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Technical Debt Management: The Road Ahead for Successful Software Delivery

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arXiv 2024年

作者： Avgeriou, Paris Ozkaya, Ipek Chatzigeorgiou, Alexander Ciolkowski, Marcus Ernst, Neil A. Koontz, Ronald J. Poort, Eltjo Shull, Forrest Dept. of Computing Science University of Groningen Groningen Netherlands Software Engineering Institute Carnegie Mellon University PittsburghPA United States Dept. of Applied Informatics University of Macedonia Thessaloniki Greece QAware GmbH München Germany Department of Computer Science University of Victoria Victoria Canada The Boeing Company MesaAZ United States CGI Rotterdam Netherlands

Technical Debt, considered by many to be the 'silent killer' of software projects, has undeniably become part of the everyday vocabulary of software engineers. We know it compromises the internal quality of a system, either deliberately or inadvertently. We understand Technical Debt is not all derogatory, often serving the purpose of expediency. But, it is associated with a clear risk, especially for large and complex systems with extended service life: if we do not properly manage Technical Debt, it threatens to "bankrupt" those systems. Software engineers and organizations that develop softwareintensive systems are facing an increasingly more dire future state of those systems if they do not start incorporating Technical Debt management into their day to day practice. But how? What have the wins and losses of the past decade of research and practice in managing Technical Debt taught us and where should we focus next? In this paper, we examine the state of the art in both industry and research communities in managing Technical Debt;we subsequently distill the gaps in industrial practice and the research shortcomings, and synthesize them to define and articulate a vision for what Technical Debt management looks like five years hence. © 2024, CC BY-NC-ND.

关键词： Industrial research

On the Baltimore Light RailLink into the quantum future

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arXiv 2024年

作者： Domino, Krzysztof Doucet, Emery Robertson, Reece Gardas, Bartlomiej Deffner, Sebastian Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Department of Physics University of Maryland Baltimore County BaltimoreMD21250 United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States National Quantum Laboratory College ParkMD20740 United States

In the current era of noisy intermediate-scale quantum (NISQ) technology, quantum devices present new avenues for addressing complex, real-world challenges including potentially NP-hard optimization problems. This work aims to showcase how the inherent noise in NISQ devices can be leveraged to solve such real-world problems effectively. Utilizing a D-Wave quantum annealer and IonQ's gate-based NISQ computers, we generate and analyze solutions for managing train traffic under stochastic disturbances. Our case study focuses on the Baltimore Light RailLink, which embodies the characteristics of both tramway and railway networks. We explore the feasibility of using NISQ technology to model the stochastic nature of disruptions in these transportation systems. Our research marks the inaugural application of both quantum computing paradigms to tramway and railway rescheduling, highlighting the potential of quantum noise as a beneficial resource in complex optimization scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum noise

YAQQ: Yet Another Quantum Quantizer - Design Space Exploration of Quantum Gate Sets using Novelty Search

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arXiv 2024年

作者： Sarkar, Aritra Kundu, Akash Steinberg, Matthew Mishra, Sibasish Fauquenot, Sebastiaan Acharya, Tamal Miszczak, Jaroslaw A. Feld, Sebastian Quantum Intelligence research team Quantum Machine Learning research group Quantum Computing division QuTech Netherlands Department of Quantum & Computer Engineering Delft University of Technology Netherlands Joint Doctoral School Silesian University of Technology Gliwice Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland QTF Centre of Excellence Department of Physics University of Helsinki Finland

The standard model of quantum computation is based on quantum circuits, where the number and quality of the quantum gates composing the circuit influence the runtime and fidelity of the computation. The fidelity of the decomposition of quantum algorithms, represented as unitary matrices, to bounded depth quantum circuits depends strongly on the set of gates available for the decomposition routine. To investigate this dependence, we explore the design space of discrete quantum gate sets and present a software tool for comparative analysis of quantum processing units and control protocols based on their native gates. The evaluation is conditioned on a set of unitary transformations representing target use cases on the quantum processors. The cost function considers three key factors: (i) the statistical distribution of the decomposed circuits’ depth, (ii) the statistical distribution of process fidelities for the approximate decomposition, and (iii) the relative novelty of a gate set compared to other gate sets in terms of the aforementioned properties. The developed software, called YAQQ (Yet Another Quantum Quantizer), enables the discovery of an optimized set of quantum gates through this tunable joint cost function. To identify these gate sets, we use the novelty search algorithm, circuit decomposition techniques (like Solovay-Kitaev, Cartan, and quantum Shannon decomposition), and stochastic optimization to implement YAQQ within the Qiskit quantum simulator environment. YAQQ exploits reachability tradeoffs conceptually derived from quantum algorithmic information theory. Our results demonstrate the pragmatic application of identifying gate sets that are advantageous to popularly used quantum gate sets in representing quantum algorithms. Consequently, we demonstrate pragmatic use cases of YAQQ in comparing transversal logical gate sets in quantum error correction codes, designing optimal quantum instruction sets, and compiling to specific quantum processors. © 202

关键词： Cost functions

Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization

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arXiv 2025年

作者： Maus, Natalie Kim, Kyurae Zeng, Yimeng Jones, Haydn Thomas Wan, Fangping Torres, Marcelo Der Torossian de la Fuente-Nunez, Cesar Gardner, Jacob R. Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Chemistry School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Penn Institute for Computational Science University of Pennsylvania PhiladelphiaPA United States

In multi-objective black-box optimization, the goal is typically to find solutions that optimize a set of T black-box objective functions, f1, . . . fT , simultaneously. Traditional approaches often seek a single Pareto-optimal set that balances trade-offs among all objectives. In this work, we introduce a novel problem setting that departs from this paradigm: finding a smaller set of K solutions, where K 1, . . . fT , there is at least one good solution. A motivating example for this problem setting occurs in drug design. For example, we may have T pathogens and aim to identify a set of K © 2025, CC BY-SA.

关键词： Pathogens

Large Scale Multi-Task Bayesian Optimization with Large Language Models

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arXiv 2025年

作者： Zeng, Yimeng Maus, Natalie Jones, Haydn Thomas Tao, Jeffrey Wan, Fangping Torres, Marcelo Der Torossian de la Fuente-Nunez, Cesar Marcus, Ryan Bastani, Osbert Gardner, Jacob R. Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Chemistry School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Penn Institute for Computational Science University of Pennsylvania PhiladelphiaPA United States

In multi-task Bayesian optimization, the goal is to leverage experience from optimizing existing tasks to improve the efficiency of optimizing new ones. While approaches using multi-task Gaussian processes or deep kernel transfer exist, the performance improvement is marginal when scaling to more than a moderate number of tasks. We introduce a novel approach leveraging large language models (LLMs) to learn from, and improve upon, previous optimization trajectories, scaling to approximately 2000 distinct tasks. Specifically, we propose an iterative framework in which an LLM is fine-tuned using the high quality solutions produced by BayesOpt to generate improved initializations that accelerate convergence for future optimization tasks based on previous search trajectories. We evaluate our method on two distinct domains: database query optimization and antimicrobial peptide design. Results demonstrate that our approach creates a positive feedback loop, where the LLM’s generated initializations gradually improve, leading to better optimization performance. As this feedback loop continues, we find that the LLM is eventually able to generate solutions to new tasks in just a few shots that are better than the solutions produced by "from scratch" by Bayesian optimization while simultaneously requiring significantly fewer oracle calls. © 2025, CC BY-SA.

关键词： Structured Query Language

Integer Factorization through Func-QAOA

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arXiv 2023年

作者： Atallah, Mostafa Velmurugan, Haemanth Sharma, Rohan Midha, Siddhant Mamun, Shamim Al Botelho, Ludmila Glos, Adam Salehi, Özlem Department of Physics Faculty of Science Cairo University Giza12613 Egypt Department of Computer Science and Engineering NIT Tiruchirappalli India Department of Applied Geophysics IIT Dhanbad India Department of Electrical Engineering IIT Bombay India Department of Electrical and Electronics Engineering Islamic University of Technology Dhaka Gazipur1704 Bangladesh Joint Doctoral School Silesian University of Technology Akademicka 2A Gliwice Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland QWorld Association Tallinn Estonia

Integer factorization is a significant problem, with implications for the security of widely-used cryptographic schemes. No efficient classical algorithm for polynomial-time integer factorization has been found despite extensive research. Although Peter Shor’s breakthrough quantum algorithm offers a viable solution, current limitations of noisy intermediate-scale quantum (NISQ) computers hinder its practical implementation. To address this, researchers have explored alternative methods for factorization suitable for NISQ devices. One such method is the Quantum Approximate Optimization Algorithm, which treats factoring as an optimization problem defined over binary bits, resulting in various problematic aspects. In this paper, we explore the Func-QAOA approach for factorization, which premises overcoming some of the limitations of previous approaches and allows the incorporation of more advanced factorization techniques. After reviewing the most promising quantum implementations for integer arithmetics, we present a few illustrative examples to demonstrate the efficacy of the Func-QAOA approach and discuss methods to reduce the search space to speed up the optimization process. Copyright © 2023, The Authors. All rights reserved.

关键词： Factorization

Programmable large-scale simulation of lattices with photonic synthetic frequency dimensions

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Programmable large-scale simulation of lattices with photoni...

Conference on Lasers and Electro-Optics (CLEO)

作者： Alen Senanian Logan G. Wright Peter F. Wade Hannah K. Doyle Peter L. McMahon Department of Physics Cornell University Ithaca NY USA School of Applied and Engineering Physics Cornell University Ithaca New York USA NTT Physics and Informatics Laboratories NTT Research Inc Sunnyvale CA USA School of Electrical and Computer Engineering Cornell University Ithaca NY USA Kavli Institute at Cornell for Nanoscale Science Cornell University Ithaca New York USA

We present a programmable analog simulator with up to 10 5 lattice sites capable of simulating 2D and 3D lattices, as well as lattices with non-planar connectivity. We also demonstrate the injection of arbitrary latt... 详细信息

ISBN: (纸本)9781957171258

关键词： Gravity Modulation Modulators Multiplexing Optical measurement Ring resonators

The sensitivity of language models and humans to Winograd schema perturbations 58

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The sensitivity of language models and humans to Winograd sc...

58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Abdou, Mostafa Ravishankar, Vinit Barrett, Maria Belinkov, Yonatan Elliott, Desmond Søgaard, Anders Department of Computer Science University of Copenhagen Department of Informatics University of Oslo John A. Paulson School of Engineering and Applied Sciences Harvard University Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology

ISBN: (纸本)9781952148255

Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of commonsense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones. Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons. Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues. © 2020 Association for Computational Linguistics

关键词： Computational linguistics

Magnetization Dynamics in Nanofiber Networks 11

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Magnetization Dynamics in Nanofiber Networks

11th IEEE International Conference "Nanomaterials: Applications and Properties", NAP 2021

作者： Blachowicz, Tomasz Steblinski, Pawel Grzybowski, Jacek Ehrmann, Andrea Institute of Physics Center for Science and Education Silesian University of Technology Gliwice Poland Faculty of Electronics and Informatics Koszalin University of Technology Koszalin Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Faculty of Engineering and Mathematics Bielefeld University of Applied Sciences Bielefeld Germany

ISBN: (纸本)9781665439077

Magnetic nanofibers are of great interest for applications like data transport and storage as well as in basic research. Especially bent nanofibers, which can unambiguously be produced by electrospinning, show a broad spectrum of possible magnetization reversal processes, depending on bending radius, geometry, magnetic field orientation, etc. Besides these quasistatic processes, dynamic investigations are necessary for investigating data transport properties of magnetic nanofibers. We report on domain wall transport through coupled bent nanowires, forming networks with multiple data inputs and outputs. Our results show diverse phenomena which have to be taken into account during these dynamic processes, such as domain wall instabilities or interference between converging signals, and suggest possible architectures of nanowire-based logics. ©2021 IEEE

关键词： Nanofibers