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IEEE International Conference on Green Industrial Electronics and Sustainable Technologies (GIEST)

Hybrid Quantum-Classical Machine Learning for Software Requirement Risk Classification

Pathan Shaheen Khan, Chandan Kumar

Published Oct 11, 2025• 0 citations
Publisher

Abstract

Software Requirement Risk Analysis is crucial for detecting potential failures early in the Software Development Life Cycle (SDLC). Existing machine learning (ML) and deep learning (DL) methods provide promising results, but they often struggle with capturing complex dependencies in requirement data. This paper introduces a novel hybrid Quantum-Classical Machine Learning (QML) framework to improve the accuracy and reliability of requirement risk classification. The dataset was preprocessed using label encoding, Synthetic Minority Oversampling Technique (SMOTE), ANOVA F-test (SelectKBest) for feature selection, and feature scaling to enable quantum circuit integration. Strongly entangling quantum circuits were implemented using the Qiskit framework to generate quantumenhanced features, which were fused with classical features. Four hybrid models-RF + QML, LGBM + QML, LR + QML, and Q-Kernel + MLP-were trained and evaluated. Experimental results show that the proposed Q-Kernel + MLP model achieved the best performance with $97.76 \%$ accuracy, $97.92 \%$ precision, $97.76 \%$ recall, and an F 1 -score of $97.77 \%$, outperforming classical baselines by up to $4 \%$. These findings highlight the potential of quantum-enhanced learning in software engineering, offering a scalable and robust solution for early risk detection and supporting more reliable project planning and software quality assurance.

DOI
10.1109/giest66547.2025.11387101

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Software Requirement Risk Analysis is crucial for detecting potential failures early in the Software Development Life Cycle (SDLC). Existing machine learning (ML) and deep learning (DL) methods provide promising results, but they often struggle with capturing complex dependencies in requirement data. This paper introduces a novel hybrid Quantum-Classical Machine Learning (QML) framework to improve the accuracy and reliability of requirement risk classification. The dataset was preprocessed using label encoding, Synthetic Minority Oversampling Technique (SMOTE), ANOVA F-test (SelectKBest) for feature selection, and feature scaling to enable quantum circuit integration. Strongly entangling quantum circuits were implemented using the Qiskit framework to generate quantumenhanced features, which were fused with classical features. Four hybrid models-RF + QML, LGBM + QML, LR + QML, and Q-Kernel + MLP-were trained and evaluated. Experimental results show that the proposed Q-Kernel + MLP model achieved the best performance with 97.76%97.76 \%97.76% accuracy, 97.92%97.92 \%97.92% precision, 97.76%97.76 \%97.76% recall, and an F 1 -score of 97.77%97.77 \%97.77%, outperforming classical baselines by up to 4%4 \%4%. These findings highlight the potential of quantum-enhanced learning in software engineering, offering a scalable and robust solution for early risk detection and supporting more reliable project planning and software quality assurance.
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