Understanding Relative Effort Using Fibonacci Sequence Story Points

Defining Relative Effort

The concept of relative effort provides a framework for comparatively estimating the work required to complete tasks in software development. Unlike absolute effort measures, relative effort scoring aims to gauge the difficulty of a user story or feature in relation to other items being worked on.

Using a relative scoring system allows product owners and teams to discuss stories from an effort perspective without needing to estimate exact hours upfront. Comparing the hypothetical workload of one story against other known stories enables more accurate planning.

Explaining the concept of relative effort for estimating tasks

Relative effort scoring centers around ranking work items based on predicted workload instead of time duration. For example, a story worth 5 points would be seen to require more effort than a 2 point story, but the exact hours are not predetermined.

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By scoring each item according to its difficulty compared to other items, the team can prioritize the backlog knowing which stories may take more work without precise estimates. This grounds conversations around feasibility and tracking velocity rather than unconfirmed hour counts.

Comparing absolute vs relative effort frameworks

Absolute effort estimation assigns each work item an exact time value such as hours or days. This requires predicting the precise workload upfront before fully understanding the story’s details.

Relative scoring uses points along a scale to determine effort based on comparisons within the backlog. By assessing one story’s predicted workload against other known stories, the indications of effort become grounded in evidence, not hypothetical duration.

While absolute estimations forecast effort as an isolated value, relative story points offer data-driven assessments calibrated through past sprint experiences. As new stories are evaluated against completed ones, the indications of effort improve.

Using the Fibonacci Sequence

Introducing the Fibonacci sequence (0, 1, 1, 2, 3, 5, 8, etc.)

The Fibonacci sequence is an integer sequence beginning with 0 and 1 where every subsequent number equals the sum of the previous two numbers. This mathematical sequence progresses as 0, 1, 1, 2, 3, 5, 8, 13, 21 and so on, following an exponential growth curve.

Fibonacci numbers provide a broad scale for graded measurements while balancing range sensitivity with cognitive load. Using Fibonacci points for relative estimation allows nuanced scoring differentiation without endless decimals or fractions.

Mapping Fibonacci numbers to relative effort levels

In Fibonacci agile estimation, the numbers in the sequence are mapped to story points indicating progressive levels of effort. Higher Fibonacci numbers imply more development work compared to smaller values.

Commonly, the baseline task is set at 1 point. From there, the subsequent Fibonacci numbers serve as exponential multipliers for categorizing more complex stories based on predicted workload.

For example, an 8 point story would be conceived to require significantly more effort than a 3 point item. The Fibonacci scale enables this wide spectrum for differentiating item complexity.

Setting baseline stories to 1 point

To implement Fibonacci estimation, teams should first identify a baseline reference story worth 1 point. This establishes the foundation for gauging complexity and effort for the project backlog.

Setting a 1 point benchmark requires determining a typical “average” story the team feels they fully understand the effort for. Future items can then be scored upwards or downwards on the Fibonacci scale based on direct work comparisons.

As new stories are evaluated against this 1 point baseline, their scores consistently communicate proportional effort estimates across the team. This informs planning and tracking capacities relative to known items.

Calculating Story Points

Breaking down stories into smaller components

For more accurate scoring based on relative effort, agile teams should decompose user stories into smaller functional pieces reflecting single units of work.

Breaking stories down isolates the core components requiring implementation. Rather than guesstimate effort holistically, scoring can focus on precise elements in isolation to improve calibration.

Additionally, finer details often surface previously hidden complexities that contribute to development work. Isolating effort at component levels captures these intricacies in estimates.

Comparatively scoring components on effort

With user stories broken into bite-sized pieces, each component can be judged independently against the 1 point baseline item. This isolates comparisons to precisely focus scoring.

By pitting a single component against the benchmark story, the relative effort of that chunk is easier to discriminate. The points assessed indicate solely the differential projected for that narrow element’s workload.

These small work items receive isolated scores based on focused comparisons. Granular effort scoring improves accuracy over broad guesstimates of larger stories.

Summing component scores into story points

After judging isolated chunks on comparative workload versus baseline items, these discrete scores can be re-aggregated to the full story level. Summing the individually assessed segments yields the total points for epic stories.

Since effort scoring occurred at microscopic component layers, the composite assessment benefits from these narrow evaluations. The cumulative points reflect targeted analysis rather than high-level assumptions.

Deconstructing then reconstructing stories distills effort scores through filtered comparisons. The final sum inherits these targeted insights on relative workload.

Interpreting Story Points

Recognizing higher points imply more effort

A core tenet of Fibonacci scoring rests on higher point values indicating increased effort, not precise duration. Doubling from 5 to 10 points shows the latter story requires substantially more predicted work.

However, the exact hours remain unknown. Perhaps 10 points takes 15 hours, or 22 hours, or more. The exact conversion between points and hours depends on team velocity and the item itself.

Nonetheless, points clearly reveal proportional effort differences between stories. This informs iteration planning regardless of unconfirmed time durations.

Understanding relative, not absolute effort measures

Points scored through Fibonacci estimation exclusively convey relative effort signals based on comparison. 10 points does not necessarily mean 10 straight hours of work – it implies more effort than a 5 point item.

There are no absolute declarations around defined timeframe at any score. Solely the proportional relationships matter, where doubling points doubles hypothetical working time.

Recognizing estimates as relative effort indicators liberates teams from making unreliable time commitments and focuses planning on data-driven complexity assessments.

Accounting for team velocity when planning

While direct work comparisons provide reliable signals on relative effort, translating points into timeframe depends on the throughput velocity of the development team.

The hours per point will vary from team to team based on skills,knowledge, tools, and other constraints affecting workflow. This gets captured in velocity metrics that gauge completed points per iteration.

Velocity contextualizes estimated effort levels into timeframe values, enabling teams to accurately plan and forecast. Changes in skills or workflow reshape velocity, keeping planning grounded in evidence not guesses.

Applying Story Points Effectively

Using points for iteration planning and tracking

Story points fuel sprint planning by highlighting scope based on projected effort rather than unreliable time estimates. Points inform the team’s capacity to deliver within constrained iterations.

Likewise, points help quantify velocity tracking how much effort the team historically completes per sprint. This grounds future plans in data not conjecture around overstated hours.

With benchmarked story points, product owners can shape upcoming iterations for sustainable execution based on factual throughput capabilities from past cycles.

Re-calibrating points to maintain relativity

For continued planning reliability, agile teams should periodically reassess the 1 point baseline when skills, tools, or processes shift significantly.

This re-calibrates the relativity of story scoring to reflect the team’s updated workflow constraints. Revised 1 point benchmarks preserve proportionality as improvements stretch previous capacities.

Revisiting points ensures estimates speak accurately to current velocities and iterations stay planned around fresh effort signals that teams feel reflects their work, not old mismatched scoring.

Ensuring consistency in scoring process

Relative estimation requires consistent application for reliable signals on comparative effort across stories. Shifting perspectives introduce noise obscuring the proportional relationships between scores.

Shared understanding of the 1 point story and what effort means for the team provides a common lens for scoring work. This grounds conversations in steady context, enabling cohesion through volatility.

Consistent scoring habits preserve the core value of relative estimation: showing effort differences between work items based on informed comparisons, not isolated guesses.

Example Codes

Python function to generate Fibonacci sequence


def fibonacci():
  num1, num2 = 0, 1
  while True: 
     yield num1
     num1, num2 = num2, num1 + num2

JavaScript function to calculate story points


function getStoryPoints(components) {

  let points = 0;

  components.forEach(component => {
    points += component.score;
  })

  return points;
}

Key Takeaways

Story points enable agile teams to gauge relative effort between product backlog items through consistent comparison-based scoring along the Fibonacci sequence scale. Key highlights include:

Story points convey relative effort differences between work items
The Fibonacci sequence provides a spectrum for balanced estimation
Consistent benchmarking against a 1 point reference story enables coordinated scoring