software development has emerged as a critical bottleneck in the human use of automatic data processing. Beginning with ad hoc heuristic methods of design and implementation of software systems, problems of software m...
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Stiffened plate structures, which enhance overall strength and stiffness, are critical in engineering applications, with stability under compressive loads being a key design consideration. While numerical approaches l...
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Stiffened plate structures, which enhance overall strength and stiffness, are critical in engineering applications, with stability under compressive loads being a key design consideration. While numerical approaches like finite element analysis are effective, their high computational cost drives interest in data-driven methods for failure load prediction. However, such methods often face challenges related to data quality, generalization and physical interpretability. This study proposed a Physics-Informed Neural Network (PINN) for predicting the compressive buckling failure of stiffened plates, and then developed the SiPFLP software based on the PINN. Firstly, compressive tests on laser-welded stiffened plates, combined with electrical and optical measurement, were conducted to elucidate failure mechanisms. A validated post-buckling analysis was then used to examine how geometric and material parameters influence buckling failure loads. These qualitative physical laws were employed to guide the training of the PINN for failure load prediction. The PINN was subsequently integrated with PyQt5 to develop the SiPFLP software. This research enhances the understanding of buckling failure in laser-welded stiffened plates and provides an innovative, physics-guided and computationally efficient tool for predicting failure loads.
ContextWith the fast advancement of techniques in artificial intelligence (AI) and of the target infrastructures in the last decades, AI software is becoming an undeniable part of software system projects. As in most ...
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ContextWith the fast advancement of techniques in artificial intelligence (AI) and of the target infrastructures in the last decades, AI software is becoming an undeniable part of software system projects. As in most cases in history, however, development methods and guides follow the advancements in technology with phase *** an aim to elicit and integrate available evidence from AI software development practices into a process model, this study synthesizes the contributions of the validation studies reported in scientific *** applied a systematic literature review to retrieve, select, and analyze the primary studies. After a comprehensive and rigorous search and scoping review, we identified 82 studies that make various contributions in relation to AI software development practices. To increase the effectiveness of the synthesis and the usefulness of the outcome, for detailed analysis, we selected 14 primary studies (out of 82) that empirically validated their *** carefully reviewed the selected studies that validate proposals on approaches/models, methods/techniques, tasks/phases, lessons learned/best practices, or workflows. We mapped the steps/activities in these proposals with the knowledge areas in SWEBOK, and using the evidence in this mapping and the primary studies, we synthesized a process model that integrates activities, artifacts, and roles for AI-enabled software system *** the best of our knowledge, this is the first study that proposes such a process model by eliciting and gathering the contributions of the validation studies in a bottom-up manner. We expect that the output of this synthesis will be input for further research to validate or improve the process model.
This article explores the transformative potential of gamification in software development education, aiming to address the challenges of sustaining student engagement. Traditional teaching methods, foundational yet a...
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This article explores the transformative potential of gamification in software development education, aiming to address the challenges of sustaining student engagement. Traditional teaching methods, foundational yet at times lacking in dynamism, are juxtaposed against the immersive experience offered by gamification. The case study unfolds in a renewable energy degree programming class, where Genially games inject vitality into the learning environment. Students, organized into groups, embark on a virtual board game journey using coding challenges. Game formats such as hangman, 3 in a row, snakes and ladders, and Jumanji introduce basic programming concepts, strategically progressing in complexity. Genially serves as a versatile digital tool, crafting interactive board games that not only teach coding but transform learning into a thrilling adventure. Research questions delve into the impact of gamification on student accomplishment, challenge perception, competence, guidance, immersion, playfulness, and social involvement. By examining the experiential journey of a gamified programming class, the article contributes to the growing knowledge on gamification in education. This transformative approach not only teaches programming concepts but also immerses students in a dynamic and engaging adventure, addressing the evolving landscape of education with a focus on motivation and personalized learning experiences.
Quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. To realize this promise, these new capabilities need software solutions that are...
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Quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. To realize this promise, these new capabilities need software solutions that are able to effectively harness its power. However, developers may face significant challenges when developing and executing quantum software due to the limited availability of quantum computer hardware, high computational demands of simulating quantum computers on classical systems, and complicated technology stack to enable currently available accelerators into development environments. These limitations make it difficult for the developer to create an efficient workflow for quantum software development. In this paper, we investigate the potential of using remote computational capabilities in an efficient manner to improve the workflow of quantum software developers, by lowering the barrier of moving between local execution and computationally more efficient remote hardware and offering speedup in execution with simulator surroundings. The goal is to allow the development of more complex circuits and to support an iterative software development approach. In our experiment, with the solution presented in this paper, we have obtained up to 5 times faster circuit execution runtime, and enabled qubit ranges from 21 to 29 qubits with a simple plug-and-play kernel for the Jupyter notebook.
The development of safety-critical systems is heavily governed by domain-specific standards. In the aerospace industry, the DO-178C-software Considerations in Airborne Systems and Equipment Certification-serves as the...
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The development of safety-critical systems is heavily governed by domain-specific standards. In the aerospace industry, the DO-178C-software Considerations in Airborne Systems and Equipment Certification-serves as the primary certification standard used by agencies such as the FAA and EASA to review and approve software-based systems. Although DO-178C aims to ensure system safety while providing evidence for certification, it does not prescribe a specific software development process, allowing flexibility for traditional Waterfall, Agile, or hybrid methods with appropriate adaptations for the aerospace context. This study proposes Scrum4DO178C, an Agile process based on Scrum, to meet the demanding requirements of aerospace software, including safety, robustness, reliability, and integrity. Scrum4DO178C introduces novel process enhancements specifically tailored to meet these criticality needs, while aligning with the standard. Unlike previous proposals that lack detail, this research presents a comprehensive, validated process applied in a real-world industry project at the highest criticality level (Level A - Catastrophic), offering insights beyond theoretical scenarios. The findings demonstrated that the Scrum4DO178C process improves project performance, allows frequent and manageable requirement changes, reduces Verification & Validation (V&V) effort, and increases efficiency while maintaining full compliance with DO-178C. The study also identifies areas for further improvement and suggests exploring the process in additional case studies, both within the aerospace industry and other domains with similarly stringent safety-critical requirements. Finally, it confirms that appropriate automation, namely for documentation production, is a central element to further improve the process.
Artificial intelligence (AI) is a powerful tool that can play a crucial role in software development. The integration of AI in that domain has allowed various AI-powered tools to emerge and have the potential to posit...
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In order to create software that is reliable, efficient, and of the highest quality, it is imperative to predict and address bugs during the development stage. Early detection of faults is crucial;yet developing a cos...
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In order to create software that is reliable, efficient, and of the highest quality, it is imperative to predict and address bugs during the development stage. Early detection of faults is crucial;yet developing a cost-effective and successful advanced bug prediction model presents challenges. This research endeavor aims to achieve precise bug identification by exploring the utilization of various machine learning techniques on training and testing datasets. Multiple machine learning methods have been devised to identify and learn from software defects. This study employs machine learning techniques to conduct a comprehensive examination of software bug detection, offering valuable insights to the software industry. It synthesizes existing research on bug prediction, detailing different methods and highlighting their effectiveness, advantages, and limitations. This comprehensive analysis offers valuable guidance to researchers and software developers seeking to enhance bug detection methods for the creation of higher-quality software.
software development model is the way that software development is organized and managed, which has a direct effect on the quality of software development. It is also one of the most significant topics of research in ...
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