Automated User Story Assessment System
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Solution Overview
Problem
User stories in software development often result in errors and inconsistencies due to variations in structure and content, leading to issues with completeness, understandability, and uniqueness, which can increase software development time and resource utilization inefficiencies.
Innovation Solution
A computer-implemented method that characterizes user stories using machine learning models and ontologies to classify complexity and recommend modifications, incorporating historical data and user feedback to improve alignment with similar stories, thereby enhancing the quality and consistency of user stories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user stories are written with varying structures and content, then developers can express diverse requirements, but errors and bugs increase due to inconsistency
Solution Approach 1:
The patent applies homogeneity by enforcing a standardized structure for user stories through templates that require consistent elements (persona, action, expected outcome, failure path, acceptance criteria, justification). This standardization reduces variability while maintaining the ability to express diverse requirements through the structured fields, thereby reducing errors and bugs caused by inconsistent formatting and missing information.
2Productivity
If user stories lack complete information, then writing them becomes faster, but completeness and understandability deteriorate
Solution Approach 1:
The patent implements preliminary action by providing pre-defined templates with all necessary fields already structured before the developer writes the user story. The template includes persona, action, expected outcome, failure path, acceptance criteria, and justification fields, guiding developers to complete all essential information upfront. This preliminary structuring maintains writing speed while ensuring completeness and understandability.
Solution Approach 2:
The system provides feedback by validating user story inputs against the template requirements, identifying missing or incomplete information, and guiding developers to fill in necessary fields. This feedback mechanism ensures that user stories meet completeness standards without significantly increasing the time required to write them.
3Reliability
If manual review of user stories is performed, then quality can be ensured, but development time increases
Solution Approach 1:
The patent applies self-service by enabling the system to automatically validate and assess user stories against the standardized template and quality criteria. The automated assessment checks for completeness, consistency, and adherence to the template structure, reducing or eliminating the need for manual review while maintaining quality assurance. This significantly reduces the time required while ensuring reliable quality control.
Data Source
AI summary
A computer-implemented method includes receiving, by a processing system, a user story for developing software. The processing system characterizes the user story to determine a plurality of attributes. The processing system classifies a complexity of the user story associated with the attributes. The processing system determines a history score of the user story based on a similarity of one or more previously analyzed user stories having a similar complexity. The processing system recommends one or more modifications to the user story to increase alignment of one or more attributes of the user story with the one or more previously analyzed user stories.