Large Language Models, particularly decoder-only generative models such as GPT, are increasingly used to automate Software Engineering tasks. These models are primarily guided through natural language prompts, making prompt engineering a critical factor in system performance and behavior. Despite their growing role in SE research, prompt-related decisions are rarely documented in a systematic or transparent manner, hindering reproducibility and comparability across studies. To address this gap, we conducted a two-phase empirical study. First, we analyzed nearly 300 papers published at the top-3 SE conferences since 2022 to assess how prompt design, testing, and optimization are currently reported. Second, we surveyed 105 program committee members from these conferences to capture their expectations for prompt reporting in LLM-driven research. Based on the findings, we derived a structured guideline that distinguishes essential, desirable, and exceptional reporting elements. Our results reveal significant misalignment between current practices and reviewer expectations, particularly regarding version disclosure, prompt justification, and threats to validity. We present our guideline as a step toward improving transparency, reproducibility, and methodological rigor in LLM-based SE research.
The quantum threat to cybersecurity has accelerated the standardization of Post-Quantum Cryptography (PQC). Migrating legacy software to these quantum-safe algorithms is not a simple library swap, but a new software engineering challenge: existing vulnerability detection, refactoring, and testing tools are not designed for PQC's probabilistic behavior, side-channel sensitivity, and complex performance trade-offs. To address these challenges, this paper outlines a vision for a new class of tools and introduces the Automated Quantum-safe Adaptation (AQuA) framework, with a three-pillar agenda for PQC-aware detection, semantic refactoring, and hybrid verification, thereby motivating Quantum-Safe Software Engineering (QSSE) as a distinct research direction.
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.
This study investigates the performance of alkali-activated mortar incorporating slag, fly ash, and desert sand, with a focus on flowability, mechanical properties, sulfate resistance, and microstructural characteristics. A four-factor, three-level orthogonal experimental design was used to analyze the effects of the fly ash substitution rate, alkali content (Na<sub>2</sub>O/b), activator modulus, and desert sand replacement rate for natural sand. The results indicate that increased slag and desert sand contents reduce mortar flowability. Despite this, the mortar exhibits excellent mechanical strength, with compressive strength reaching 77.7 MPa at 28 days and increasing to 89.34 MPa under sulfate exposure. However, after 120 days of sulfate erosion, a decline in strength is observed due to the formation of expansive products such as gypsum and caliche, leading to cracking. Microstructural analyses (XRD, SEM/EDS, MIP) reveal partial dissolution of desert sand under alkali activation, enhancing gel formation and reducing cumulative porosity. The pore structure predominantly consists of harmless pores. These findings demonstrate the potential of slag–fly ash–desert sand alkali-activated mortar as a durable and sustainable material for structural and construction engineering applications, especially in sulfate-rich environments or arid regions where desert sand is abundant.
Discontinuity trace provides critical geological data for engineering design and construction optimization. However, current extraction methods relying on discontinuity intersection fitting are highly sensitive to the segmentation accuracy of individual discontinuity, while trace segment connectivity remains suboptimal. To address these challenges, we propose an ARCG (Adaptive Region Contour Growing) method using 3D point clouds. By dynamically adjusting parameter thresholds, our approach simultaneously extracts both discontinuities and their boundaries. We then evaluate the fitting performance of different discontinuity models using area ratios, identifying the parallelogram as the most suitable representation. The method then detects intersection lines between paired discontinuities through spatial intersection analysis, with dynamic partitioning preserving original geometric properties. Finally, a bidirectional weighted graph-based growth algorithm connects intersection lines belonging to the same discontinuity, generating the final trace results. The proposed method was validated using slope data from two case studies. Results demonstrate that, compared to existing methods and point cloud processing software, our approach achieves robust extraction of complex traces while maintaining high connectivity. Moreover, it improves computational efficiency by 48.8% without compromising trace accuracy. Thus, this method offers a novel solution for the digital characterization of rock mass discontinuity parameters.
Fabian T. Faul, Laurent Cronier, Ali Alhulaymi
et al.
Abstract Filter synthesis is an inverse problem that is traditionally approached rationally by engineering the coupling between selected pairs of lumped resonators. The implicit restriction to spatially disjoint resonators strongly limits the design space, making it challenging to build extremely tunable filters. Here, agile free‐form signal filtering and routing are demonstrated with an alternative purely‐optimization‐based approach leveraging a multi‐parameter programmable system with many spatially overlapping modes. The approach is largely insensitive to system details other than the programmable system configuration. In the fabricated prototype, all ports and tunable meta‐elements are strongly coupled via a quasi‐2D chaotic cavity such that the meta‐elements’ configuration efficiently controls the transfer function between the ports. The all‐metallic device enables low‐loss and ultra‐wideband (UWB) tunability (7.5–13.5 GHz) and guarantees signal‐strength‐independent linearity. First, theoretical predictions about reflectionless and transmissionless scattering modes (including transmissionless exceptional points) are experimentally confirmed. Second, these transfer function zeros are imposed at desired frequencies within an UWB range. Third, low‐loss reflectionless programmable signal routing is achieved. Fourth, the trade‐off between routing fidelity and bandwidth is investigated, achieving 20 dB discrimination over 10 MHz bandwidth. Fifth, UWB‐tunable multi‐band filtering is demonstrated that rejects (< –24 dB) or passes (≥ –1 dB) signals in specified bands whose centers, widths and number are reprogrammable.
ObjectiveGiven that most salt rock strata in China consist of thin-layered salt formations, conventional single-well and single-cavity construction technologies are no longer adequate for the efficient construction of large-size salt cavities. In this context, the application of horizontal multi-step cavity-building technology for salt cavern gas storage can enhance the construction of salt cavities with expanded volumes in salt rock strata of limited thickness. MethodsThis study explored the influence of key parameters on the final shapes of cavities created through the horizontal multi-step cavity-building approach and analyzed both the cavity shape and the construction process from an engineering perspective, thus presenting recommended values for these key parameters. A physical simulation experimental setup was designed to examine cavity expansion patterns during horizontal multi-step cavity construction. Subsequent experiments incorporated various cavity-building parameters to generate horizontal cavities of different shapes. Finally, 3D scanning technology was employed to create complete 3D cavity models based on the cavities obtained from the experiments through mirroring operations. ResultsThe following results were derived from analyzing these 3D cavity models corresponding to various cavity-building parameters. For cavities with equal volumes, variations in water injection flow rates had a significant influence on their height, length, and maximum width. Tubing withdrawal distances had a major impact on the shape of the cavity roofs, while their effect on the overall size of the cavities was relatively minor. Additionally, the air cushion used during cavity construction to protect the roofs resulted in “flat top” shapes, which not only affected the stability of the cavities but also increased the economic costs for cavity construction. ConclusionWater injection rates ranging from 160 m3/h to 240 m3/h are considered rational for horizontal multi-step cavity building. It is recommended to use small tubing withdrawal distances. Additionally, continuous injection of dissolution inhibitors during construction for cavity roof protection is not advised. The research results offer valuable references and guidance for shape design and process parameter optimization of cavities using the horizontal multi-step construction approach for salt cavern gas storage.
Simon Bechert, Simon Aicher, Lyudmila Gorokhova
et al.
Segmented timber shells present a novel building system that utilizes modular, planar building components to create lightweight free-form structures in architecture. Recent advancements in the research field of segmented timber shells pursue, among others, two fundamentally opposing research objectives. 1. The modularity of their building components facilitates the reuse of such structures in response to a changing built environment. 2. Advanced developments aim at establishing segmented timber shells as permanent building structures for sustainable architecture. This paper addresses the first research objective through the successful relocation of the BUGA Wood Pavilion in the context of the proposed methodology of Co-Design for circular construction. The methods and results involve integrative design and engineering processes and advanced quality assessment methods, including structural, geodetic, and physical properties for modular timber constructions. The BUGA Wood Pavilion serves as a building demonstrator for the presented research on segmented shells as lightweight, reusable, and durable timber structures.
Many organisational problems are addressed through systemic change and re-engineering of existing Information Systems rather than radical new design. In the face of widespread IT project failure, devising effective ways to tackle this type of change remains an open challenge. This work discusses the motivation, theoretical foundation, characteristics and evaluation of a novel framework - referred to as POE-$Δ$, which is rooted in design and engineering and is aimed at providing systematic support for representing, structuring and exploring change problems of a socio-technical nature, including implementing their solutions when they exist. We generalise an existing framework of greenfield design as problem solving for application to change problems. From a theoretical perspective,POE-$Δ$ is a strict extension to its parent framework, allowing the seamless integration of greenfield and brownfield design to tackle change problems. A Design Science Research methodology was applied over a decade to define and evaluate POE-$Δ$, with significant case study research conducted to evaluate the framework in its application to real-world change problems of varying criticality and complexity. The results show that POE-$Δ$ exhibits desirable characteristics of a design approach to organisational change and can bring tangible benefits when applied in practice as a holistic and systematic approach to change in socio-technical contexts.
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.
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.
Case studies are a popular and noteworthy type of research study in software engineering, offering significant potential to impact industry practices by investigating phenomena in their natural contexts. This potential to reach a broad audience beyond the academic community is often undermined by deficiencies in reporting, particularly in the context description, study classification, generalizability, and the handling of validity threats. This paper presents a reflective analysis aiming to share insights that can enhance the quality and impact of case study reporting. We emphasize the need to follow established guidelines, accurate classification, and detailed context descriptions in case studies. Additionally, particular focus is placed on articulating generalizable findings and thoroughly discussing generalizability threats. We aim to encourage researchers to adopt more rigorous and communicative strategies, ensuring that case studies are methodologically sound, resonate with, and apply to software engineering practitioners and the broader academic community. The reflections and recommendations offered in this paper aim to ensure that insights from case studies are transparent, understandable, and tailored to meet the needs of both academic researchers and industry practitioners. In doing so, we seek to enhance the real-world applicability of academic research, bridging the gap between theoretical research and practical implementation in industry.
Marco Autili, Martina De Sanctis, Paola Inverardi
et al.
As testified by new regulations like the European AI Act, worries about the human and societal impact of (autonomous) software technologies are becoming of public concern. Human, societal, and environmental values, alongside traditional software quality, are increasingly recognized as essential for sustainability and long-term well-being. Traditionally, systems are engineered taking into account business goals and technology drivers. Considering the growing awareness in the community, in this paper, we argue that engineering of systems should also consider human, societal, and environmental drivers. Then, we identify the macro and technological challenges by focusing on humans and their role while co-existing with digital systems. The first challenge considers humans in a proactive role when interacting with digital systems, i.e., taking initiative in making things happen instead of reacting to events. The second concerns humans having a reactive role in interacting with digital systems, i.e., humans interacting with digital systems as a reaction to events. The third challenge focuses on humans with a passive role, i.e., they experience, enjoy or even suffer the decisions and/or actions of digital systems. The fourth challenge concerns the duality of trust and trustworthiness, with humans playing any role. Building on the new human, societal, and environmental drivers and the macro and technological challenges, we identify a research roadmap of digital systems for humanity. The research roadmap is concretized in a number of research directions organized into four groups: development process, requirements engineering, software architecture and design, and verification and validation.
Diana Robinson, Christian Cabrera, Andrew D. Gordon
et al.
What if end users could own the software development lifecycle from conception to deployment using only requirements expressed in language, images, video or audio? We explore this idea, building on the capabilities that generative Artificial Intelligence brings to software generation and maintenance techniques. How could designing software in this way better serve end users? What are the implications of this process for the future of end-user software engineering and the software development lifecycle? We discuss the research needed to bridge the gap between where we are today and these imagined systems of the future.
With changing dietary habits and increasing awareness of the nutraceutical role of dietary foods, the demand for natural plant proteins and interest in non-traditional protein sources in the food industry are increasing. Industrial hemp, belonging to the plant family Cannabaceae, is cultivated for its fibre and edible seeds. Due to its nutritional value, it has also been used in the food industry and medicine. In particular, hemp seed proteins have drawn considerable attention in both scientific and industrial fields because of their excellent nutraceutical values, superior digestibility, low allergenicity and diverse techno-functional properties. In this review, we provide a summary of the current research progress on the extraction and purification processes, physiochemical properties, nutraceutical functions, and applications of hemp seed proteins. Perspectives in the application of advanced technologies for hemp seed bioactive peptide mining are also discussed. This review provides up-to-date insights into the nutraceutical values, health benefits, and future applications of this emerging plant source protein.