Tanja E. J. Vos, Tijs van der Storm, Alexander Serebrenik
et al.
Software engineering is the invisible infrastructure of the digital age. Every breakthrough in artificial intelligence, quantum computing, photonics, and cybersecurity relies on advances in software engineering, yet the field is too often treated as a supportive digital component rather than as a strategic, enabling discipline. In policy frameworks, including major European programmes, software appears primarily as a building block within other technologies, while the scientific discipline of software engineering remains largely absent. This position paper argues that the long-term sustainability, dependability, and sovereignty of digital technologies depend on investment in software engineering research. It is a call to reclaim the identity of software engineering.
PURPOSE OR GOAL: This study investigates how GenAI can be integrated with a criterion-referenced grading framework to improve the efficiency and quality of grading for mathematical assessments in engineering. It specifically explores the challenges demonstrators face with manual, model solution-based grading and how a GenAI-supported system can be designed to reliably identify student errors, provide high-quality feedback, and support human graders. The research also examines human graders' perceptions of the effectiveness of this GenAI-assisted approach. ACTUAL OR ANTICIPATED OUTCOMES: The study found that GenAI achieved an overall grading accuracy of 92.5%, comparable to two experienced human graders. The two researchers, who also served as subject demonstrators, perceived the GenAI as a helpful second reviewer that improved accuracy by catching small errors and provided more complete feedback than they could manually. A central outcome was the significant enhancement of formative feedback. However, they noted the GenAI tool is not yet reliable enough for autonomous use, especially with unconventional solutions. CONCLUSIONS/RECOMMENDATIONS/SUMMARY: This study demonstrates that GenAI, when paired with a structured, criterion-referenced framework using binary questions, can grade engineering mathematical assessments with an accuracy comparable to human experts. Its primary contribution is a novel methodological approach that embeds the generation of high-quality, scalable formative feedback directly into the assessment workflow. Future work should investigate student perceptions of GenAI grading and feedback.
Alessandro Frascati, Michele Bolla Pittaluga, Octavio E. Sequeiros
et al.
Turbidity currents pose significant threats to offshore seabed infrastructures, including subsea hydrocarbon production facilities and submarine communication cables. These powerful underwater flows can damage pipelines, potentially causing hydrocarbon spills that endanger local communities, the environment, and negatively impact energy production infrastructures. Therefore, a comprehensive understanding of the spatio-temporal development and destructive force of turbidity currents is essential. While numerical computation of 3D flow, sediment transport, and substrate exchange is possible, field-scale simulations are computationally intensive. In this study, we develop a simplified morphodynamic approach to model the flow properties of channelized turbidity currents and the associated trends of sediment accretion and erosion. This model is applied to the Baco–Malaylay submarine system to investigate the dynamics of a significant turbidity current event that impacted a submarine pipeline offshore the Philippines. The modeling results align with available seabed assessments and observed erosion trends of the protective rock berm. Our simplified modeling approach shows good agreement with simulations from a fully 3D numerical model, demonstrating its effectiveness in providing valuable insights while reducing computational demands.
Lucia Porlan-Ferrando, J. David Nuñez-Gonzalez, Alain Ulazia Manterola
et al.
Accurate prediction of extreme waves, particularly the maximum wave height and the ratio between the maximum and significant wave heights of individual waves, is crucial for maritime safety and the resilience of offshore infrastructure. This study employs machine learning (ML) techniques such as linear regression modeling (LM), support vector regression (SVR), long short-term memory (LSTM), and gated recurrent units (GRU) to develop predictive models based on historical data (1990–2024) obtained from a buoy at a specific oceanic location. The results show that the SVR model provides the highest accuracy in predicting the maximum wave height (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>H</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></semantics></math></inline-formula>), achieving a coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) of 0.9006 and mean squared error (MSE) of 0.0185. For estimation of the ratio between maximum and significant wave heights (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>H</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo>/</mo><msub><mi>H</mi><mi>s</mi></msub></mrow></semantics></math></inline-formula>), the SVR and LM models exhibit comparable performance, with MSE values of 0.0229. These findings have significant implications for improving early warning systems, optimizing the structural design of offshore infrastructure, and enhancing the efficiency of energy extraction under changing climate conditions.
Steve P. Lund, Norbert Nowaczyk, Lloyd Keigwin
et al.
IODP Ex. 323 to the Bering Sea recovered a detailed record of Quaternary environmental variability adjacent to Alaska and eastern Siberia. The deep-sea sediment records show a dramatic bimodal environmental record of alternating high versus low magnetic susceptibility. Oxygen isotope records indicate that the interglacials are times of high clastic flux (high magnetic susceptibility) from the adjacent continents into the Bering Sea. Subsequent, more detailed chronostratigraphy indicates that Interstadial 3 and Interglacials 5, 7, and 9 are also intervals of large-amplitude, millennial-scale environmental variability alternating between warmer/wetter and cooler/drier intervals, with a quasi-cyclicity of ~5000 years. Comparative studies of North Atlantic Quaternary sediments associated with ODP Leg 172, with a similar dramatic glacial/interglacial variation in carbonate, show an almost identical millennial-scale (~5000 yrs) pattern of variability that we attribute to alternating warmer/cooler intervals in Interstadial 3 and Interglacials 5, 7, and 9. These results can also be compared to findings for Lake Elgygytgyn in Siberia. The chronology of this record is less certain than those of the other two regions, but it, too, shows large-amplitude changes in magnetic susceptibility in Interstadial 3 and Interglacials 5, 7, and 9 that can be attributed to oscillating warmer/cooler conditions on a millennial scale. These results suggest a coherent, hemispheric-scale pattern of climate variability in interstadial/interglacial periods of the last 400 ka with a quasi-cyclicity of ~5000 years. We speculate that this cyclicity is driven by a harmonic of the chaotic precession Milankovich cyclicity.
Irene Montoya-Blázquez, Ana Rodríguez-Pérez, Borja Martínez-Clavel
et al.
The Jucar is a perennial river with a high sedimentary load which has transferred sediment to the continental shelf in the form of a deltaic lobe since pre-historic times. The aim of this study is to analyze the changes that have occurred in the submerged delta of the Jucar since the nineteenth century. With this aim in mind, five nautical charts were georeferenced, covering the period from 1893 to the present day, from which Digital Elevation Models were generated and compared using Geographic Information Systems. The results indicate that the large-scale contributions of the nineteenth century caused the submerged delta to grow during the cold, dry period of the Little Ice Age. In the mid-twentieth century, the flow and solid load of the river were reduced by the construction of dams, leading to the stabilization of the delta. The bursting of the Tous Dam in 1982 and the ensuing ordinary floods that occurred until its reconstruction, led to large amounts of sediment that counteracted the anthropic action generated by the sediment trap of the dams. The climate of the twenty-first century, characterized by frequent extreme weather events, has allowed the deltaic lobe to continue to grow until the present day since these events increased sediment input to the shelf. Coastal erosion is also observed.
In this study, the potential use of corn-based crude bioethanol was investigated as an alternative energy source for marine fuel oil under increasingly stringent maritime emissions regulations. A small-scale combustion chamber with a capacity of approximately 1 ton was developed, and comparative combustion tests were conducted with various fuel types, including MGO, diesel, kerosene, and BE100. In addition, component analysis was performed and compared using the ISO-8217 method. Complete combustion of the fuel was performed under the same experimental conditions of stable atmospheric pressure and temperature. BE100 exhibited an 8.3% increase in the oxygen concentration and a 5.9% reduction in the carbon dioxide emissions compared to MGO. Despite the low nitrogen oxide (NOx) emissions of MGO at approximately 34.4 ppm, BE100 demonstrated superior reduction potential, with a reading of 1.9 ppm. Sulfur oxides (SOx) were not detected in any of the fuels tested, underscoring the high quality of the currently available low-sulfur MGO. The exhaust gas temperatures were reduced by approximately 44.6% when using BE100, from 367.1 °C for MGO to 203.2 °C for BE100. However, the combustion efficiency of BE100 was 8.3% lower than that of MGO. While crude bioethanol shows promise in reducing exhaust gas emissions, its limited thermal output poses a challenge for direct substitution. Future studies should investigate the development of blended fuels combining bioethanol and conventional marine fuels to improve the performance and sustainability.
The paper entitled "Qualitative Methods in Empirical Studies of Software Engineering" by Carolyn Seaman was published in TSE in 1999. It has been chosen as one of the most influential papers from the third decade of TSE's 50 years history. In this retrospective, the authors discuss the evolution of the use of qualitative methods in software engineering research, the impact it's had on research and practice, and reflections on what is coming and deserves attention.
Jianming Tong, Tianhao Huang, Jingtian Dang
et al.
Homomorphic Encryption (HE) provides strong data privacy for cloud services but at the cost of prohibitive computational overhead. While GPUs have emerged as a practical platform for accelerating HE, there remains an order-of-magnitude energy-efficiency gap compared to specialized (but expensive) HE ASICs. This paper explores an alternate direction: leveraging existing AI accelerators, like Google's TPUs with coarse-grained compute and memory architectures, to offer a path toward ASIC-level energy efficiency for HE. However, this architectural paradigm creates a fundamental mismatch with SoTA HE algorithms designed for GPUs. These algorithms rely heavily on: (1) high-precision (32-bit) integer arithmetic to now run on a TPU's low-throughput vector unit, leaving its high-throughput low-precision (8-bit) matrix engine (MXU) idle, and (2) fine-grained data permutations that are inefficient on the TPU's coarse-grained memory subsystem. Consequently, porting GPU-optimized HE libraries to TPUs results in severe resource under-utilization and performance degradation. To tackle above challenges, we introduce CROSS, a compiler framework that systematically transforms HE workloads to align with the TPU's architecture. CROSS makes two key contributions: (1) Basis-Aligned Transformation (BAT), a novel technique that converts high-precision modular arithmetic into dense, low-precision (INT8) matrix multiplications, unlocking and improving the utilization of TPU's MXU for HE, and (2) Memory-Aligned Transformation (MAT), which eliminates costly runtime data reordering by embedding reordering into compute kernels through offline parameter transformation. CROSS (TPU v6e) achieves higher throughput per watt on NTT and HE operators than WarpDrive, FIDESlib, FAB, HEAP, and Cheddar, establishing AI ASIC as the SotA efficient platform for HE operators. Code: https://github.com/EfficientPPML/CROSS
Foundation models (FMs), particularly large language models (LLMs), have shown significant promise in various software engineering (SE) tasks, including code generation, debugging, and requirement refinement. Despite these advances, existing evaluation frameworks are insufficient for assessing model performance in iterative, context-rich workflows characteristic of SE activities. To address this limitation, we introduce \emph{SWE-Arena}, an interactive platform designed to evaluate FMs in SE tasks. SWE-Arena provides a transparent, open-source leaderboard, supports multi-round conversational workflows, and enables end-to-end model comparisons. The platform introduces novel metrics, including \emph{model consistency score} that measures the consistency of model outputs through self-play matches, and \emph{conversation efficiency index} that evaluates model performance while accounting for the number of interaction rounds required to reach conclusions. Moreover, SWE-Arena incorporates a new feature called \emph{RepoChat}, which automatically injects repository-related context (e.g., issues, commits, pull requests) into the conversation, further aligning evaluations with real-world development processes. This paper outlines the design and capabilities of SWE-Arena, emphasizing its potential to advance the evaluation and practical application of FMs in software engineering.
Marc Bruni, Fabio Gabrielli, Mohammad Ghafari
et al.
Prompt engineering reduces reasoning mistakes in Large Language Models (LLMs). However, its effectiveness in mitigating vulnerabilities in LLM-generated code remains underexplored. To address this gap, we implemented a benchmark to automatically assess the impact of various prompt engineering strategies on code security. Our benchmark leverages two peer-reviewed prompt datasets and employs static scanners to evaluate code security at scale. We tested multiple prompt engineering techniques on GPT-3.5-turbo, GPT-4o, and GPT-4o-mini. Our results show that for GPT-4o and GPT-4o-mini, a security-focused prompt prefix can reduce the occurrence of security vulnerabilities by up to 56%. Additionally, all tested models demonstrated the ability to detect and repair between 41.9% and 68.7% of vulnerabilities in previously generated code when using iterative prompting techniques. Finally, we introduce a "prompt agent" that demonstrates how the most effective techniques can be applied in real-world development workflows.
Shavindra Wickramathilaka, John Grundy, Kashumi Madampe
et al.
The use of diverse mobile applications among senior users is becoming increasingly widespread. However, many of these apps contain accessibility problems that result in negative user experiences for seniors. A key reason is that software practitioners often lack the time or resources to address the broad spectrum of age-related accessibility and personalisation needs. As current developer tools and practices encourage one-size-fits-all interfaces with limited potential to address the diversity of senior needs, there is a growing demand for approaches that support the systematic creation of adaptive, accessible app experiences. To this end, we present AdaptForge, a novel model-driven engineering (MDE) approach that enables advanced design-time adaptations of mobile application interfaces and behaviours tailored to the accessibility needs of senior users. AdaptForge uses two domain-specific languages (DSLs) to address age-related accessibility needs. The first model defines users' context-of-use parameters, while the second defines conditional accessibility scenarios and corresponding UI adaptation rules. These rules are interpreted by an MDE workflow to transform an app's original source code into personalised instances. We also report evaluations with professional software developers and senior end-users, demonstrating the feasibility and practical utility of AdaptForge.
Sebastian Baltes, Florian Angermeir, Chetan Arora
et al.
Large Language Models (LLMs) are now ubiquitous in software engineering (SE) research and practice, yet their non-determinism, opaque training data, and rapidly evolving models threaten the reproducibility and replicability of empirical studies. We address this challenge through a collaborative effort of 22 researchers, presenting a taxonomy of seven study types that organizes the landscape of LLM involvement in SE research, together with eight guidelines for designing and reporting such studies. Each guideline distinguishes requirements (must) from recommended practices (should) and is contextualized by the study types it applies to. Our guidelines recommend that researchers: (1) declare LLM usage and role; (2) report model versions, configurations, and customizations; (3) document the tool architecture beyond the model; (4) disclose prompts, their development, and interaction logs; (5) validate LLM outputs with humans; (6) include an open LLM as a baseline; (7) use suitable baselines, benchmarks, and metrics; and (8) articulate limitations and mitigations. We complement the guidelines with an applicability matrix mapping guidelines to study types and a reporting checklist for authors and reviewers. We maintain the study types and guidelines online as a living resource for the community to use and shape (llm-guidelines$.$org).
Eduard C. Groen, Kazi Rezoanur Rahman, Nikita Narsinghani
et al.
The farming domain has seen a tremendous shift towards digital solutions. However, capturing farmers' requirements regarding Digital Farming (DF) technology remains a difficult task due to domain-specific challenges. Farmers form a diverse and international crowd of practitioners who use a common pool of agricultural products and services, which means we can consider the possibility of applying Crowd-based Requirements Engineering (CrowdRE) for DF: CrowdRE4DF. We found that online user feedback in this domain is limited, necessitating a way of capturing user feedback from farmers in situ. Our solution, the Farmers' Voice application, uses speech-to-text, Machine Learning (ML), and Web 2.0 technology. A preliminary evaluation with five farmers showed good technology acceptance, and accurate transcription and ML analysis even in noisy farm settings. Our findings help to drive the development of DF technology through in-situ requirements elicitation.