Software Release Cycle Optimization via Field Defect Simulation
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Solution Overview
Problem
Historical software release planning often overestimates development effort and fails to consider multiple parameters, leading to inadequate planning and potential delays or quality issues in software product delivery.
Innovation Solution
A computer system and method for release cycle optimization that analyzes historical data, selects significant attributes using machine learning algorithms, performs simulations with data values for these attributes, and predicts field defects to identify optimal resource allocations and planning decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical planning methodology is used based on single parameter evaluation, then planning simplicity is maintained, but prediction accuracy of field defects deteriorates
Solution Approach 1:
The patent segments the planning process into distinct phases: historical data collection, attribute selection, simulation execution, and result analysis. Each phase handles specific tasks independently, allowing complex multi-parameter analysis to be broken down into manageable segments that can be processed systematically through simulation cycles.
Solution Approach 2:
The patent introduces simulation as an intermediary layer between historical data and planning decisions. The simulation model acts as a mediator that processes multiple parameters and attributes, translating complex historical data into predicted field defect values that inform planning decisions without requiring direct complex analysis by planners.
2Measurement precision
If multiple parameters are considered in planning, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary attribute selection before simulation to identify only the most significant attributes from historical data. This preliminary filtering reduces the dimensionality of data that needs to be processed in subsequent simulations, decreasing computational requirements while maintaining prediction accuracy by focusing on critical attributes.
Solution Approach 2:
The patent employs multiple simulation cycles with varying data values to thoroughly explore the parameter space. By running simulations with different attribute combinations and values, the system performs excessive analysis to ensure comprehensive coverage of possible outcomes, thereby improving prediction accuracy through repeated evaluation of critical scenarios.
3Manufacturing precision
If simulation with multiple data values is performed, then resource allocation accuracy improves, but time consumption increases
Solution Approach 1:
The patent implements dynamic simulation where data values for attributes are varied across multiple simulation cycles to reflect different possible scenarios and conditions. This dynamic approach allows the system to adaptively explore how changes in resource allocation and other attributes affect predicted field defects, enabling more accurate resource allocation decisions through scenario-based analysis.
Solution Approach 2:
The patent uses feedback from simulation results to refine planning decisions. Predicted field defect values from simulations feed back into the planning process, allowing planners to adjust resource allocation and other attributes based on simulated outcomes. This feedback loop enables iterative improvement of resource allocation accuracy without requiring exhaustive analysis of all possible scenarios.
Data Source
AI summary
Embodiments include a system for release cycle optimization; the system includes a processor configured to perform a method. The method includes accessing, by a processor, historical data relating to a plurality of software version each having a plurality of attributes; selecting a subset of attributes from the plurality of attributes; receiving a set of data values for each of the subset of attributes from the plurality of attributes; performing one or more simulations of a software development cycle utilizing the set of data values; and obtaining a set of results from the one or more simulations comprising a plurality of predicted field defects values corresponding to each of the set of data values.