Cognitive Model for Agile Sprint Estimation
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Solution Overview
Problem
Agile software development projects face inefficiencies and increased costs due to the lack of full-time subject matter expert (SME) involvement, particularly in Agile Backlog Planning, which hinders effective prioritization and estimation of software development tasks.
Innovation Solution
A cognitive model-based system that automatically assigns sprints and estimates software development parameters by leveraging historical data from similar projects, reducing dependency on SMEs and streamlining the planning process through data analytics and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-time subject matter expert (SME) involvement is used in Agile Backlog Planning, then estimation accuracy and prioritization quality are improved, but project costs and overhead increase
Solution Approach 1:
The patent creates a cognitive model that copies and learns from historical project data, including past estimations, user stories, and project characteristics. This model then generates estimated story points for new projects without requiring full-time SME involvement, thus maintaining estimation accuracy while reducing costs
Solution Approach 2:
The system enables self-service estimation by automatically analyzing project requirements and generating story point estimates using the cognitive model. The model serves itself by learning from historical data and applying that knowledge to new projects, reducing dependency on continuous SME participation
2Reliability
If full-time subject matter expert (SME) involvement is used in Agile Backlog Planning, then prioritization quality is improved, but time overhead increases
Solution Approach 1:
The cognitive model copies prioritization patterns from historical projects, learning how SMEs previously prioritized user stories based on project context, business value, and technical constraints. This allows the system to automatically prioritize new user stories with reliability comparable to SME involvement
Solution Approach 2:
The system performs preliminary prioritization automatically before formal planning meetings, pre-processing user stories and assigning initial priorities based on the cognitive model's analysis of historical data. This reduces the time SMEs need to spend during actual planning sessions while maintaining prioritization quality
3Adaptability or versatility
If manual estimation and sprint assignment is performed, then flexibility and adaptability are improved, but productivity and efficiency decrease
Solution Approach 1:
The cognitive model is designed to be dynamic, continuously learning from new project data and adapting its estimation and prioritization algorithms. The system can adjust to different project types, methodologies, and organizational patterns, maintaining flexibility while automating the planning process to improve productivity
Solution Approach 2:
The system incorporates feedback mechanisms where actual project outcomes, velocity data, and retrospective insights are fed back into the cognitive model. This continuous feedback loop allows the model to learn and adapt to real project conditions, improving both the accuracy of automated estimations and the system's overall flexibility
4Productivity
If automated cognitive model is used for sprint assignment, then productivity and efficiency are improved, but measurement precision of estimation may worsen
Solution Approach 1:
The cognitive model performs preliminary analysis of project requirements, user stories, and historical data before generating estimates. This pre-processing allows the automated system to gather and analyze relevant information thoroughly, improving estimation accuracy while maintaining high productivity through automation
Solution Approach 2:
The model copies proven estimation patterns and methodologies from historical projects where SMEs produced accurate estimates. By replicating these successful patterns, the automated system achieves measurement precision comparable to manual SME estimation while maintaining the productivity benefits of automation
Data Source
AI summary
A method includes using, by one or more processors of a computer system, a cognitive model to estimate software development parameters for a software development project based on one or more similar past projects, and automatically assigning, by the one or more processors of the computer system, story points to sprints of the software development project based on the estimated software development parameters.