Bug Classification in Quantum Software: A Rule-Based Framework and Its Evaluation
Mir Mohammad Yousuf, Shabir Ahmad Sofi

TL;DR
This paper introduces a rule-based framework for classifying bugs in quantum software, demonstrating its effectiveness through extensive evaluation and revealing insights into bug types and severity in quantum repositories.
Contribution
It presents a novel, tailored rule-based approach for quantum bug classification and provides a comprehensive evaluation on real-world quantum software issues.
Findings
Achieved up to 85.21% classification accuracy.
Classical bugs dominate at 67.2%, quantum-specific at 27.3%.
Most issues are low severity, with quantum circuit problems being prevalent.
Abstract
Accurate classification of software bugs is essential for improving software quality. This paper presents a rule-based automated framework for classifying issues in quantum software repositories by bug type, category, severity, and impacted quality attributes, with additional focus on quantum-specific bug types. The framework applies keyword and heuristic-based techniques tailored to quantum computing. To assess its reliability, we manually classified a stratified sample of 4,984 issues from a dataset of 12,910 issues across 36 Qiskit repositories. Automated classifications were compared with ground truth using accuracy, precision, recall, and F1-score. The framework achieved up to 85.21% accuracy, with F1-scores ranging from 0.7075 (severity) to 0.8393 (quality attribute). Statistical validation via paired t-tests and Cohen's Kappa showed substantial to almost perfect agreement for bug…
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Taxonomy
TopicsSoftware Engineering Research · Quantum Computing Algorithms and Architecture · Software Reliability and Analysis Research
