Borrowing Software Testing Techniques for Effective Story Slicing
When developing user stories for Agile development, teams often struggle to break down complex features into manageable, testable units that can be completed within a sprint. While several frameworks and methodologies offer guidance, one unconventional but highly effective approach is borrowing techniques from software testing, such as Boundary Value Analysis, Equivalence Class Partitioning, and others, to inform the process of story slicing. By thinking of user stories as “test cases” that cover specific scenarios, teams can create smaller, more focused stories that cover critical functionality with reduced risk of over-scope.
What's even more powerful is that these techniques can help drive shifted-left Test-Driven Development (TDD) before any code is even written. With clearly defined boundaries, equivalence classes, and decision criteria, developers can start with tests that guide the development process, ensuring functionality is delivered incrementally and meets requirements from the outset.
Here’s how important test design techniques can be applied to help split large stories into meaningful, testable units.
1. Boundary Value Analysis (BVA) for Story Slicing
What it is in testing:
Boundary Value Analysis (BVA) focuses on testing the boundaries between valid and invalid input ranges. It is often more effective than testing arbitrary values because many errors occur at the boundaries.
How to apply it for story slicing:
When faced with a user story that deals with ranges, thresholds, or limits (e.g., age inputs, price filters, or quantity limits), BVA can be used to slice the story by focusing on these critical boundary cases. You can create separate stories for handling minimum values, maximum values, and values just inside or outside the acceptable range.
Example Story: “As a user, I want to filter hotel rooms by price, where the valid price range is between $100 and $500 per night.”
Using BVA:
Story 1: Handle the minimum boundary case ($100).
How Logic is Handled:
Back-End: The API or filtering service responsible for retrieving hotel rooms ensures that requests for $100 return valid room results. Validation logic checks that price filtering begins at $100.
Front-End: The UI displays rooms available for exactly $100 and ensures that when the user inputs $100 in the filter box, the system does not return an error.
Story 2: Handle the maximum boundary case ($500).
How Logic is Handled:
Back-End: The API ensures that rooms with a price of $500 are included in the search results. The service correctly handles the upper boundary, preventing prices over $500 from being fetched.
Front-End: The price filter accepts $500 as a valid input, showing results for rooms priced exactly at $500. The UI does not throw an error when this value is entered.
Story 3: Handle invalid price inputs (e.g., below $100, above $500).
How Logic is Handled:
Back-End: Input validation logic in the API prevents prices outside the valid range from being processed. If a user submits prices below $100 or above $500, the API returns a validation error or no results.
Front-End: The UI provides feedback if users enter a value outside the valid range (e.g., $50 or $600). This could be done via a visual indicator (like a red border on the input field) or a pop-up error message that informs the user that the price must be between $100 and $500.
Story 4: Handle valid prices within the range.
How Logic is Handled:
Back-End: The API processes filter requests with prices within the range (e.g., $150, $300) and returns appropriate results. Any valid price input is successfully applied in the query to fetch rooms.
Front-End: When a user enters a valid price within the range, the UI updates smoothly with the filtered results. The interface should allow for this without additional error handling, simply displaying the rooms that match the user's input.
This ensures that key edge cases are not missed and simplifies development by breaking down complex logic into distinct deliverables.
2. Equivalence Class Partitioning (ECP) for Story Slicing
What it is in testing: Equivalence Class Partitioning (ECP) divides input data into different classes or partitions where the system behaves similarly. Only one test case from each class is typically tested, reducing the number of test cases while still providing sufficient coverage.
How to apply it for story slicing: When you have a large user story that includes different types of inputs or actions that can be grouped together, use ECP to break the story down by those input partitions.
Story Example: "As a user, I want to enter a username that should be between 5 and 20 characters."
Using Equivalence Class Partitioning (ECP):
Story 1: Handle valid username lengths (5-20 characters).
How Logic is Handled:
Back-End: The API validates the username length when a user submits the form, accepting usernames between 5 and 20 characters.
Front-End: The form provides immediate feedback (e.g., visual cue like a green checkmark) when the username length is valid, ensuring a smooth user experience without submitting invalid data.
Story 2: Handle too short usernames (less than 5 characters).
How Logic is Handled:
Back-End: The API rejects the username if it's less than 5 characters, returning a specific validation error.
Front-End: The UI prevents submission of the form and displays an error message, like “Username must be at least 5 characters.” The input field could also visually indicate the problem (e.g., red border or error icon).
Story 3: Handle too long usernames (greater than 20 characters).
How Logic is Handled:
Back-End: The API validates that the username doesn't exceed 20 characters, returning a validation error if it does.
Front-End: The UI prevents submission, displaying an error like “Username cannot exceed 20 characters.” The field itself could restrict entry beyond 20 characters to further prevent invalid input.
Here, instead of handling all input scenarios in one large story, you split the story by classes of valid and invalid inputs.
3. Decision Table Testing for Story Slicing
What it is in testing: Decision Table Testing creates a matrix of possible input conditions and their expected outcomes. It ensures that combinations of inputs are tested, especially in complex systems where multiple conditions impact outcomes.
How to apply it for story slicing: Use decision tables to analyse combinations of conditions within a user story, and break the story into smaller stories that handle different combinations.
Example Story: “As a user, I want to log in using a correct username, password, and captcha.”
Using a decision table:
Story 1: Correct username, correct password, correct captcha (successful login).
How Logic is Handled:
Back-End: The login API checks the validity of the username, password, and captcha. If all are correct, the user’s session is created, and they are authenticated.
Front-End: The UI submits the login form and redirects the user to the dashboard or homepage after successful authentication, showing a success message if needed.
Story 2: Incorrect username, correct password, correct captcha (error message).
How Logic is Handled:
Back-End: The API checks the username, identifies it as incorrect, and returns an authentication failure message.
Front-End: The login form displays an error message like "Invalid username," preventing login without affecting the password or captcha fields.
Story 3: Correct username, incorrect password, correct captcha (error message).
How Logic is Handled:
Back-End: The API detects that the password is incorrect and returns an error specific to that input.
Front-End: The UI displays an error message like "Incorrect password," allowing the user to retry without re-entering the captcha.
Story 4: Correct username, correct password, incorrect captcha (error message).
How Logic is Handled:
Back-End: The API checks the captcha input and returns a validation error for an incorrect captcha.
Front-End: The UI displays an error message like "Captcha validation failed," possibly generating a new captcha for the user to try again.
By slicing based on condition combinations, you ensure that each story is focused on a specific scenario, improving clarity and manageability.
4. State Transition for Story Slicing
What it is in testing: State Transition testing involves examining how a system moves between states based on input events. It is useful for systems where the current state determines the next possible actions.
How to apply it for story slicing: When user stories involve systems with multiple states (e.g., a user’s session or a product’s life cycle), use state transition diagrams to slice stories based on different transitions.
Example Story: “As a user, I want to manage my hotel booking, including booking, cancellation, and re-booking.”
Using state transition:
Story 1: Handle booking creation.
How Logic is Handled:
Back-End: The booking service processes the request to create a new booking, transitioning the booking state from "None" to "Booked." This involves checking room availability and finalising payment.
Front-End: The booking UI shows confirmation once the booking is successfully created, allowing the user to view booking details in their account.
Story 2: Handle booking cancellation.
How Logic is Handled:
Back-End: The API transitions the booking state from "Booked" to "Cancelled" once the user initiates cancellation. It may handle refund processing, if applicable.
Front-End: The UI updates the booking status to "Cancelled" and displays appropriate messaging, such as confirmation that the booking was successfully canceled.
Story 3: Handle re-booking after cancellation.
How Logic is Handled:
Back-End: The system allows the state to transition from "Cancelled" back to "Booked" when a user re-books a previously canceled reservation. This may involve re-checking room availability and processing payment again.
Front-End: The booking UI reflects the updated status, showing the new booking details after re-booking and confirming the transaction.
Story 4: Handle invalid state transitions (e.g., canceling a non-existent booking).
How Logic is Handled:
Back-End: The API throws an error if a user attempts an invalid state transition, like canceling a booking that doesn't exist. It ensures business rules are followed, preventing unintended actions.
Front-End: The UI displays a relevant error message (e.g., "No booking found to cancel") and prevents any further invalid actions from being triggered.
This approach ensures that each story covers a distinct system state, simplifying the handling of state changes.
5. Error Guessing for Story Slicing
What it is in testing: Error Guessing relies on testers’ intuition and experience to anticipate areas where errors might occur, often based on common failure points in similar systems.
How to apply it for story slicing: When breaking down a large story, think about the error-prone areas based on past experiences with similar features or systems. Create stories that address these potential pitfalls explicitly.
Example Story: “As a user, I want to reset my password if I forget it.”
Using Error Guessing:
Story 1: Handle successful password reset.
How Logic is Handled:
Back-End: The reset link is validated, and if successful, the API allows the user to create a new password, updating the user’s credentials in the database.
Front-End: The UI confirms that the password reset was successful and redirects the user to the login page, possibly displaying a success message like “Your password has been successfully reset.”
Story 2: Handle invalid reset links.
How Logic is Handled:
Back-End: The API detects if a password reset link is invalid (e.g., manipulated or corrupted) and rejects the request with an appropriate error message.
Front-End: The UI displays an error message like “Invalid reset link” and offers options to request a new link or return to the login page.
Story 3: Handle expired reset tokens.
How Logic is Handled:
Back-End: The API checks the timestamp on the reset token and determines that it has expired, returning an error.
Front-End: The UI displays an error message like “Reset link expired,” prompting the user to request a new password reset link.
Story 4: Handle incorrect answers to security questions.
How Logic is Handled:
Back-End: If security questions are used as part of the password reset flow, the API validates the answers. If incorrect, it returns an error indicating the failure.
Front-End: The UI informs the user that the answer to the security question was incorrect and provides a retry option.
By anticipating likely failure points, you create stories that cover not only the “happy path” but also common error cases, improving overall system robustness.
Borrowing techniques from software testing, such as Boundary Value Analysis, Equivalence Class Partitioning, Decision Table Testing, State Transition, and Error Guessing, can dramatically improve how you slice user stories. These methods help to ensure stories are broken down into well-defined, testable units that cover key scenarios and edge cases without overwhelming the development team. By thinking like a tester when slicing stories, teams can manage complexity better and deliver higher-quality software incrementally.
When applied correctly, they lay the groundwork for shifted-left TDD, ensuring that testing drives development decisions before any code is written. This approach helps teams build high-quality, thoroughly tested features incrementally, leading to more reliable and maintainable software over time.