Task-Based Software Estimation Model Using Design Pattern Segmentation
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Solution Overview
Problem
Traditional software development estimation methods fail to consider the local context of tasks, leading to generic cost estimates that are not tailored to the specific needs of an organization, resulting in inaccurate predictions and additional expenses due to cost and effort overruns.
Innovation Solution
The system generates design patterns based on historical data at a task level, validates them, decomposes them into scenarios, and develops estimate baselines to create a bottom-up estimating model that accounts for the local context and unique tasks of an organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional estimation methods are used, then estimation process is simple and fast, but estimation accuracy is poor and does not consider local context
Solution Approach 1:
The estimation system segments the software development process into distinct tasks and design patterns. Each task is analyzed individually to extract local context factors, and design patterns are identified as reusable templates. This segmentation allows the system to maintain high estimation accuracy by considering specific local context for each task while managing complexity through structured organization of estimation elements.
Solution Approach 2:
The system performs preliminary analysis of historical project data to identify and validate design patterns before actual estimation occurs. By pre-processing historical information to extract task-level details and establish baseline estimates, the system prepares estimation models in advance, improving accuracy without adding significant complexity during the actual estimation process.
2Adaptability or versatility
If standardized design patterns are used, then development process is streamlined and reusable, but underlying tasks are obscured and local context is lost
Solution Approach 1:
The system segments design patterns into their constituent tasks and scenarios, allowing the standardized pattern structure to be maintained while exposing the underlying task-level details. This segmentation enables the system to adapt to local context by analyzing specific task requirements within the framework of standardized patterns, achieving both reusability and local customization.
Solution Approach 2:
The system applies local quality analysis by examining the specific characteristics of tasks within design patterns. Each task is analyzed to identify local context factors such as industry-specific requirements, organizational processes, and technical constraints. This allows standardized design patterns to be customized for local context without losing the benefits of standardization, as each pattern instance can be tailored to specific local requirements.
3Measurement precision
If expert estimation is used, then estimation reflects organizational experience, but estimation is time-consuming and not scalable
Solution Approach 1:
The system enables self-service estimation by automatically analyzing historical project data to generate baseline estimates without requiring extensive manual expert intervention. The system performs self-learning from historical information, extracting design patterns and task characteristics automatically. This maintains estimation accuracy reflective of organizational experience while dramatically increasing estimation speed and scalability, as the system can process multiple projects simultaneously without proportionally increasing expert time requirements.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously refine estimation models using actual project performance data. By comparing predicted estimates with actual outcomes, the system learns and adjusts its design pattern analyses automatically. This feedback loop maintains high estimation accuracy over time while reducing the need for manual expert adjustment, thereby improving both accuracy and productivity.
Data Source
AI summary
System and methods are disclosed for a task-based approach to the generation of a project estimation. The systems and method described herein generate design patterns associated with a software development project based on historical information at a task level. Validate the design patterns. Decompose the design patterns into scenarios having a plurality of tasks. Survey the scenarios. Develop a plurality of estimate baselines based on past actual estimates and survey information. Develop an estimation model based on the plurality of estimate baselines; and provide feedback to the historical information of the estimation model based on actual task based performance metrics for use in generating design patterns for future projects.