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.
The rapid integration of Large Language Models (LLMs) into software development workflows has given rise to a new class of AI-assisted coding tools, such as Claude-Code, Codex, and Gemini CLIs. While promising significant productivity gains, the engineering process of building these tools, which sit at the complex intersection of traditional software engineering, AI system design, and human-computer interaction, is fraught with unique and poorly understood challenges. This paper presents the first empirical study of engineering pitfalls in building such tools, on a systematic, manual analysis of over 3.8K publicly reported bugs in the open-source repositories of three AI-assisted coding tools (i.e., Claude-Code, Codex, and Gemini CLIs) on GitHub. Specifically, we employ an open-coding methodology to manually examine the issue description, associated user discussions, and developer responses. Through this process, we categorize each bug along multiple dimensions, including bug type, bug location, root cause, and observed symptoms. This fine-grained annotation enables us to characterize common failure patterns and identify recurring engineering challenges. Our results show that more than 67% of the bugs in these tools are related to functionality. In terms of root causes, 36.9% of the bugs stem from API, integration, or configuration errors. Consequently, the most commonly observed symptoms reported by users are API errors (18.3%), terminal problems (14%), and command failures (12.7%). These bugs predominantly affect the tool invocation (37.2%) and command execution (24.7%) stages of the system workflow. Collectively, our findings provide a critical roadmap for developers seeking to design the next generation of reliable and robust AI coding assistants.
Multithreading is widely used in microcontroller unit (MCU) chips. Multithreaded hardware is composed of multiple identical single threads and provides instructions to different threads. Using the concept of thread-level parallelism (TLP), pauses are compensated for during single-thread operation to increase the throughput at the same unit. The principle of pipelined management is to use instruction-level parallelism (ILP) to split the MCU into multiple stages. When an instruction is given in a certain stage, other instructions are provided to operate in other idle stages and improve their execution efficiency. Based on the four-thread and pipelined RISC-V MCU architecture, we analyzed the instruction types of three benchmarks, i.e., Coremark, SHA, and Dijkstra. A total of 94% of the instructions use the arithmetic logic unit (ALU). Based on the executable four-thread architecture, we developed two to four RISC-V architectures with different numbers of ALUs and a dispatch algorithm. This architecture allows for the simultaneous delivery of multiple instructions, enabling parallel processing of instructions and increasing efficiency. Compared to the traditional RISC-V architecture with only one ALU, the test results showed that the instructions per clock (IPCs) of RISC-V architectures with two, three, and four ALUs increased efficiency by 76, 128.9, and 154.3%, while the area increased by 12, 22.3, and 32.6% and the static power consumption increased by 5.1, 9.2, and 13.3%. The results showed a significant improvement in performance with only a slight increase in the area. Due to the limited area of chips, a two-thread microcontroller architecture was used for the IC design and tape-out. TSMC’s 180nm process with a chip area of 1190 × 1190 μm at 133 MHz was used in this study.
To reduce carbon dioxide emissions, carbon-free ammonia is being investigated as an alternative fuel for internal combustion engines. When injected into a low-pressure environment, liquid ammonia undergoes rapid vaporization (flash boiling) due to its low boiling point. In this study, the effects of ambient pressure and fuel temperature on spray morphology and tip dynamics of ammonia from a multi-hole injector were investigated under an injection pressure representative of port fuel injection. Visualization was performed in a constant volume chamber. Mie scattering was used to visualize the liquid phase distribution, while schlieren imaging was used to visualize the gas-liquid distribution. The results showed that, as the ambient pressure decreased and the superheat degree increased, the spray plumes from individual holes collapsed and merged into a single spray structure. The spatial distributions of liquid and gas phases remained similar, and the spray remained in the liquid phase up to the tip, regardless of ambient pressure and fuel temperature conditions. With increasing superheat degree, the spray region expanded near the nozzle and narrowed downstream. In addition, the penetration length increased. This increase was quantitatively correlated with the volume expansion caused by flash boiling.
Mechanical engineering and machinery, Engineering machinery, tools, and implements
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.
Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions.
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.
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.
The rapid advancements in Generative AI (GenAI) tools, such as ChatGPT and GitHub Copilot, are transforming software engineering by automating code generation tasks. While these tools improve developer productivity, they also present challenges for organizations and hiring professionals in evaluating software engineering candidates' true abilities and potential. Although there is existing research on these tools in both industry and academia, there is a lack of research on how these tools specifically affect the hiring process. Therefore, this study aims to explore recruiters' experiences and perceptions regarding GenAI-powered code generation tools, as well as their challenges and strategies for evaluating candidates. Findings from our survey of 32 industry professionals indicate that although most participants are familiar with such tools, the majority of organizations have not adjusted their candidate evaluation methods to account for candidates' use/knowledge of these tools. There are mixed opinions on whether candidates should be allowed to use these tools during interviews, with many participants valuing candidates who can effectively demonstrate their skills in using these tools. Additionally, most participants believe that it is important to incorporate GenAI-powered code generation tools into computer science curricula and mention the key risks and benefits of doing so.
The purpose of this techno-economic study is to investigate the potential of the implementation of standalone photovoltaic (PV) energy systems in rural areas of Quetta, Pakistan. This study focuses on alleviating the region’s energy crisis and giving the local population access to clean and sustainable energy. The technique used entails data collection, load demand analysis, technical and economic evaluation, site selection, and data interpretation. The findings and discussion provide an estimate of PV energy systems’ lifetime costs, annual maintenance expenses, and energy prices. The results show that standalone PV energy systems are a feasible and financially viable option for electricity generation in Quetta’s rural areas that operates via monitoring the economic development and environmental sustainability of the region. Three plants are proposed for generating 26.8 KW, 15 KW, and 6.8 KW of power, and it is demonstrated that green and clean energy can be provided for domestic consumers at reasonable unit costs of PKR. 21.33, PKR. 21.9, and PKR. 23.89, respectively.
This article provides not only a description of an electroencephalogram (EEG), the principle of its operation, conditions of use, methods of decoding but also studies aimed at improving this procedure and facilitating the work of highly professional employees studying EEG results. A study of published articles related to electroencephalograms was carried out in order to trace the entire path of development and use of EEGs.
One of the main reasons that cause seniors to face accessibility barriers when trying to use software applications is that the age-related user interface (UI) needs of seniors (e.g., physical and cognitive limitations) are not properly addressed in software user interfaces. The existing literature proposes model-driven engineering based UI adaptations as a prominent solution for this phenomenon. But in our exploration into the domain, we identified that the existing work lacks comprehensiveness when it comes to integrating accessibility into software modelling tools and methods when compared to a well-recognised accessibility standard such as the Web Content Accessibility Guidelines (WCAG). Thus in this paper, we outline a research roadmap that aims to use WCAG as a reference framework to design domain-specific languages that model the diverse accessibility scenarios of senior users via user context information and UI adaptation rules modelling so that they meet the accessibility standards specified in WCAG.
Wouter Groeneveld, Laurens Luyten, Joost Vennekens
et al.
Creativity is a critical skill that professional software engineers leverage to tackle difficult problems. In higher education, multiple efforts have been made to spark creative skills of engineering students. However, creativity is a vague concept that is open to interpretation. Furthermore, studies have shown that there is a gap in perception and implementation of creativity between industry and academia. To better understand the role of creativity in software engineering (SE), we interviewed 33 professionals via four focus groups and 10 SE students. Our results reveal 45 underlying topics related to creativity. When comparing the perception of students versus professionals, we discovered fundamental differences, grouped into five themes: the creative environment, application of techniques, creative collaboration, nature vs nurture, and the perceived value of creativity. As our aim is to use these findings to install and further encourage creative problem solving in higher education, we have included a list of implications for educational practice.
The growing number of students enrolling in Computer Science (CS) programmes is pushing CS educators to their limits. This poses significant challenges to computing education, particularly the teaching of introductory programming and advanced software engineering (SE) courses. First-year programming courses often face overwhelming enrollments, including interdisciplinary students who are not CS majors. The high teacher-to-student ratio makes it challenging to provide timely and high-quality feedback. Meanwhile, software engineering education comes with inherent difficulties like acquiring industry partners and the dilemma that such software projects are often under or over-specified and one-time efforts within one team or one course. To address these challenges, we designed a novel foundational SE course. This SE course envisions building a full-fledged Intelligent Tutoring System (ITS) of Programming Assignments to provide automated, real-time feedback for novice students in programming courses over multiple years. Each year, SE students contribute to specific short-running SE projects that improve the existing ITS implementation, while at the same time, we can deploy the ITS for usage by students for learning programming. This project setup builds awareness among SE students about their contribution to a "to-be-deployed" software project. In this multi-year teaching effort, we have incrementally built an ITS that is now deployed in various programming courses. This paper discusses the Intelligent Tutoring System architecture, our teaching concept in the SE course, our experience with the built ITS, and our view of future computing education.
The geometric structure of the surface of textile materials is of significant functional, operational and aesthetic importance. The basic parameters of the woven fabrics’ structure are the following: weave, warp and weft density as well as warp and weft linear density. Roughness is one of the surface quality features most often assessed by quantitative indicators called surface roughness parameters. The aim of the presented research was to analyze the parameters characterizing the geometric structure of the surface of cotton woven fabrics with different weaves. Surface topography measurements were performed using the MicroSpy® Profile profilometer by FRT the art of metrology™. Using the Mark III software cooperating with the profilometer, a number of indices characterizing the geometric structure of the fabric surface and histograms illustrating the frequency of occurrence of points of a certain height on the tested surface were determined. The research confirmed that, on the basis of the results obtained with the profilometer, it is possible to analyze comprehensively the topography of the fabric surface.
Textile bleaching, dyeing, printing, etc., Engineering machinery, tools, and implements
Armin Norouzi, Saeid Shahpouri, David Gordon
et al.
Machine learning (ML) and a nonlinear model predictive controller (NMPC) are used in this paper to minimize the emissions and fuel consumption of a compression ignition engine. In this work machine learning is applied in two methods. In the first application, ML is used to identify a model for implementation in model predictive control optimization problems. In the second application, ML is used as a replacement of the NMPC where the ML controller learns the optimal control action by imitating or mimicking the behavior of the model predictive controller. In this study, a deep recurrent neural network including long-short term memory (LSTM) layers are used to model the emissions and performance of an industrial 4.5 liter 4-cylinder Cummins diesel engine. This model is then used for model predictive controller implementation. Then, a deep learning scheme is deployed to clone the behavior of the developed controller. In the LSTM integration, a novel scheme is used by augmenting hidden and cell states of the network in an NMPC optimization problem. The developed LSTM-NMPC and the imitative NMPC are compared with the Cummins calibrated Engine Control Unit (ECU) model in an experimentally validated engine simulation platform. Results show a significant reduction in Nitrogen Oxides (\nox) emissions and a slight decrease in the injected fuel quantity while maintaining the same load. In addition, the imitative NMPC has a similar performance as the NMPC but with a two orders of magnitude reduction of the computation time.