On the Reproducibility of Quantum Software Defect Datasets: A Case Study of Bugs4Q

📅 2026-06-25
📈 Citations: 0
Influential: 0
📄 PDF
🤖 AI Summary
This study addresses the critical issue of declining reproducibility in quantum software defect datasets—such as Bugs4Q—due to dependency evolution, which undermines research reliability. The authors present the first systematic evaluation of this reproducibility degradation by reproducing 37 bugs across 21 Qiskit versions through 77,700 executions. Combining root cause analysis, dependency management, and API migration insights, they demonstrate that 93.6% of reproduction failures stem from environmental dependency issues rather than actual bug disappearance. Based on these findings, they propose a novel maintenance paradigm requiring source-level fixes and introduce an enhanced dataset, Bugs4Q-Robust, which boosts the reproduction rate from 16.2% to 78.4% on Qiskit v2.3.1—substantially outperforming conventional version-locking approaches.
📝 Abstract
The reproducibility of software defect datasets is essential for obtaining reliable and comparable research results. Zhu et al. have shown that defect datasets such as Defects4J suffer from reproduction failures (i.e., reported bugs become non-reproducible) as time passes since their creation. However, it remains unclear whether these findings generalize to quantum software defect datasets. We therefore conduct a replication study of the prior work using Bugs4Q, a widely used dataset of real-world bugs in quantum programs. Our analysis includes 77,700 quantum program executions of 37 Bugs4Q artifacts across 21 core-library versions. The experimental results showed that the reproducibility of Bugs4Q dropped from 62.2% on Qiskit v0.20.1 to 16.2% on v2.3.1, the latest version as of April 1, 2026. A manual inspection of the root causes further indicated that 93.6% of the failures were dependency-related. While these findings are consistent with those of the prior work, we also observed differences. In particular, most reproduction failures in Bugs4Q cannot be resolved merely by adjusting dependency versions; instead, they require source-code modifications such as migrating import paths and API invocations. Based on this observation, we curated Bugs4Q-Robust, a patched version of Bugs4Q to restore reproducibility. Bugs4Q-Robust increases reproducibility from 16.2% to 78.4% on Qiskit v2.3.1. Our findings highlight the importance of continuous dataset maintenance in the rapidly evolving quantum software ecosystem.
Problem

Research questions and friction points this paper is trying to address.

reproducibility
quantum software
defect datasets
Bugs4Q
dependency evolution
Innovation

Methods, ideas, or system contributions that make the work stand out.

reproducibility
quantum software defects
Bugs4Q
dependency evolution
dataset maintenance
💼 Related Jobs
No related jobs found.
H
Haruto Ohto
The University of Osaka, Osaka, Japan
Y
Yuta Ishimoto
The University of Osaka, Osaka, Japan
S
Shinsuke Matsumoto
The University of Osaka, Osaka, Japan
S
Shinji Kusumoto
The University of Osaka, Osaka, Japan