leveraging agile estimation approaches positions organizations to bound unpredictability.
Through tracking volatility sources, updating velocity trends, decomposing stories, modeling uncertainty ranges, and progressively refining, teams counter uncertainty with pragmatic flexibility needed to navigate complexity. Such empirical agility conquers volatility over time via gathered wisdom in the face of imperfect foresight.
