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Research Paper#Quantum Software Engineering🔬 ResearchAnalyzed: Jan 3, 2026 08:50

Quantum Software Bugs: A Large-Scale Empirical Study

Published:Dec 31, 2025 06:05
1 min read
ArXiv

Analysis

This paper provides a crucial first large-scale, data-driven analysis of software defects in quantum computing projects. It addresses a critical gap in Quantum Software Engineering (QSE) by empirically characterizing bugs and their impact on quality attributes. The findings offer valuable insights for improving testing, documentation, and maintainability practices, which are essential for the development and adoption of quantum technologies. The study's longitudinal approach and mixed-method methodology strengthen its credibility and impact.
Reference

“Full-stack libraries and compilers are the most defect-prone categories due to circuit, gate, and transpilation-related issues, while simulators are mainly affected by measurement and noise modeling errors.”

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Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 10:11

CodeFlowLM: Incremental Just-In-Time Defect Prediction with Pretrained Language Models and Exploratory Insights into Defect Localization

Published:Nov 28, 2025 22:18
1 min read
ArXiv

Analysis

This article introduces CodeFlowLM, a system for predicting software defects using pretrained language models. It focuses on incremental, just-in-time defect prediction, which is crucial for efficient software development. The research also explores defect localization, providing insights into where defects are likely to occur within the code. The use of pretrained language models suggests a focus on leveraging existing knowledge to improve prediction accuracy. The source being ArXiv indicates this is a research paper.
Reference

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