Predictive Model System for Software Development Performance
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Solution Overview
Problem
Current agile practices rely on retrospective metrics, making it difficult to accurately predict future software development team performance for similar projects, leading to potential overcommitment or undercommitment of resources.
Innovation Solution
A system and method for providing software development performance predictions that receive project data associated with completed projects, define predictive models based on predictive variables such as team size, scheduled time off, new tools, and team member expertise, and use these models to identify similar completed projects to generate performance predictions for new projects, continuously updating the models with actual results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If retrospective metrics are used to determine team performance, then past performance can be measured, but future performance prediction accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing project data before new projects begin. It establishes baseline metrics from historical completed projects and uses predictive models to forecast future performance before resource commitment decisions are made, rather than waiting for retrospective measurement after projects complete.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing actual project outcomes with predicted performance metrics. This feedback loop allows the system to refine and update predictive models based on real performance data, improving future prediction accuracy while maintaining the ability to measure past performance.
2Ease of operation
If resource allocation is based on historical retrospective data, then resource distribution can be simplified, but resource allocation accuracy deteriorates
Solution Approach 1:
The system enables self-service by automatically collecting project data, calculating metrics, and generating resource allocation recommendations without manual intervention. This maintains ease of operation while improving accuracy through automated analysis of multiple predictive variables and machine learning models that continuously refine their predictions.
Solution Approach 2:
The patent replaces manual mechanical resource allocation processes with automated computational systems. Machine learning models and algorithms substitute for human judgment and spreadsheets, automatically analyzing historical data and predictive variables to generate accurate resource allocation recommendations while keeping the process simple and scalable.
3Reliability
If predictive models are continuously updated with actual results, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system embraces dynamics by implementing continuously updating predictive models that adapt to changing conditions. The models are designed to be dynamic rather than static, automatically incorporating new project data and refining their parameters over time. This dynamic approach improves prediction accuracy while the modular architecture manages complexity through automated updates rather than manual reconfiguration.
Solution Approach 2:
The system uses copying by creating simplified representative models that capture essential patterns from historical data. Rather than managing complex raw data directly, the system creates aggregated metric copies and standardized data representations that maintain predictive power while reducing complexity. These copied models can be efficiently updated and distributed across the organization.
Data Source
AI summary
A system for providing software development performance predictions is disclosed. The system can include one or more processors and a memory in communication with the processors storing instructions that, when executed by the processors are configured to cause the system to perform method steps. The system can receive data associated with a plurality of completed projects and a request for a new software development project. The system can determine first metrics associated with each completed project and second metrics associated with the new software development project. The first and second metrics may be associated with one or more predictive variables. The system can define predictive model systems based on one or more predictive variables and identify completed projects including a first subgroup of first metrics that match the second metrics beyond a predetermined threshold. The system can determine a performance prediction based on the identified first subgroup of first metrics.