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.
This study addresses the energy significance of the coefficient of performance (COP) in vapor compression systems and the practical need to forecast COP quickly and reliably. Because COP directly reflects the amount of cooling delivered per unit of input power, accurate prediction supports energy savings, refrigerant selection, and early stage design decisions, especially for low Global Warming Potential (GWP) refrigerants. Authors develop data-driven models to estimate COP without full thermodynamic calculations. A synthetic dataset of 2,000 samples is generated in Engineering Equation Solver (EES) for four refrigerants (R1234yf, R134a, R290, R600a) by using five inputs: refrigerant type, evaporation temperature, condensing temperature, subcooling, and superheat. Five supervised learning algorithms are trained and compared: Linear Regression, Polynomial Regression, Random Forest, Decision Tree, and Support Vector Machine. The study evaluates model performance using the Coefficient of Determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) based on an 80/20 train/test split. Results show Polynomial Regression (degree 3) delivers the highest accuracy (R² ≈ 0.9999; RMSE ≈ 0.0071; MAE ≈ 0.0053), with Random Forest as the next strongest baseline. The findings suggest that lightweight, well-tuned regressors can provide fast, precise COP predictions, reducing analysis time while guiding system design and parameter optimization. The approach offers an accessible tool for engineers seeking efficient, low-carbon refrigeration solutions.
To address hypothermia in casualties in cold environments, this paper designs a solar-powered rewarming sleeping bag for casualties in extreme cold conditions using phase change materials ODE@CaF2MPCMs and Cu2O@n-C20, which release heat during phase change at low temperatures. Hot water simulation and human experiments were conducted. Results show that in the hot water simulation at 0, 4, and 8°C, with a 40°C initial phase change material temperature, the initial 37°C warm water can be maintained at around 36°C within 3600s, and the bilateral distribution of phase change materials has a higher heating rate than the unilateral distribution. In human experiments, at -5, 0, and 5°C conditions, the average temperature can be maintained at about 35°C within 3600s, and the unilateral distribution of phase change materials has better insulation than the bilateral distribution, with an average temperature increase of 2°C; at -15°C, the average temperature can be maintained above 32°C within 540 minutes. The temperature distribution uniformity analysis shows that the maximum coefficient of variation for unilateral distribution is 0.036, and for bilateral distribution is 0.053, indicating a relatively uniform temperature distribution inside the sleeping bag, with unilateral distribution being more uniform than bilateral distribution. Comprehensive performance assessment shows that the sleeping bag has excellent insulation but needs optimization in convenience; after repeated experiments at -5°C for 30 times, the temperature effect remains almost unchanged, with only a 0.05°C decrease in average human body temperature after 30 uses; moreover, the cost of the sleeping bag is only about 400 yuan, a single heating to 40 ° C consumes about 0.24 ° C , which is low and has good economic benefits.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
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.
Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such experiments and improving the system based on the experimental results still remain manual. These processes are labor-intensive and require multi-domain expertise. To address these challenges and enable anyone to build resilient systems at low cost, this paper proposes ChaosEater, a system that automates the entire CE cycle with Large Language Models (LLMs). It predefines an agentic workflow according to a systematic CE cycle and assigns subdivided processes within the workflow to LLMs. ChaosEater targets CE for software systems built on Kubernetes. Therefore, the LLMs in ChaosEater complete CE cycles through software engineering tasks, including requirement definition, code generation, testing, and debugging. We evaluate ChaosEater through case studies on small- and large-scale Kubernetes systems. The results demonstrate that it consistently completes reasonable CE cycles with significantly low time and monetary costs. Its cycles are also qualitatively validated by human engineers and LLMs.
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.
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.
Corson L. Cramer, Edgar Lara‐Curzio, Amy M. Elliott
et al.
Many energy systems demand heat transfer at high temperatures to keep up with high demand for power, so high‐temperature material that can perform and last under these harsh conditions is needed for heat exchangers. The engineering requirements for these high‐temperature heat exchanger material call for high thermal conductivity, high resistance to fracture, high resistance to creep deformation, environmental stability in environments associated with the application, and high modulus of elasticity while maintaining low cost to make and maintain. Naturally, ceramics are a good solution for this endeavor. In the past, high‐temperature heat exchangers made from ceramics have been used. We provide examples of ceramics in relevant heat exchange applications and provide motivation where additive manufacturing (AM) can improve efficiency. AM for the relevant material is under development, and we provide insight on the AM of ceramic materials and examples of AM heat exchangers keeping cost in mind. The motivation of the review paper is to provide a framework for material and manufacturing selection for high‐temperature heat exchangers for AM to keep up with the demand for better efficiency, better material, better manufacturing, and cost moving forward with AM technology in high‐temperature ceramic heat exchangers.
Microbially Induced Carbonate Precipitation (MICP) shows promise for enhancing soil strength, but environmental factors significantly affect its mechanisms. The feasibility of MICP in challenging conditions, such as the soil around piles in shallow seabeds during winter,characterized by high salinity, low oxygen, and cold temperatures,remains uncertain due to limited research. To address this gap, we employed microfluidic techniques and advanced measurement tools, including Raman spectroscopy and SEM, to assess how these conditions influence bacterial growth, calcium carbonate crystallization, and porous medium permeability through MICP.Our findings indicate that cold temperatures notably hinder bacterial growth, while high salinity and low oxygen also play critical roles, particularly reducing bacterial attachment. In seawater environments, high salinity and cold temperatures significantly affect calcium carbonate crystal shape and type; low oxygen has a lesser impact. Specifically, high salinity reduces crystal quantity by 20.2% with minimal effect on diameter, while low oxygen increases diameter by 20.3% but decreases quantity by 50.9%. Cold temperatures decrease diameter by 36.9% without much effect on quantity.After six injections of the cementation solution, MICP treated samples showed a chemical transformation efficiency of 20.6% compared to DI water, primarily due to cold temperatures (contributing 40.1%). An exponential decline in permeability with increasing calcium carbonate content was observed, leading to the categorization of calcium carbonate generation into fast and slow decay types. This study provides insights for optimizing mineralization in marine conditions and emphasizes how environmental factors impact MICP performance, presenting challenges for engineering applications.
In the present work, helium serves as the primary working fluid within the supercritical Brayton cycle, employed to generate power through a solar power tower system. The conventional Brayton cycle for recovering the wasted heat is combined with a cascaded vapor absorption-compression refrigeration system to increase the overall system’s performance. Additional benefits of this integrated system include the ability to supply enhanced heating and cooling for food storage applications at lower temperatures. A comprehensive analysis of the combined system was conducted based on exergy, energy, and exergo-environmental (3E) factors a using computational technique engineering equation solver. The combined system’s energy, exergy efficiency, and power output were determined to be 28.82%, 39.53%, and 14.865 kW, respectively. The coefficient of performances for cooling and heating were observed as 0.5391 and 1.539 respectively. Approximately 78.18% of the total exergy destruction within the entire plant can be attributed to the solar subsystem, which amounts to 22.763 kW. As direct normal irradiance rises, the environmental impact index decreases from 1.6504 to 0.6801, while the system’s environmental stability factor improves, increasing from 0.3773 to 0.5952. Moreover, the parametric assessment highlights the substantial influence of heliostat and receiver efficiencies, as well as the helium turbine’s inlet temperature, on the trigeneration system’s performance. In addition, compared to previously published research, the current proposed system outperforms supercritical CO2 cycle systems and the conventional steam Rankine cycle systems.
Abstract A novel heat treatment route consisting of a low-temperature solution followed by an over-aging treatment at 500 °C is proposed to develop a high-strength, high-cryogenic-toughness maraging steel by forming an ultrafine-grained martensite (α′) and austenite (γ) dual-phase microstructure. Compared to the same maraging steel subjected to the conventional heat treatment with a mostly martensitic microstructure, the present dual-phase microstructure offers a remarkable increase by 12 times of cryogenic impact energy (~140 J at 77 K), while the yield strength is not reduced obviously. A large amount of ultrafine-grained austenite (about 50% volume fraction) are formed in the present steel due to the reversed transformation of martensite to austenite during the over-aging process at 500 °C. It is surprised that the present steel with such a high fraction of austenite still possesses a high yield strength comparable to the conventional maraging steel with a mostly martensitic microstructure. Intensive nanoprecipitates are found not only in martensite but also in austenite phases, indicating both phases have high strength. This is confirmed by nanoindentation test, showing similar hardness values in both martensite and austenite phases. Such intensive nanoprecipitates in both phases ensure the high yield strength of the present steel. The excellent cryogenic toughness of the present steel mainly origins from: (i) the pronounced amount of reversed austenite that are intrinsically tough due to their face-centered cubic (fcc) structure; (ii) transformation-induced plasticity (TRIP) toughening as some austenite grains transforming to martensite during impact test; (iii) the ultrafine-grained structure of both martensite and austenite phases. The present heat treatment route offers a potential solution for processing large engineering components for cryogenic application that require a long heat treatment duration to achieving uniform mechanical properties in the components.
The need to decrease the gross weight of cryogenic hydrogen fuel systems in future zero emission mobility leads to increasing activities in the field of cryogenic lightweight engineering. Fibre reinforced thermoplastic composite materials (FRT) are considered for cryogenic applications despite their bias towards permeation. However, permeation through plastic materials is of major concern in cryogenic applications. Even tiny fluxes can very much compromise the insulating power of high vacuum spaces required for insulation of cryogenic systems such as tank structures or transfer lines. Hence, it is essential to qualify those FRT in terms of their hydrogen permeability for future usage in mobile cryogenic applications. The conventional concepts for measuring permeability in plastic materials are not sufficient for cryogenic measurements of FRT as shown in a previous paper. For that, a novel laboratory test rig concept was proposed. In this paper, we validate this concept and show its eligibility for measuring permeation of hydrogen through thermoplastic materials. Therefore, we report results of helium permeation through polytetrafluoroethylene (PTFE) at room temperature and compare those results to literature permeability data.
The organic Rankine cycle (ORC) has received a lot of attention in recent years due to its wide application in energy recovery and the use of low‐temperature energy sources. In this article, the energy, exergy, and economic analyses of a high‐temperature ORC (HTORC) in combined heat and power production mode have been performed. In this system, the heating water at 90°C for domestic or industrial purposes is provided in the HTORC condenser. Two working fluids, hexamethyldisiloxane (MM) and siloxane mixture (MDM), have been evaluated and compared in HTORC. The system has been modeled in engineering equation solver software and key parameters such as energy efficiency and exergy of the system, output power, heat‐to‐power ratio, and levelized cost of electricity (LCOE) have been calculated. The energy and exergy efficiency of the system for the two working fluids MM and MDM are equal to 40.8%, 45.6%, 22.45%, and 19.3%, respectively. From the point of view of energy and exergy, the working fluid MM performs better. The LCOE of the system with MM working fluid is equal to 0.5946 US$/kWh, which is slightly higher than MDM working fluid (0.5702 US$/kWh).
The performance of a vapor compression refrigeration system (VCRS)-based residential air conditioner operating in a high-ambient temperature (HAT) country was investigated using six zero-ODP (ozone depletion potential) refrigerants as replacements to R22. The non-flammable alternative refrigerants considered in the present research were R134a, R404A, R407C, R410A, R448A, and R507A. Using the basic conservation laws, the VCRS was modeled during steady-state operation and solved using engineering equation solver (EES) software. Coefficient of performance (COP), pressures and temperatures at compressor suction and discharge, Global Warming Potential (GWP), critical pressure and temperature, compressor pressure ratio, volumetric cooling capacity (VCC) specific cooling capacity (SCC), and refrigeration effect were utilized as assessment criteria for the alternative refrigerants considered. From these refrigerants, the highest values of suction pressure, discharge temperature, and condenser pressure were attained by R410A. In addition, the discharge temperatures for all refrigerants, except R134a, were all higher than their corresponding critical values, causing a quicker drop in the VCRS’s performance. As an alternative refrigerant, R407C showed the highest SCC of 141.0 kJ/kg followed directly by 139.2 and 138.0 kJ/kg for R410A and R448A, respectively. A reverse trend was found for VCC with respective values of 4722 and 3775 kJ/m3 for R410A and R448A. Lower volume flow rates and smaller-sized compressors are expected for higher VCC refrigerants. The same trend was found for the compressor’s specific work input and condenser’s specific heat transfer with values of (51.14, 46.82, and 45.38 kJ/kg) and (190.3, 187.8, and 183.4 kJ/kg) for R410A, R407C, and R448A, respectively. For applications in HAT countries, larger condenser’s specific heat transfer makes the refrigerant more applicable. Conversely, with respect to COP, refrigerant R134a with a value of 3.075 was the superior alternative followed by R448A and R407C with respective COPs of 3.042 and 3.011. Based on the overall assessment in terms of environmental obligation, COP, compressor input power, refrigerant flow rate required, and all the evaluations made in this research, refrigerant R448A was recommended as the most appropriate substitute to R22 which can effectively be used in residential air conditioners in a HAT country.
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.