Software Project Estimation via Multi-Dimensional Decision Matrix
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Solution Overview
Problem
Conventional software estimation techniques fail to consider all relevant parameters such as project size, effort, schedule, and cost collectively, leading to issues like incorrect budgeting, resource loading, and inaccurate forecasting, and are not adaptable to various types of software projects.
Innovation Solution
A system and method that integrates project size, effort, and cost estimation using a multi-dimensional decision matrix, mapping user-input parameters to relevant techniques, evaluating success ratings based on historical data, and determining interoperability to provide an optimum estimate for software projects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional software estimation techniques are used, then the estimation process is simple, but the accuracy and comprehensiveness of the estimate deteriorates due to failure to consider all relevant parameters collectively
Solution Approach 1:
The patent segments the software estimation process into multiple independent modules, each responsible for a specific estimation parameter (size, effort, cost, schedule). This allows comprehensive consideration of all parameters while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces a multi-dimensional decision matrix that evaluates estimation techniques across multiple dimensions simultaneously (parameter coverage, data availability, project characteristics). This transforms the estimation process from a single-dimension approach to a multi-dimensional comprehensive evaluation.
2Adaptability or versatility
If conventional estimation techniques are used, then the implementation is straightforward, but the adaptability to various types of software projects deteriorates
Solution Approach 1:
The patent creates a universal estimation system that can handle multiple project types (custom development, enhancement, maintenance, etc.) through a unified multi-dimensional decision matrix framework. The system adapts to different project types by selecting appropriate estimation techniques based on project characteristics rather than requiring separate methodologies.
Solution Approach 2:
The patent implements a dynamic estimation system where the selection of estimation techniques is not fixed but adapts based on project characteristics, available data, and organizational context. The decision matrix dynamically evaluates and selects the most appropriate techniques for each specific project scenario.
3Measurement precision
If comprehensive parameter consideration is implemented, then the estimation quality improves, but the computational resources and time required increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing the multi-dimensional decision matrix framework and pre-identifying available organizational data assets. This preliminary setup enables faster execution during actual estimation by avoiding ad-hoc data collection and analysis for each estimation task.
Solution Approach 2:
The patent implements self-service mechanisms where the system automatically evaluates available data, selects appropriate estimation techniques, and performs computations without requiring extensive manual intervention. The decision matrix framework autonomously guides the estimation process, reducing time investment while maintaining comprehensive parameter consideration.
Data Source
AI summary
A system and a method related to software project estimation. The method receives a value corresponding to at least one decision parameter including a project type, a technology, a software development life cycle (SDLC) type and a stage of the software project from user. The received value is mapped with techniques associated with each of estimation parameters including a size parameter, an effort parameter, a cost parameter, and a schedule parameter in a decision matrix. Based on the mapping, one or more techniques for each estimation parameter are shortlisted, and a success rating factor for each shortlisted technique is evaluated based on historical data to identify a primary set of techniques. Compatibility of the primary set of techniques is then determined based on an interoperability factor to identify at least one secondary set of techniques providing optimum estimate of the software project.