The rapid advance of Generative AI into software development prompts this empirical investigation of perceptual effects on practice. We study the usage patterns of 147 professional developers, examining perceived correlates of AI tools use, the resulting productivity and quality outcomes, and developer readiness for emerging AI-enhanced development. We describe a virtuous adoption cycle where frequent and broad AI tools use are the strongest correlates of both Perceived Productivity (PP) and quality, with frequency strongest. The study finds no perceptual support for the Quality Paradox and shows that PP is positively correlated with Perceived Code Quality (PQ) improvement. Developers thus report both productivity and quality gains. High current usage, breadth of application, frequent use of AI tools for testing, and ease of use correlate strongly with future intended adoption, though security concerns remain a moderate and statistically significant barrier to adoption. Moreover, AI testing tools' adoption lags that of coding tools, opening a Testing Gap. We identify three developer archetypes (Enthusiasts, Pragmatists, Cautious) that align with an innovation diffusion process wherein the virtuous adoption cycle serves as the individual engine of progression. Our findings reveal that organizational adoption of AI tools follows such a process: Enthusiasts push ahead with tools, creating organizational success that converts Pragmatists. The Cautious are held in organizational stasis: without early adopter examples, they don't enter the virtuous adoption cycle, never accumulate the usage frequency that drives intent, and never attain high efficacy. Policy itself does not predict individuals' intent to increase usage but functions as a marker of maturity, formalizing the successful diffusion of adoption by Enthusiasts while acting as a gateway that the Cautious group has yet to reach.
Nidhal Selmi, Jean-michel Bruel, Sébastien Mosser
et al.
Decision-making is a core engineering design activity that conveys the engineer's knowledge and translates it into courses of action. Capturing this form of knowledge can reap potential benefits for the engineering teams and enhance development efficiency. Despite its clear value, traditional decision capture often requires a significant amount of effort and still falls short of capturing the necessary context for reuse. Model-based systems engineering (MBSE) can be a promising solution to address these challenges by embedding decisions directly within system models, which can reduce the capture workload while maintaining explicit links to requirements, behaviors, and architectural elements. This article discusses a lightweight framework for integrating decision capture into MBSE workflows by representing decision alternatives as system model slices. Using a simplified industry example from aircraft architecture, we discuss the main challenges associated with decision capture and propose preliminary solutions to address these challenges.
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.
With the rapid advancement of artificial intelligence and robotics, the integration of Large Language Models (LLMs) with 3D vision is emerging as a transformative approach to enhancing robotic sensing technologies. This convergence enables machines to perceive, reason, and interact with complex environments through natural language and spatial understanding, bridging the gap between linguistic intelligence and spatial perception. This review provides a comprehensive analysis of state-of-the-art methodologies, applications, and challenges at the intersection of LLMs and 3D vision, with a focus on next-generation robotic sensing technologies. We first introduce the foundational principles of LLMs and 3D data representations, followed by an in-depth examination of 3D sensing technologies critical for robotics. The review then explores key advancements in scene understanding, text-to-3D generation, object grounding, and embodied agents, highlighting cutting-edge techniques such as zero-shot 3D segmentation, dynamic scene synthesis, and language-guided manipulation. Furthermore, we discuss multimodal LLMs that integrate 3D data with touch, auditory, and thermal inputs, enhancing environmental comprehension and robotic decision-making. To support future research, we catalog benchmark datasets and evaluation metrics tailored for 3D-language and vision tasks. Finally, we identify key challenges and future research directions, including adaptive model architectures, enhanced cross-modal alignment, and real-time processing capabilities, which pave the way for more intelligent, context-aware, and autonomous robotic sensing systems.
We introduce the concept of "Design Agents" for engineering applications, particularly focusing on the automotive design process, while emphasizing that our approach can be readily extended to other engineering and design domains. Our framework integrates AI-driven design agents into the traditional engineering workflow, demonstrating how these specialized computational agents interact seamlessly with engineers and designers to augment creativity, enhance efficiency, and significantly accelerate the overall design cycle. By automating and streamlining tasks traditionally performed manually, such as conceptual sketching, styling enhancements, 3D shape retrieval and generative modeling, computational fluid dynamics (CFD) meshing, and aerodynamic simulations, our approach reduces certain aspects of the conventional workflow from weeks and days down to minutes. These agents leverage state-of-the-art vision-language models (VLMs), large language models (LLMs), and geometric deep learning techniques, providing rapid iteration and comprehensive design exploration capabilities. We ground our methodology in industry-standard benchmarks, encompassing a wide variety of conventional automotive designs, and utilize high-fidelity aerodynamic simulations to ensure practical and applicable outcomes. Furthermore, we present design agents that can swiftly and accurately predict simulation outcomes, empowering engineers and designers to engage in more informed design optimization and exploration. This research underscores the transformative potential of integrating advanced generative AI techniques into complex engineering tasks, paving the way for broader adoption and innovation across multiple engineering disciplines.
Mohammed Latif Siddiq, Arvin Islam-Gomes, Natalie Sekerak
et al.
Reproducibility is a cornerstone of scientific progress, yet its state in large language model (LLM)-based software engineering (SE) research remains poorly understood. This paper presents the first large-scale, empirical study of reproducibility practices in LLM-for-SE research. We systematically mined and analyzed 640 papers published between 2017 and 2025 across premier software engineering, machine learning, and natural language processing venues, extracting structured metadata from publications, repositories, and documentation. Guided by four research questions, we examine (i) the prevalence of reproducibility smells, (ii) how reproducibility has evolved over time, (iii) whether artifact evaluation badges reliably reflect reproducibility quality, and (iv) how publication venues influence transparency practices. Using a taxonomy of seven smell categories: Code and Execution, Data, Documentation, Environment and Tooling, Versioning, Model, and Access and Legal, we manually annotated all papers and associated artifacts. Our analysis reveals persistent gaps in artifact availability, environment specification, versioning rigor, and documentation clarity, despite modest improvements in recent years and increased adoption of artifact evaluation processes at top SE venues. Notably, we find that badges often signal artifact presence but do not consistently guarantee execution fidelity or long-term reproducibility. Motivated by these findings, we provide actionable recommendations to mitigate reproducibility smells and introduce a Reproducibility Maturity Model (RMM) to move beyond binary artifact certification toward multi-dimensional, progressive evaluation of reproducibility rigor.
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.
J. S. van Haren, J. S. van Haren, F. L. M. Delbressine
et al.
IntroductionExtra-uterine life support technology could provide a more physiologic alternative for the treatment of extremely premature infants, as it allows further fetal growth and development ex utero. Animal studies have been carried out which involved placing fetuses in a liquid-filled incubator, with oxygen supplied through an oxygenator connected to the umbilical vessels. Hence, by delaying lung exposure to air, further lung development and maturation can take place. This medical intervention requires adjustments to current obstetric procedures to maintain liquid-filled lungs through a so-called transfer procedure.MethodsOur objective was to develop obstetric device prototypes that allow clinicians to simulate this birth procedure to safely transfer the infant from the mother's uterus to an extra-uterine life support system. To facilitate a user-centered design, implementation of medical simulation during early phase design of the prototype development was used. First, the requirements for the procedure and devices were established, by reviewing the literature and through interviewing direct stakeholders. The initial transfer device prototypes were tested on maternal and fetal manikins in participatory simulations with clinicians.Results & discussionThrough analysis of recordings of the simulations, the prototypes were evaluated on effectiveness, safety and usability with latent conditions being identified and improved. This medical simulation-based design process resulted in the development of a set of surgical prototypes and allowed for knowledge building on obstetric care in an extra-uterine life support context.
Al Ansari Mohammed Saleh, Krishnakumari A., Saravanan M.
et al.
This present research deals with optimizing machining parameters and surface quality improvement of Al2024/SiCp composites which are important materials used in the aerospace industry. The optimal quartet of factors was investigated to achieve the best outcomes using Taguchi design approach and includes cutting speed of 105 m/min, feed rate of 0.15 mm/rev, and depth of cut of 0.35 mm with a minimal level of roughness of 0.9 μm. An ANN model has been trained and validated, and a high level of predictive accuracy with an overall accuracy of 100% after 195 epochs has been achieved. The results indicated that systematic experimentation and the application of advanced modeling approaches, including the beneficial configuration of parameters and validated ANN model, can help to achieve a superior surface quality meeting the requirements of the aerospace industry. As a result, manufacturers can benefit from the proposed solutions to optimize their production practices, enhance the performance of components, and contribute to the field of aerospace engineering.
Hoa Nhu Le, Martiela Vaz de Freitas, Dinler Amaral Antunes
Cellular immunity relies on the ability of a T-cell receptor (TCR) to recognize a peptide (p) presented by a class I major histocompatibility complex (MHC) receptor on the surface of a cell. The TCR-peptide-MHC (TCRpMHC) interaction is a crucial step in activating T-cells, and the structural characteristics of these molecules play a significant role in determining the specificity and affinity of this interaction. Hence, obtaining 3D structures of TCRpMHC complexes offers valuable insights into various aspects of cellular immunity and can facilitate the development of T-cell-based immunotherapies. Here, we aimed to compare three popular web servers for modeling the structures of TCRpMHC complexes, namely ImmuneScape (IS), TCRpMHCmodels, and TCRmodel2, to examine their strengths and limitations. Each method employs a different modeling strategy, including docking, homology modeling, and deep learning. The accuracy of each method was evaluated by reproducing the 3D structures of a dataset of 87 TCRpMHC complexes with experimentally determined crystal structures available on the Protein Data Bank (PDB). All selected structures were limited to human MHC alleles, presenting a diverse set of peptide ligands. A detailed analysis of produced models was conducted using multiple metrics, including Root Mean Square Deviation (RMSD) and standardized assessments from CAPRI and DockQ. Special attention was given to the complementarity-determining region (CDR) loops of the TCRs and to the peptide ligands, which define most of the unique features and specificity of a given TCRpMHC interaction. Our study provides an optimistic view of the current state-of-the-art for TCRpMHC modeling but highlights some remaining challenges that must be addressed in order to support the future application of these tools for TCR engineering and computer-aided design of TCR-based immunotherapies.
Diana Popescu, Mariana Cristiana Iacob, Cristian Tarbă
et al.
This article proposes the integration of two novel aspects into the production of 3D-printed customized wrist-hand orthoses. One aspect involves the material, particularly Colorfabb varioShore thermoplastic polyurethane (TPU) filament with an active foaming agent, which allows adjusting the 3D-printed orthoses’ mechanical properties via process parameters such as printing temperature. Consequently, within the same printing process, by using a single extrusion nozzle, orthoses with varying stiffness levels can be produced, aiming at both immobilization rigidity and skin-comfortable softness. This capability is harnessed by 3D-printing the orthosis in a flat shape via material extrusion-based additive manufacturing, which represents the other novel aspect. Subsequently, the orthosis conforms to the user’s upper limb shape after secure attachment, or by thermoforming in the case of a bi-material solution. A dedicated design web app, which relies on key patient hand measurement input, is also proposed, differing from the 3D scanning and modeling approach that requires engineering expertise and 3D scan data processing. The evaluation of varioShore TPU orthoses with diverse designs was conducted considering printing time, cost, maximum flexion angle, comfort, and perceived wrist stability as criteria. As some of the produced TPU orthoses lacked the necessary stiffness around the wrist or did not properly fit the palm shape, bi-material orthoses including polylactic acid (PLA) inserts of varying sizes were 3D-printed and assessed, showing an improved stiffness around the wrist and a better hand shape conformity. The findings demonstrated the potential of this innovative approach in creating bi-material upper limb orthoses, capitalizing on various characteristics such as varioShore properties, PLA thermoforming capabilities, and the design flexibility provided by additive manufacturing technology.
Organic Rankine cycle (ORC) has been considered as one of the most promising technologies in industrial waste heat utilization and power generation. During the actual operation of ORC system, due to the fluctuation of cooling and heat sources, the system operates under off-design conditions in most cases. In this paper, thermodynamic model, heat transfer process description and power equipment model are established to evaluate the operating parameters of ORC for the off-design conditions. Evaporation temperature and condensation temperature are taken as independent parameters for the operation of ORC system. Genetic algorithm is adopted to optimize the independent parameters under the maximum net output power. The results show that the effect of optimizing independent parameters is to make the working fluid at the outlet of the preheater as close as possible to a saturated liquid state, and the working fluid at the inlet of the screw expander should be in a saturated gas state. With the optimal power output increasing by 19.1% for every 5 °C increase in hot water inlet temperature, 9.2% for every 20 kg/s increase in hot water mass flow rate, and 3.9% for every 1 °C decrease in cooling water temperature. The optimization method of off-design operating conditions has good system performance and good engineering application prospects.
Environmental technology. Sanitary engineering, Building construction
Prompt design and engineering has rapidly become essential for maximizing the potential of large language models. In this paper, we introduce core concepts, advanced techniques like Chain-of-Thought and Reflection, and the principles behind building LLM-based agents. Finally, we provide a survey of tools for prompt engineers.
Jialiang Wei, Anne-Lise Courbis, Thomas Lambolais
et al.
Graphical User Interfaces (GUIs) are central to app development projects. App developers may use the GUIs of other apps as a means of requirements refinement and rapid prototyping or as a source of inspiration for designing and improving their own apps. Recent research has thus suggested retrieving relevant GUI designs that match a certain text query from screenshot datasets acquired through crowdsourced or automated exploration of GUIs. However, such text-to-GUI retrieval approaches only leverage the textual information of the GUI elements, neglecting visual information such as icons or background images. In addition, retrieved screenshots are not steered by app developers and lack app features that require particular input data. To overcome these limitations, this paper proposes GUing, a GUI search engine based on a vision-language model called GUIClip, which we trained specifically for the problem of designing app GUIs. For this, we first collected from Google Play app introduction images which display the most representative screenshots and are often captioned (i.e.~labelled) by app vendors. Then, we developed an automated pipeline to classify, crop, and extract the captions from these images. This resulted in a large dataset which we share with this paper: including 303k app screenshots, out of which 135k have captions. We used this dataset to train a novel vision-language model, which is, to the best of our knowledge, the first of its kind for GUI retrieval. We evaluated our approach on various datasets from related work and in a manual experiment. The results demonstrate that our model outperforms previous approaches in text-to-GUI retrieval achieving a Recall@10 of up to 0.69 and a HIT@10 of 0.91. We also explored the performance of GUIClip for other GUI tasks including GUI classification and sketch-to-GUI retrieval with encouraging results.
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.