Software Delivery · Requirements · Testing

Building Better Software Starts With Clearer Stories

A practical way to create shared understanding early, keep work manageable, reduce avoidable rework, and make software delivery calmer and more predictable for everyone involved.

TLDR

Most software teams are trying to do good work. The trouble usually starts when a big idea reaches the team before it has been reduced into a small, shared, testable understanding of what should happen.

That gap creates assumptions. Assumptions create rework. Rework creates schedule pressure, context switching, complicated code, emergency fixes, and eventually a lot of unnecessary stress.

The good news is that this is not a new problem. Software teams have spent decades developing practical ways to deal with it: collaborate early, keep work small, make expected behavior concrete, limit work in progress, test the behavior that matters, and deliver in increments that can be inspected and adjusted.

A Very Common Software Problem

Building software is hard because we are usually trying to create something that does not exist yet.

Stakeholders have an idea of what they want. Product turns that idea into priorities. Design turns it into an experience. Engineering turns it into a working system. Along the way, everybody is making decisions with incomplete information.

That is normal.

The problems start when the organization treats uncertainty as if it does not exist.

A large feature may be described as if it is already fully understood. A date may be treated as a promise even though the scope is still changing. Several priorities may become "urgent" at the same time. A development team may start five things at once because everyone understandably wants to see progress on their request.

Then the system begins to work against the people inside it.

• Product spends more time answering emergencies than shaping the next useful outcome.
• Design revisits work because implementation exposed a state nobody had considered.
• Engineering jumps between partially finished features and accumulates context switching.
• Managers have a harder time knowing what is actually close to done.
• Stakeholders hear optimistic dates that become harder and harder to trust.
• Everyone feels busy, but completed value moves more slowly than expected.

I do not think this is usually caused by lazy people or bad intentions.

It is usually a system problem: ambition, capacity, priority, and definition of done have drifted out of alignment.

Once that happens, even talented people can spend a lot of time working very hard without getting the calm, predictable delivery everyone wants.

The Opportunity

The encouraging part is that many of these problems can be reduced surprisingly early.

Before code is written, the team can agree on a few basic things:

• Who needs this?
• What problem are we actually solving?
• What is the smallest useful behavior we can deliver?
• What are we intentionally not doing yet?
• What does success look like?
• How will we test it?
• What needs to happen before this work can start?
• What should happen after it is released?

When those answers are visible, a lot of downstream work becomes easier.

What the whole team gets back

Product & Management

• More realistic planning
• Clearer tradeoffs
• Easier stakeholder conversations
• Better visibility into what is actually done

Design

• Fewer surprise states
• Clearer scope boundaries
• More focused reviews
• Less late-stage rework

Engineering

• Less context switching
• Smaller changes
• Cleaner tests and code reviews
• Fewer emergency fixes

The goal is not to slow people down with process.

The goal is to spend a little more effort creating shared understanding when change is cheap, so that we spend much less effort correcting misunderstandings after they have become code.

This Is Not a New or Personal Idea

These practices come from a long history of software teams trying to solve the same basic delivery problems.

Modern approaches differ in terminology, but many of them converge on the same themes:

• Business and engineering should collaborate closely.
• Work should be delivered in smaller increments.
• Teams need a sustainable pace.
• Backlog items should be clear enough to support useful planning.
• Started-but-not-finished work should be controlled rather than allowed to grow without limit.
• Concrete examples are a powerful way to remove ambiguity from expected behavior.
• Teams should inspect results, learn, and adjust rather than pretending the future can be perfectly predicted.

Those ideas show up repeatedly across established Agile, Scrum, Kanban, behavior-driven development, automated testing, and continuous-delivery practices.

Useful references: Principles behind the Agile Manifesto ↗, The Scrum Guide ↗, Kanban Guides ↗, and the Gherkin reference ↗.

Where Things Usually Get Difficult

A ticket can look detailed and still leave the most important questions unanswered.

Here is a completely generic e-commerce example:

Example: one ticket doing too much

Improve the checkout experience.

- Let customers choose a saved delivery address.
- Add estimated delivery dates.
- Add a promo-code box.
- Add gift receipt support.
- Show alternative payment methods.
- Make it work on mobile.
- Display validation errors.
- Use the new checkout mockups.
- Track checkout analytics.
- Make sure existing customers still work.

There is nothing unreasonable about wanting all of those things.

The problem is that they do not represent one behavior. They contain several independent product decisions, data flows, visual states, technical integrations, and testing concerns.

For example, "saved delivery address" alone raises useful questions:

• Which customers have saved addresses?
• Which address is selected by default?
• Can the customer edit an address during checkout?
• What happens if an old address is no longer valid?
• Can an address be deleted while it is being used by an order?
• What happens to customers who created accounts before saved addresses existed?
• Does the shipping estimate change when the address changes?

If all of those decisions are hidden inside one giant "checkout" ticket, the team has to discover them while implementing it.

Note: The solution is not to turn the ticket into a 20-page specification. The solution is to separate the work into useful behaviors and make the important decisions visible at the right time.

The Real Question

When a team picks up a story, everybody involved should be able to answer one basic question:

"How will we know that this specific piece of work is correct?"

If Product, Design, Engineering, and QA would all answer that differently, the story probably needs another conversation before implementation.

That does not mean there can be no uncertainty.

It means we want uncertainty to be known, instead of accidentally turning into requirements while somebody is coding.

Start With a Simple Hierarchy

I like organizing work from a large product idea down to concrete behavior:

Initiative / Capability → Epic → Feature / User Story → Business Rule → Scenario

The exact names can vary by organization. The important part is the direction.

Large ideas are progressively broken down until we have something small enough to:

• understand
• prioritize
• build
• test
• demo
• release
• learn from

Example: E-commerce checkout

Capability: Online Purchasing

Epic: Checkout

Possible stories:

• Customer can select a saved delivery address.
• Customer can see an estimated delivery date for the selected address.
• Customer can apply a valid promotion code.
• Customer receives a useful message when a promotion code is invalid.
• Customer can request a gift receipt.
• Customer can choose from eligible payment methods.

Those behaviors are related, but they do not have to be implemented as one giant unit.

Smaller stories let us finish one useful behavior, learn from it, and then move to the next.

One Thing at a Time Is a Feature, Not a Limitation

Organizations naturally want progress on many important things at once.

Unfortunately, starting more work does not automatically mean finishing more work.

Every partially finished item creates additional coordination:

• another branch or pull request to maintain
• another set of assumptions to remember
• another stakeholder waiting for an update
• another test environment state
• another context engineers and designers have to reload into their heads

That is why controlling work in progress is such a recurring idea in modern delivery systems.

Finishing a small number of things is usually more valuable than making partial progress on a large number of things.

A practical backlog should therefore make priority real.

If everything is priority one, the team does not actually have a priority.

The backlog should make it possible for engineers to focus, finish, validate, and move on rather than continually switching between unrelated work.

Describe What the User Does

For an individual story, I like this familiar format:

As a <specific user or actor>
I want <a capability or behavior>
So that <I get a useful outcome>

The words themselves are not magic. Their value is that they make three things explicit:

1. Who is doing this?
2. What do they need to do?
3. Why does it matter?

Be specific about the role

"As a user" often hides important differences.

These are more useful:

• As an anonymous shopper...
• As an authenticated customer...
• As a customer-service representative with refund permission...
• As an organization administrator...
• As an external payment service...

The role matters because permissions, visible information, available actions, and error behavior can all change depending on who is interacting with the system.

Use Concrete Scenarios Instead of Long Lists

For acceptance criteria, Gherkin gives the team a simple vocabulary for describing behavior:

Given <a known starting condition>
When <an action or event happens>
Then <an observable result happens>

And and But can add additional context without turning the scenario into a paragraph.

Example: selecting a delivery address

Scenario: Customer chooses a saved delivery address

  Given the customer is signed in
  And the customer has two saved delivery addresses
  And the first address is currently selected

  When the customer selects the second address

  Then the second address is shown as the selected delivery address
  And the order's delivery estimate is recalculated for that address

That is much easier to discuss than:

"Add saved addresses to checkout."

Tip: I try to make the Then something a user, external system, or automated test can observe. Acceptance criteria should describe behavior, not dictate the internal implementation.

A Story Template I Would Actually Use

I do not want every ticket to become a giant form that people fill out mechanically.

That creates paperwork without necessarily creating understanding.

I prefer two layers:

1. A small required core that gives the team enough context to understand the work.
2. Conditional considerations that are included when they matter.

Copy / Paste Story Template

# [WORK ITEM ID] Story title

TYPE
Feature | Bug | Chore | Spike

PARENT
Capability:
Epic:
Related feature:

USER / ACTOR
Exact user role or external system involved.

STORY
As a <specific user or actor>
I want <capability or behavior>
So that <user/business outcome>

WHY
Brief explanation of the problem, user need, or business outcome.

SCOPE
What this story includes.

OUT OF SCOPE
Important adjacent behaviors explicitly excluded from this story.

DESIGN / REFERENCES
Design:
Architecture/documentation:
API documentation:
Related research:
Other references:

DEPENDENCIES / PRECONDITIONS
Required predecessor stories:
External dependencies:
Required configuration/data:

FOLLOW-UP / POST-CONDITIONS
Known stories or capabilities expected to follow this work.

ACCEPTANCE CRITERIA

Scenario: <observable behavior>
  Given <known state>
  And <additional context>
  When <user/system action>
  Then <observable result>
  And <additional observable result>

Scenario: <alternate, error, or edge behavior>
  Given ...
  When ...
  Then ...

TESTING NOTES
Unit:
Integration:
Acceptance:
End-to-end:
Smoke-test candidate: Yes / No

ROLLOUT
Feature flag: Yes / No
Rollout strategy:
Rollback considerations:

NOTES / OPEN QUESTIONS
Anything that must be clarified before or during implementation.

The Sanity-Check Section

This is where the team asks a very useful question:

"What else could this change affect?"

I do not expect every item below to become a section in every ticket. I do expect the relevant items to be considered.

Experience

Accessibility Responsive Light / Dark Color palette Localization Design system Empty states Error states

System

Security Privacy Performance Logging Telemetry External APIs Concurrency

Data

Legacy data Migration Backfill Compatibility Retention Audit

Delivery

Feature flag Gradual rollout Rollback Smoke test Monitoring Docs

Design Is More Than a Screenshot

Direct links to the relevant design are extremely useful. What I try to avoid is making an engineer search through a large design file and guess which version or frame is current.

A screenshot also represents one state at one moment in time.

Depending on the feature, the team may need to define:

• default
• hover
• focus
• selected
• disabled
• loading
• empty
• error
• success
• mobile
• tablet
• desktop
• light mode
• dark mode
• long-content behavior
• keyboard navigation

Color palettes and light/dark modes

Theme support is a good example of a requirement that affects more than a single toggle.

If a change touches color or theme behavior, it is worth asking:

• What is the default theme?
• Does the application follow the operating system preference?
• Can the user override that preference?
• Is the preference persisted?
• Where is it persisted?
• Are colors represented through approved design tokens?
• Do charts and visualizations have theme-safe palettes?
• Are hover, focus, disabled, warning, success, and error states covered?
• Does contrast remain acceptable in each supported theme?
• What happens to existing users with no stored preference?
• Should this be protected by a feature flag?
• Is a critical theme path worth adding to the production smoke suite?

How a Story Translates Into Tests

One of the biggest benefits of concrete scenarios is that they make testing much easier to reason about.

The story establishes the behavior.

Engineering decides how best to protect that behavior at the appropriate levels.

User need → Story → Scenario → Acceptance behavior → Automated coverage → Deployment confidence

Unit tests

Unit tests protect small pieces of implementation logic.

For the delivery-address example, Engineering might test:

• default address selection
• address validation rules
• delivery-estimate calculation inputs
• mapping data between application layers

Product does not need to prescribe those tests. They follow from the implementation.

Integration tests

Integration tests verify important boundaries.

For example, the team might verify that changing the delivery address causes the checkout service to retrieve a new shipping estimate from the correct dependency.

Acceptance tests

Acceptance tests answer:

"Does the system satisfy the behavior the story promised?"

The Gherkin scenarios are natural candidates for this layer.

They do not necessarily need to run through a browser. If a behavior can be reliably proven at an API or service boundary, that may be faster and more stable.

End-to-end tests

Some behavior really does need the assembled system.

Given I am a signed-in customer
And I have two saved delivery addresses
When I select a different delivery address
And I complete checkout
Then my order uses the address I selected

That may justify an end-to-end test because the complete workflow is the behavior we care about.

Smoke tests

Smoke tests answer a narrower operational question:

"After deployment, is the system alive and are the most important workflows still working?"

For an e-commerce application, a small smoke suite might verify:

• storefront loads
• customer can sign in
• product search works
• product can be added to the cart
• checkout can be reached

Tip: The smoke suite should stay small. It is valuable because it gives fast confidence after deployment, not because it repeats the entire acceptance suite.

Feature, Bug, Chore, or Spike?

Making the work type explicit helps set expectations about what "done" means.

Feature

A feature adds or changes meaningful user or system behavior.

Bug

A bug is behavior that differs from the agreed or expected behavior.

EXPECTED BEHAVIOR
What should happen?

ACTUAL BEHAVIOR
What happens instead?

REPRODUCTION
1.
2.
3.

ENVIRONMENT
Where did this happen?

REGRESSION
Yes | No | Unknown

RELATED STORY / EXPECTATION
Link to the original behavior when available.

When possible, the reproduction can become a regression test so the same problem is less likely to return.

Chore

A chore is necessary work that usually does not add new user-facing behavior: framework upgrades, CI configuration, refactoring, certificates, observability, infrastructure work, and similar maintenance.

A chore still needs a verifiable completion condition.

Spike

A spike exists to reduce uncertainty. Its output is learning or a decision, not automatically production code.

QUESTION
What are we trying to learn?

TIME BOX
How much engineering time will we spend?

EXPECTED OUTPUT
Decision, proof of concept, measurements, documented options, etc.

DECISION CRITERIA
What information will let us decide what happens next?

Timeboxes Protect Both the Team and the Schedule

If the purpose of the work is investigation, I like making the limit explicit.

TIME BOX
1 engineer day

QUESTION
Can the existing authorization service support delegated account access?

OUTPUT
Document:
- supported approach
- unsupported cases
- security considerations
- implementation risks
- rough implementation size
- recommended next story

At the end of the timebox, the team decides what to do with what was learned.

That is much healthier than allowing "quick research" to quietly become an open-ended project.

Give Every Work Item a Stable ID

A stable work-item number makes traceability much easier.

STORE-142

That ID can connect:

• the backlog item
• branch names
• pull requests
• test metadata
• feature flags
• release notes
• production defects

Deployment → Code → Pull Request → Work Item → Scenario → Design → Business Reason

Feature Flags and Rollout Are Part of the Work

Delivery does not stop when code is merged.

For meaningful or higher-risk changes, I want the team to consider:

• Do we need a feature flag?
• Who controls it?
• Do internal or pilot users receive the feature first?
• Is rollout immediate or gradual?
• What metrics tell us that the rollout is healthy?
• What would cause us to pause?
• How do we roll back safely?
• When does a temporary flag get removed?

Possible rollout strategies include:

• 100% immediately
• internal users first
• selected pilot customers
• organization-by-organization
• 5% → 25% → 50% → 100%
• opt-in beta

Always Ask About Existing Data

This is one of my favorite sanity checks:

"What happens to the data that already existed yesterday?"

A feature can work perfectly for every new record and still fail for existing customers.

Depending on the change, the team may need:

• a migration
• a backfill
• a default for a missing field
• a compatibility layer
• lazy migration
• schema-version handling
• cleanup after migration

"Works with new data" is not the same thing as "works."

Security Should Be a Design Input, Not a Surprise

For relevant stories, basic security questions should be considered before implementation:

• Who may perform the action?
• Who may see the information?
• Is a specific permission required?
• Could one user access another user's resource?
• Is sensitive information introduced?
• Could sensitive information enter logs?
• Are new external inputs introduced?
• Is an audit event required?

The story does not need to become a security thesis. It just needs to make security-relevant behavior visible when it matters.

Who Owns What?

I think shared ownership works best when it is paired with clear areas of responsibility.

Instead of asking who is at fault after a requirement is missed, I prefer asking who should anchor each type of decision before the work begins.

Product

Product anchors:

• the user problem
• business outcome
• priority
• scope and tradeoffs
• business rules
• acceptance outcomes

Question:
Why are we building this, and what outcome makes it useful?

Design

Design anchors:

• workflow
• interaction behavior
• component states
• responsive behavior
• design-system use
• accessibility implications

Question:
What experience are we intending the user to have?

Engineering

Engineering anchors:

• technical feasibility
• architecture
• implementation
• data and compatibility
• test strategy
• instrumentation and operations

Question:
Can we implement this safely, and how will we prove it works?

The Whole Team

Everyone helps with:

• asking questions
• surfacing assumptions
• identifying dependencies
• calling out contradictions
• keeping the work small enough to understand

Question:
Are we all building the same thing?

The point is not blame. Shared understanding is a team activity. Product should not have to specify internal unit tests, Engineering should not have to invent product behavior, and Design should not have to predict every technical constraint alone.

A Lightweight Definition of Ready

Before normal feature work begins, I like a short readiness check:

• The user or actor is understood.
• The desired outcome is understood.
• The story is small enough to validate independently.
• The important scenarios are understandable.
• The relevant design is linked.
• Dependencies are known.
• Open questions are visible.
• Important security, accessibility, theme, and data concerns have been considered.
• Rollout behavior is understood when it matters.
• Engineering can explain the likely testing approach.

Known uncertainty is manageable. Hidden uncertainty becomes rework.

A Lightweight Definition of Done

"Code is merged" and "the behavior is done" are not always the same thing.

A team's Definition of Done might include:

• Acceptance criteria are satisfied.
• Product accepts the behavior.
• Design validates the experience when relevant.
• Unit and integration coverage are updated.
• Acceptance or end-to-end automation is updated where appropriate.
• The smoke suite is updated when appropriate.
• Security and accessibility concerns are addressed.
• Existing-data behavior is verified.
• Feature flags and rollout are configured when needed.
• Logging and telemetry exist when needed.
• Follow-up work is linked.

Why This Makes Work Better

Fewer surprises

Important decisions happen while they are still cheap to change.

More realistic schedules

Smaller work is easier to estimate, sequence, and reprioritize.

Less context switching

A team can finish useful work instead of carrying a large inventory of half-finished features.

Less rework

Product, Design, and Engineering agree on the behavior before it becomes deeply embedded in the implementation.

Cleaner code

Smaller changes make it easier to build focused abstractions instead of trying to solve several uncertain future problems at once.

Better testing

Concrete scenarios give the team a direct bridge from requirements into automated coverage.

Calmer releases

Feature flags, smaller increments, smoke tests, and observable rollout make production changes less dramatic.

More honest stakeholder conversations

A visible backlog and realistic work-in-progress limit make tradeoffs explicit. Adding something urgent means deciding what moves, rather than quietly asking the same capacity to absorb more work.

The Goal Is Not Perfect Tickets

No template will eliminate uncertainty.

Requirements change. Designs change. Technical discoveries happen. Users surprise us.

That is part of software development.

The goal is not to write a specification so complete that nobody ever needs to talk again.

The goal is to create shared understanding at the cheapest possible point in the process.

• A five-minute question during refinement is cheap.
• Reworking a completed component costs more.
• Finding a misunderstanding during acceptance costs more again.
• Finding it after rollout creates operational stress.
• Finding it after data has been changed can be very expensive.

A little clarity early can remove a surprising amount of stress later.

What Good Looks Like

When an engineer begins a story, I want them to be able to say:

I understand who needs this, why they need it, what behavior we expect, what we are intentionally not building yet, what dependencies exist, and how we will know when it works.

I want Product to be able to say:

I know what outcome I am prioritizing and what behavior I will accept.

I want Design to be able to say:

I know the experience this story is supposed to provide and which states matter for this increment.

And I want the team to be able to say:

We have a realistic amount of work in progress, we know what matters most, and we can finish this before taking on the next thing.

At that point, a ticket stops being a list of requests.

It becomes a small agreement about a useful outcome.

That makes planning easier, implementation cleaner, testing more meaningful, releases safer, and the day-to-day experience better for everyone involved.

Quick Reference: Copy / Paste Checklist

BEFORE DEVELOPMENT

[ ] User / actor is specific
[ ] Why / outcome is clear
[ ] Scope is clear
[ ] Important out-of-scope behavior is stated
[ ] Relevant design is linked
[ ] Dependencies / predecessor stories are linked
[ ] Acceptance scenarios are observable
[ ] Important error / edge cases are considered
[ ] Security implications are considered
[ ] Accessibility is considered
[ ] Theme / responsive states are considered
[ ] Existing / legacy data is considered
[ ] Feature flag is considered
[ ] Rollout / rollback is considered
[ ] Test strategy is understood
[ ] Open questions are visible


BEFORE DONE

[ ] Acceptance criteria pass
[ ] Product accepts behavior
[ ] Design validates experience when relevant
[ ] Unit coverage updated
[ ] Integration coverage updated
[ ] Acceptance coverage updated where appropriate
[ ] Smoke coverage updated where appropriate
[ ] Security / accessibility concerns addressed
[ ] Existing data verified
[ ] Feature flag / rollout configured
[ ] Telemetry / logging available when needed
[ ] Documentation updated when needed
[ ] Follow-up work linked

References & Further Reading

These are external references for the delivery and requirements practices discussed above. Each link opens in a new tab.

Agile Manifesto — Principles Behind the Agile Manifesto ↗ https://agilemanifesto.org/principles.html The Scrum Guide ↗ https://scrumguides.org/ Kanban Guides ↗ https://kanbanguides.org/ Cucumber — Gherkin Reference ↗ https://cucumber.io/docs/gherkin/reference/