Alexandra González, Xavier Franch, Silverio Martínez-Fernández
Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face (HF) and new ones continuously being created, it is infeasible to identify SE models without a dedicated catalogue. To address this gap, we present SEMODS: an SE-focused dataset of 3,427 models extracted from HF, combining automated collection with rigorous validation through manual annotation and large language model assistance. Our dataset links models to SE tasks and activities from the software development lifecycle, offering a standardized representation of their evaluation results, and supporting multiple applications such as data analysis, model discovery, benchmarking, and model adaptation.
Mairieli Wessel, Daniel Feitosa, Sangeeth Kochanthara
Rising publication pressure and the routine use of generative AI tools are reshaping how software engineering research is produced, assessed, and taught. While these developments promise efficiency, they also raise concerns about skill degradation, responsibility, and trust in scholarly outputs. This vision paper employs Design Fiction as a methodological lens to examine how such concerns might materialise if current practices persist. Drawing on themes reported in a recent community survey, we construct a speculative artifact situated in a near future research setting. The fiction is used as an analytical device rather than a forecast, enabling reflection on how automated assistance might impede domain knowledge competence, verification, and mentoring practices. By presenting an intentionally unsettling scenario, the paper invites discussion on how the software engineering research community in the future will define proficiency, allocate responsibility, and support learning.
The inclusion of internship courses in Software Engineering (SE) programs is essential for closing knowledge gaps and improving graduates' readiness for the software industry. Our study focuses on year-long internships at RMIT University (Melbourne, Australia), which offers in-depth industry engagement. We analysed how the course evolved over the last 10 years to incorporate students' needs and summarised the lessons learned that can be helpful for other educators supporting internship courses. Our qualitative analysis of internship data based on 91 reports during 2023-2024 identified three challenge themes the students faced, and which courses were found by students to be particularly beneficial during their internships. On this basis, we proposed recommendations for educators and companies to help interns overcome challenges and maximise their learning experience.
Massimiliano Di Penta, Kelly Blincoe, Marsha Chechik
et al.
As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers. These barriers threaten the inclusivity and global diversity that have contributed to the success of the SE community. Based on survey data, we identify concrete actions the ACM Special Interest Group on Software Engineering (SIGSOFT) can take to address these challenges, including improving transparency around conference funding, experimenting with hybrid poster presentations, and expanding outreach to underrepresented regions. By implementing these changes, SIGSOFT can help ensure the software engineering community remains accessible and welcoming.
Ana B. M. Bett, Thais S. Nepomuceno, Edson OliveiraJr
et al.
Context: The empirical software engineering (ESE) community has contributed to improving experimentation over the years. However, there is still a lack of rigor in describing controlled experiments, hindering reproducibility and transparency. Registered Reports (RR) have been discussed in the ESE community to address these issues. A RR registers a study's hypotheses, methods, and/or analyses before execution, involving peer review and potential acceptance before data collection. This helps mitigate problematic practices such as p-hacking, publication bias, and inappropriate post hoc analysis. Objective: This paper presents initial results toward establishing an RR template for Software Engineering controlled experiments using the Open Science Framework (OSF). Method: We analyzed templates of selected OSF RR types in light of documentation guidelines for controlled experiments. Results: The observed lack of rigor motivated our investigation of OSF-based RR types. Our analysis showed that, although one of the RR types aligned with many of the documentation suggestions contained in the guidelines, none of them covered the guidelines comprehensively. The study also highlights limitations in OSF RR template customization. Conclusion: Despite progress in ESE, planning and documenting experiments still lack rigor, compromising reproducibility. Adopting OSF-based RRs is proposed. However, no currently available RR type fully satisfies the guidelines. Establishing RR-specific guidelines for SE is deemed essential.
This study investigates how the digital economy takes shape within and influences the resilience of the traditional real economy. Integrating theoretical analysis with practical application insights, it explores the pervasiveness of digital technologies across the entire value chain—encompassing production, distribution, consumption, and even post-consumption services. The analysis reveals that digital technologies significantly enhance the adaptability and recovery capacity of economic systems amid shocks and disasters, primarily by optimizing resource utilization efficiency, stimulating innovative activities, and fostering inter-organizational collaboration. Furthermore, this study identifies and elaborates on the major obstacles hindering the transition from a traditional economic system to a digital-driven model, including the digital divide, technological integration barriers, and regulatory mismatches. It ultimately proposes a comprehensive policy framework aimed at creating a more inclusive, secure, and sustainable development model that maximizes the digital economy’s empowering effect on real economy resilience.
Abstract The rally to mitigate growing carbon emissions and climate change necessitates decarbonization strategies, with hydrogen emerging as a key candidate option across multiple sectors. This literature review examines the current state of the road to the hydrogen economy, including production, implementation, and associated risks. Hydrogen's versatility in industry, transportation, and energy storage is highlighted, alongside the challenges of transitioning from fossil fuel-based production. It explores hydrogen's potential across various sectors, including transportation, industry, and energy storage, while acknowledging the challenges associated with its production, storage, and implementation. The review analyzes the current state of hydrogen technologies, differentiating between green, blue, and grey hydrogen production methods, and highlights advancements in production techniques like thermochemical water splitting. Key findings shows that while green hydrogen offers the cleanest pathway, high production costs and infrastructure limitations remain significant barriers to widespread adoption. The study also addresses safety concerns and public perception, emphasizing the need for robust risk assessment methodologies and management approaches. Furthermore, the review underscores the importance of technological innovations, such as high-temperature electrolysis and synergies with renewable energy sources, to enhance efficiency and sustainability. Policy recommendations include financial incentives, regulatory frameworks, and international cooperation to accelerate hydrogen adoption and balance its development with other low-carbon solutions.
Nining Nur Alaini, Dede Hidayatullah, Saefuddin Saefuddin
et al.
The Mbojo community in West Nusa Tenggara has a folklore entitled "Oi Wobo". Like fairy tales or myths that always contain messages, this "Oi Wobo" folklore reflects the values of the Mbojo community. This Oi Wobo story becomes important when we look back at why the Mbojo people feel the need to pass down and teach these values through folklore, and see whether the old concepts of ancestral heritage contained in the Oi Wobo story today can be contextualized or relevant to the challenges that exist today. Through semiotic studies and literary sociology conducted, it is known that Oi Wobo folklore, one of them, reveals values related to the concept of leader and leadership of the Mbojo community. The concept of leader and leadership of the Mbojo community reflected in the folklore "Oi Wobo" adheres to the concept of nggusu waru. From the studies that have been conducted, it can also be concluded that the concept of leadership of the Mbojo community, which is based on the philosophy of nggusu waru, can be recontextualized or relevant to today's leadership challenges. The ignorance and incomprehension of the Mbojo people towards these values causes that thenoble values related to leaders and leadership are not widely realized in today's leadership. Thus, this study is important so that the values of the noble ancestral heritage can be known, understood and reused to achieve a more quality community life.
Abstract The migration of rivers in permafrost landscapes has critical implications for riverine infrastructure, ecosystem stability, and carbon cycling, yet its magnitude and underlying mechanisms remain poorly understood. Here, we leverage four decadal satellite imagery, hydrological observations, and permafrost modeling to investigate meander migration dynamics on the Tibetan Plateau. Our data show that the migration rates of permafrost rivers have increased by 34.6% from 1987 to 2022, in response to the combined effects of increased discharge, riverbank destabilization driven by ground ice melt and extended thawing days (increased by 35 days). In contrast, rivers flowing across seasonally frozen ground exhibited a decline in migration rate by 11.1%, driven by vegetation greening and riverbank stabilization. In a future warming climate for the Tibetan Plateau, the migration rates of permafrost rivers are anticipated to further accelerate, potentially threatening riverine infrastructure safety and aquatic ecosystems, and intensifying the permafrost carbon cycle.
Tatiana Вeridze, Tetiana Melikhova, Мaryna Аdamenko
et al.
The article investigates and models the process of forming financial results of an enterprise operating in technogenic conditions on the basis of the social responsibility of business. It is proposed to consider technogenic conditions that are formed directly by the enterprise itself as self-pollution. The formed economic and mathematical model allowed to analyze the financial results of an enterprise operating in technogenic conditions. The optimal value of technogenic self-pollution was determined. It is shown that when the values of technogenic pollution are less than the optimal value, the profit of the enterprise increases. The application of methods of similarity theory made it possible to present the economic and mathematical model of the enterprise's profit in a dimensionless form, replacing individual parameters with analytical complexes that are recorded in the form of products. This allowed for a reduction in the volume of necessary calculations during research. The importance of the synergy of environmental standards and the financial component of the enterprise, in combination with the component of social responsibility of business, is proven.
The methodology for the formation of economic and mathematical modeling of the financial component of enterprises operating in technogenic conditions has been developed. The possibility of using differential equations at a qualitative level is shown. This made it possible to determine the feasibility of using "soft" economic and mathematical models. It has been proven that there is an optimal value of the enterprise's income with the corresponding value of man-made pollution, which is characterized by structural stability.
Economic and mathematical modeling of the financial indicators of the enterprise's activity in relation to technogenic self-pollution has made it possible to determine, using a dimensionless approach, the optimal conditions of the cost-target components. The conducted research provides enterprises with a tool for forming a financial strategy for their activities in technogenic conditions on the basis of social responsibility.
Systems engineering (SE) is evolving with the availability of generative artificial intelligence (AI) and the demand for a systems-of-systems perspective, formalized under the purview of mission engineering (ME) in the US Department of Defense. Formulating ME problems is challenging because they are open-ended exercises that involve translation of ill-defined problems into well-defined ones that are amenable for engineering development. It remains to be seen to which extent AI could assist problem formulation objectives. To that end, this paper explores the quality and consistency of multi-purpose Large Language Models (LLM) in supporting ME problem formulation tasks, specifically focusing on stakeholder identification. We identify a relevant reference problem, a NASA space mission design challenge, and document ChatGPT-3.5's ability to perform stakeholder identification tasks. We execute multiple parallel attempts and qualitatively evaluate LLM outputs, focusing on both their quality and variability. Our findings portray a nuanced picture. We find that the LLM performs well in identifying human-focused stakeholders but poorly in recognizing external systems and environmental factors, despite explicit efforts to account for these. Additionally, LLMs struggle with preserving the desired level of abstraction and exhibit a tendency to produce solution specific outputs that are inappropriate for problem formulation. More importantly, we document great variability among parallel threads, highlighting that LLM outputs should be used with caution, ideally by adopting a stochastic view of their abilities. Overall, our findings suggest that, while ChatGPT could reduce some expert workload, its lack of consistency and domain understanding may limit its reliability for problem formulation tasks.
Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.
Jake Zappin, Trevor Stalnaker, Oscar Chaparro
et al.
This position paper examines the substantial divide between academia and industry within quantum software engineering. For example, while academic research related to debugging and testing predominantly focuses on a limited subset of primarily quantum-specific issues, industry practitioners face a broader range of practical concerns, including software integration, compatibility, and real-world implementation hurdles. This disconnect mainly arises due to academia's limited access to industry practices and the often confidential, competitive nature of quantum development in commercial settings. As a result, academic advancements often fail to translate into actionable tools and methodologies that meet industry needs. By analyzing discussions within quantum developer forums, we identify key gaps in focus and resource availability that hinder progress on both sides. We propose collaborative efforts aimed at developing practical tools, methodologies, and best practices to bridge this divide, enabling academia to address the application-driven needs of industry and fostering a more aligned, sustainable ecosystem for quantum software development.
Software development relies heavily on text-based communication, making sentiment analysis a valuable tool for understanding team dynamics and supporting trustworthy AI-driven analytics in requirements engineering. However, existing sentiment analysis tools often perform inconsistently across datasets from different platforms, due to variations in communication style and content. In this study, we analyze linguistic and statistical features of 10 developer communication datasets from five platforms and evaluate the performance of 14 sentiment analysis tools. Based on these results, we propose a mapping approach and questionnaire that recommends suitable sentiment analysis tools for new datasets, using their characteristic features as input. Our results show that dataset characteristics can be leveraged to improve tool selection, as platforms differ substantially in both linguistic and statistical properties. While transformer-based models such as SetFit and RoBERTa consistently achieve strong results, tool effectiveness remains context-dependent. Our approach supports researchers and practitioners in selecting trustworthy tools for sentiment analysis in software engineering, while highlighting the need for ongoing evaluation as communication contexts evolve.
UI automation is a useful technique for UI testing, bug reproduction, and robotic process automation. Recording user actions with an application assists rapid development of UI automation scripts, but existing recording techniques are intrusive, rely on OS or GUI framework accessibility support, or assume specific app implementations. Reverse engineering user actions from screencasts is non-intrusive, but a key reverse-engineering step is currently missing - recognizing human-understandable structured user actions ([command] [widget] [location]) from action screencasts. To fill the gap, we propose a deep learning-based computer vision model that can recognize 11 commands and 11 widgets, and generate location phrases from action screencasts, through joint learning and multi-task learning. We label a large dataset with 7260 video-action pairs, which record user interactions with Word, Zoom, Firefox, Photoshop, and Windows 10 Settings. Through extensive experiments, we confirm the effectiveness and generality of our model, and demonstrate the usefulness of a screencast-to-action-script tool built upon our model for bug reproduction.
Climate constraints impose greenhouse gas emissions mitigation and the passenger cars have considerable contributions to give it. To improve engine efficiency of vehicles equipped with conventional powertrains, many technologies are available but with limited individual contribution. The experimental assessment of some technology regarding fuel economy measurement results is sometimes lower than test uncertainties. This study proposes a methodology to compensate the fuel economy for two test uncertainties: vehicle speed variations and battery recharging. The proposed method can be applied when investigating the effects of different vehicle design changes, including engine powercell design, in this work, the proposed method is demonstrated on the test of oils with two different viscosities, which allows a more detailed analysis. Applying the proposed methodology to experimental results changed from a raw 1.3% reduction in fuel consumption measured in the combined cycle to 2.9% compensated by these two uncertainties considering a vehicle numerical simulation model, a more plausible and reliable result.
This paper explores the intricate relationship between man and nature in Ernest Hemingway’s The Old Man and the Sea through the lens of deep ecology. It challenges the traditional anthropocentric interpretation of the novella, proposing that the protagonist Santiago’s struggle is not merely a tale of human triumph over nature but a journey towards understanding and coexisting with the natural world. By applying the principles of deep ecology, the study reveals how Santiago’s evolving relationship with the marlin and other sea elements reflects a broader ecological consciousness. The analysis also draws parallels between Santiago’s experience and the Biblical narrative of Jonah, suggesting that Santiago’s success is not solely due to his physical endurance but also the cosmic forces that aid him. This paper ultimately rethinks the themes of struggle and victory in the novella, emphasising the need for a harmonious relationship between humanity and the environment.
Garlic is a versatile vegetable commonly grown in subtropical and highland agroecosystems, which is utilized for its culinary, medicinal, and spice properties. The use of garlic as a medicinal aid can be traced back to ancient times. The health benefits of garlic production are attributed to its antiviral, antibacterial, and antifungal properties. The use of garlic is prevalent in both traditional and modern healthcare systems, where it is used to treat a wide range of conditions. Numerous studies have reported the therapeutic properties of garlic, and its effectiveness has been demonstrated in clinical trials. The growing global interest in health and wellness, the widespread use of garlic as a spice, and its potential economic, social, and health benefits have contributed to a surge in its demand worldwide. This review aims to provide a comprehensive overview of the scientific literature on the morphological descriptions of garlic and its nutritional and health significance.
Abstract In this study, the performance of a novel organic tea compost developed for the first time in the world from raw tea waste from tea processing factories and enriched with worms, beneficial microorganisms, and enzymes was tested in comparison to chemical fertilizers in tea plantations in Rize and Artvin provinces, where the most intensive tea cultivation is carried out in Turkey. In the field trials, the developed organic tea vermicompost was incorporated into the root zones of the plants in the tea plantations in amounts of 1000 (OVT1), 2000 (OVT2) and 4000 (OVT4) (kg ha-1). The experimental design included a control group without OVT applications and positive controls with chemical fertilizers (N: P: K 25:5:10, (CF) 1200 kg ha-1) commonly used by local growers. The evaluation included field trials over two years. The average yields obtained in two-year field trials in five different areas were: Control (6326), OVT1 (7082), OVT2 (7408), OVT4 (7910), and CF (8028) kg ha-1. Notably, there was no significant statistical difference in yields between the organic (at 4000 kg ha-1 ) and chemical fertilizers (at 1200 kg ha-1). The highest nutrient contents were obtained when CF and OVT4 were applied. According to the average values across all regions, the application of OVT4 increased the uptake of 63% N, 18% K, 75% P, 21% Mg, 19% Na, 29% Ca, 28% Zn, 11% Cu and 24% Mn compared to the control group. The application of chemical fertilizers increased the uptake of 75% N, 21% K, 75% P, 21% Mg, 28% Na, 27% Ca, 30% Zn, 18% Cu and 31% Mn compared to the control group. The organic fertilizer treatment had the lowest levels of antioxidants compared to the control groups and the chemical fertilizers. It was also found that the organic fertilizer increased the levels of amino acids, organic acids and chlorophyll in the tea plant. Its low antioxidant activity and proline content prepared them for or protected them from stress conditions. With these properties, the biotechnologically developed organic tea compost fertilizer has proven to be very promising for tea cultivation and organic plant production.