arXiv subjects

Nicole Hoess

Publications and source records attributed to Nicole Hoess.

5 recordsLinked to original sources

arXiv2026

Quality Inspection of Printed Circuit Board Pin Insertion via Semantic Segmentation and Board-Level Feature Extraction

Quality control during printed circuit board (PCB) assembly is a critical step in ensuring reliable electronic products. Detecting misaligned pins during or after pin insertion remains a particularly challenging inspection task. This paper presents an automated defect detection method for identifying incorrectly inserted pins on PCBs. The proposed pipeline combines semantic segmentation using a U-Net architecture with contour-based feature extraction and logistic regression for board-level pass/fail classification. Segmentation masks are used to derive contour representations of individual pins, from which board-level features -such as average contour size- are extracted and used to train a logistic regression classifier. We evaluate the method on two datasets: an industrial collection of real-world PCB images, and a publicly available PCB pin-inspection dataset with substantially different visual characteristics. To assess the effectiveness of the proposed approach, a comparison against PatchCore, an anomaly detection technique new to be applied to pin inspection, as well as instance segmentation-based pin detection is made. The developed method achieved Area Under the Receiver Operating Characteristic Curve (ROC-AUC) values of 0.990 on a random test set split from the industrial data and 1.000 on the public dataset indicating strong separation between pass and fail boards. The results indicate that the proposed approach is a promising candidate for automated pin inspection in industrial environments and achieves strong performance on datasets with substantially different visual characteristics after dataset-specific training.

cs.CV↗

arXiv2026

Repositories, Contributors, and Continuity: An Empirical Study of Foundational Quantum Software

Driven by contributions from academia, industry, and open-source communities, the quantum software ecosystem is rapidly growing. Across this ecosystem, new concepts often emerge through software artefacts accompanying scientific publications as well as through sustained development in larger communities. However, many repositories receive development efforts only over a limited period of time, raising the question whether their concepts persist beyond individual repositories. In this paper, we apply established empirical software engineering techniques to analyse a set of foundational quantum software repositories. We combine cross-repository activity with contributor relationships to study the evolution of communities. Our analysis provides empirical evidence of contributor migration patterns and indications of cross-project knowledge transfer. We observe multiple development paths: projects may evolve into sustained communities, contributors may integrate concepts into established ecosystems, or activity may continue through new and follow-up software artefacts. Our observations provide an initial empirical perspective on how concepts and influence persist across repository boundaries in quantum software ecosystems.

cs.SE↗

arXiv2026

Works on My QPU: Reproducibility in Quantum Computing Research

Quantum computing research increasingly depends on complex software stacks, yet the reproducibility of published results does not receive the priority and longevity mandated by recommendations of large international scientific bodies and best practices in software-centric systems research. In this paper, we present a combined manual and automated large-scale analysis of the reproducibility landscape in quantum computing research, quantify shortcomings, and derive actionable steps forward. We manually evaluate a curated sample of 127 papers using a five-question framework that covers code availability, environment specification, documentation, hardware description, and executability. To place these findings in a broader context, we conduct an automated large-scale screening of nearly 5000 quantum computing papers for the same reproducibility indicators. Our manual analysis reveals that only 24.4% of the sampled papers provide code artefacts, and among those, 64.5% fail to execute successfully in a clean environment. This assessment is corroborated by a large-scale automated analysis that yields a consistent code availability rate of 26.8%. Further, it shows that approximately one-third of the papers with accessible code lack machine-readable environment specifications. The results in this paper indicate that reproducibility is not yet consistently achieved in quantum computing research. In response, we outline a set of practical recommendations that address the observed failure modes and illustrate how reproducibility can be improved in practice.

quant-ph↗

arXiv2025

Oops!... I did it again. Analysing and Handling Conclusion (In-)Stability in Socio-Technical Software Engineering

Context: Mining software repositories is a popular means to gain insights into a software project's evolution, monitor project health, support decisions and derive best practices. Tools supporting the mining process are commonly applied by researchers and practitioners, but their limitations and agreement are often not well understood. Objective: This study investigates some threats to validity in complex tool pipelines for evolutionary socio-technical software analyses. We evaluate the tools' agreement in terms of data, study outcomes and conclusions for the same research questions to derive actionable advice for researchers and practitioners. Method: We conduct a lightweight literature review to select \emph{three} studies on collaboration and coordination, software maintenance and software quality from high-ranked venues, which we formally replicate with \emph{four} independent, systematically selected mining tools to quantitatively and qualitatively compare the extracted data, analysis results and conclusions. Results: We find and summarise numerous technical details in tool design and implementation which accumulate along the complex mining pipelines and can cause substantial differences in the extracted baseline data, its derivatives, subsequent results of statistical analyses and, under specific circumstances, conclusions. Conclusions: Users should evaluate mining tools and their limitations carefully to scope the validity of their conclusions, for instance with the checklist we provide. Researchers and tool authors can further reduce uncertainty through reproduction packages and comparative studies in other MSR fields following our methodology.

cs.SE↗

arXiv2025

Does the Tool Matter? Exploring Some Causes of Threats to Validity in Mining Software Repositories

Software repositories are an essential source of information for software engineering research on topics such as project evolution and developer collaboration. Appropriate mining tools and analysis pipelines are therefore an indispensable precondition for many research activities. Ideally, valid results should not depend on technical details of data collection and processing. It is, however, widely acknowledged that mining pipelines are complex, with a multitude of implementation decisions made by tool authors based on their interests and assumptions. This raises the questions if (and to what extent) tools agree on their results and are interchangeable. In this study, we use two tools to extract and analyse ten large software projects, quantitatively and qualitatively comparing results and derived data to better understand this concern. We analyse discrepancies from a technical point of view, and adjust code and parametrisation to minimise replication differences. Our results indicate that despite similar trends, even simple metrics such as the numbers of commits and developers may differ by up to 500%. We find that such substantial differences are often caused by minor technical details. We show how tool-level and data post-processing changes can overcome these issues, but find they may require considerable efforts. We summarise identified causes in our lessons learned to help researchers and practitioners avoid common pitfalls, and reflect on implementation decisions and their influence in ensuring obtained data meets explicit and implicit expectations. Our findings lead us to hypothesise that similar uncertainties exist in other analysis tools, which may limit the validity of conclusions drawn in tool-centric research.

cs.SE↗