Software Project Performance Prediction via Function Point Analysis
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Solution Overview
Problem
Current software project performance prediction methods rely on reactive approaches, focusing on measuring and adjusting after results are tabulated, lacking a standard mechanism for predicting future performance, and thus are not proactive.
Innovation Solution
A method using function point analysis to determine performance estimates, comparing them with actual data, and refining the software project design based on new estimates, incorporating iterative approaches and Technical Performance Measurements (TPMs) to model end estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If reactive measurement and adjustment methods are used, then implementation simplicity is maintained, but predictive capability and proactive performance management deteriorate
Solution Approach 1:
The system performs preliminary performance estimation using function point analysis before the software project is fully implemented. By calculating performance metrics based on design counts and function points during the planning phase, the system enables proactive performance management and early identification of potential issues, resolving the contradiction between implementation simplicity and predictive capability.
2Quantity of substance
If traditional function point counting is used, then project scope measurement is achieved, but performance prediction accuracy deteriorates due to lack of iterative refinement
Solution Approach 1:
The system implements an iterative feedback mechanism where actual performance data collected during software execution is compared against initial performance estimates. The discrepancies are used to refine and update the performance model, improving prediction accuracy for remaining project phases. This feedback loop transforms static function point counting into a dynamic, self-improving performance prediction system.
Solution Approach 2:
The performance prediction system transitions from a static, one-time estimation to a dynamic, evolving model. As the software project progresses and actual performance data becomes available, the system continuously updates its predictions and refines its understanding of performance characteristics, enabling increasingly accurate forecasts throughout the project lifecycle.
3Measurement precision
If detailed performance monitoring is implemented, then performance prediction accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system introduces function point analysis as an intermediary layer between raw performance data and prediction outcomes. By aggregating and processing performance metrics through the function point framework, the system simplifies the complexity of detailed monitoring while maintaining prediction accuracy. The function points serve as a manageable abstraction that reduces data processing requirements.
Data Source
AI summary
In one aspect, a method to predict performance of a software project includes determining, using a computer, performance estimates associated with at least one design count, the design count comprising at least one function point count, collecting actual performance data, comparing the performance estimates with the actual performance data, determining, using the computer, new performance estimates for a remainder of the software project in response to the actual performance data collected and refining a design of the software project based on the new performance estimates.