A software quality self-adaptive measurement method based on information geometry and reinforcement learning

By combining information geometry and reinforcement learning, low-dimensional manifold coordinates of software class graphs are extracted, composite fuzzy matter-element is constructed and adaptively optimized, overcoming the limitations of traditional software quality measurement methods and achieving efficient and accurate evaluation of complex software systems.

CN121579322BActive Publication Date: 2026-07-03JIANGXI UNIVERSITY OF FINANCE AND ECONOMICS
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Patent Information

Application Number
CN202511864814.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-12-11
Publication Date
2026-07-03
Estimated Expiration
2045-12-11

AI Technical Summary

Technical Problem

Existing software quality measurement methods cannot adapt to the dynamic and uncertain nature of software quality evaluation. They lack geometric modeling of high-dimensional manifold spaces, traditional methods cannot accurately characterize the intrinsic structure of quality features, and they fail to fully utilize machine learning, especially reinforcement learning techniques, to achieve adaptive optimization of the evaluation process.

Method used

A method combining information geometry and reinforcement learning is adopted. Low-dimensional manifold coordinates are extracted through information geometric mapping and sparse representation to construct composite fuzzy matter-element, and the initial weights are calculated using the entropy weight method. Multiple rounds of iterative optimization are carried out through the adaptive feedback mechanism of reinforcement learning, and finally the MARCOS method is used to calculate the comprehensive evaluation value.

Benefits of technology

It enables dynamic and adaptive evaluation of software quality, improves the accuracy and discriminative power of measurement results, overcomes the limitations of traditional methods, and adapts to the nonlinear and high-dimensional quality data of complex software systems.

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Abstract

This invention discloses an adaptive software quality measurement method based on information geometry and reinforcement learning, comprising: acquiring original quality feature data of software class graphs; extracting low-dimensional manifold coordinates through information geometry mapping and sparse representation; reconstructing index values ​​and constructing composite fuzzy matter elements based on manifold coordinates; dimensionlessly transforming the matter elements to obtain an optimal membership matrix; determining initial weights based on the entropy weight method; iteratively optimizing the weights through a reinforcement learning adaptive feedback mechanism; and calculating the comprehensive evaluation value and outputting the measurement result using the MARCOS method. This invention achieves a breakthrough in software quality measurement from static to dynamic adaptation, significantly improving the accuracy and practicality of the measurement, and effectively overcoming the shortcomings of traditional methods in terms of high-dimensional complexity, evaluation fuzziness, and limitations of static weights.
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