Potential tropospheric noise is a critical factor that undermines the effectiveness of deformation monitoring in Synthetic Aperture Radar Interferometry (InSAR) technologies. In most scenarios, many point targets within the InSAR deformation monitoring area either do not undergo deformation or exhibit only minimal deformation trends. The phases of densely distributed stable points can effectively respond to spatial tropospheric delays, particularly turbulent atmospheric phases. This study proposes a data-driven InSAR atmospheric correction method by exploring how to use these densely stable InSAR time series to model atmospheric phase delays. Our focus is on selecting stable InSAR time series point targets and evaluating the impact of different densities of stable points on atmospheric correction performance. Analysis of 645 interferograms derived from 217 Sentinel-1A SAR images, spanning from 13 June 2017 to 15 November 2024, demonstrates that the proposed method reduces the Root Mean Square Error (RMSE) by 70%, 59%, and 69% compared to the terrain-related linear approach, the General Atmospheric Correction Online Service, and common scene stacking methods, respectively. In addition, simulation data and leveling data were used to validate the proposed method. This article does not develop an independent InSAR atmospheric correction method. Instead, the proposed approach starts with the InSAR deformation time series, allowing for easy integration into existing InSAR workflows and widely used atmospheric correction strategies. It can serve as a post-processing tool to improve InSAR time series analysis.
The inclusion of internship courses in Software Engineering (SE) programs is essential for closing knowledge gaps and improving graduates' readiness for the software industry. Our study focuses on year-long internships at RMIT University (Melbourne, Australia), which offers in-depth industry engagement. We analysed how the course evolved over the last 10 years to incorporate students' needs and summarised the lessons learned that can be helpful for other educators supporting internship courses. Our qualitative analysis of internship data based on 91 reports during 2023-2024 identified three challenge themes the students faced, and which courses were found by students to be particularly beneficial during their internships. On this basis, we proposed recommendations for educators and companies to help interns overcome challenges and maximise their learning experience.
Massimiliano Di Penta, Kelly Blincoe, Marsha Chechik
et al.
As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers. These barriers threaten the inclusivity and global diversity that have contributed to the success of the SE community. Based on survey data, we identify concrete actions the ACM Special Interest Group on Software Engineering (SIGSOFT) can take to address these challenges, including improving transparency around conference funding, experimenting with hybrid poster presentations, and expanding outreach to underrepresented regions. By implementing these changes, SIGSOFT can help ensure the software engineering community remains accessible and welcoming.
Oleksandr Kosenkov, Ehsan Zabardast, Jannik Fischbach
et al.
Implementing privacy by design (PbD) according to the General Data Protection Regulation (GDPR) is met with a growing number of requirements engineering (RE) approaches. However, the question of which RE method for PbD fits best the goals of organisations remains a challenge. We report our endeavor to close this gap by synthesizing a goal-centric approach for PbD methods assessment. We used literature review, interviews, and validation with practitioners to achieve the goal of our study. As practitioners do not approach PbD systematically, we suggest that RE methods for PbD should be assessed against organisational goals, rather than process characteristics only. We hope that, when further developed, the goal-centric approach could support the development, selection, and tailoring of RE practices for PbD.
Ana B. M. Bett, Thais S. Nepomuceno, Edson OliveiraJr
et al.
Context: The empirical software engineering (ESE) community has contributed to improving experimentation over the years. However, there is still a lack of rigor in describing controlled experiments, hindering reproducibility and transparency. Registered Reports (RR) have been discussed in the ESE community to address these issues. A RR registers a study's hypotheses, methods, and/or analyses before execution, involving peer review and potential acceptance before data collection. This helps mitigate problematic practices such as p-hacking, publication bias, and inappropriate post hoc analysis. Objective: This paper presents initial results toward establishing an RR template for Software Engineering controlled experiments using the Open Science Framework (OSF). Method: We analyzed templates of selected OSF RR types in light of documentation guidelines for controlled experiments. Results: The observed lack of rigor motivated our investigation of OSF-based RR types. Our analysis showed that, although one of the RR types aligned with many of the documentation suggestions contained in the guidelines, none of them covered the guidelines comprehensively. The study also highlights limitations in OSF RR template customization. Conclusion: Despite progress in ESE, planning and documenting experiments still lack rigor, compromising reproducibility. Adopting OSF-based RRs is proposed. However, no currently available RR type fully satisfies the guidelines. Establishing RR-specific guidelines for SE is deemed essential.
Mohey Eldeen H. H. Ali, Ahmed F. Tayel, Hossam M. Ezzat
et al.
Energy plays a crucial role in national development, influencing critical sectors such as industry, agriculture, healthcare, and education. Accurate energy consumption prediction is essential for efficient energy management, helping prevent imbalances between supply and demand and potential energy shortages. This study aims to forecast the total primary energy supply (TPES), using Egypt as a case study for the first time in literature and utilizing several models (ordinary differential equations (ODEs), regression, and ANN models). Although ordinary differential equations (ODEs) offer flexibility and convenience, their application in energy forecasting remains limited. One of the main objectives of this research is to evaluate the effectiveness of ODEs in predicting energy consumption. Various ODE and regression models are employed to identify the most suitable model amongst each category for forecasting energy demand. Additionally, an artificial neural network (ANN) is developed, trained, validated, and tested for the same forecasting task. The study compares the performance of the selected ODE model (Mendelsohn), with the selected regression model (Polynomial), and an ANN model predicting Egypt’s TPES until 2035. By assessing multiple forecasting methods, this work improves the accuracy and reliability of energy consumption predictions, which is crucial for sustainable energy planning and policy development.
Jake Zappin, Trevor Stalnaker, Oscar Chaparro
et al.
This position paper examines the substantial divide between academia and industry within quantum software engineering. For example, while academic research related to debugging and testing predominantly focuses on a limited subset of primarily quantum-specific issues, industry practitioners face a broader range of practical concerns, including software integration, compatibility, and real-world implementation hurdles. This disconnect mainly arises due to academia's limited access to industry practices and the often confidential, competitive nature of quantum development in commercial settings. As a result, academic advancements often fail to translate into actionable tools and methodologies that meet industry needs. By analyzing discussions within quantum developer forums, we identify key gaps in focus and resource availability that hinder progress on both sides. We propose collaborative efforts aimed at developing practical tools, methodologies, and best practices to bridge this divide, enabling academia to address the application-driven needs of industry and fostering a more aligned, sustainable ecosystem for quantum software development.
Roham Koohestani, Philippe de Bekker, Begüm Koç
et al.
Benchmarks are essential for unified evaluation and reproducibility. The rapid rise of Artificial Intelligence for Software Engineering (AI4SE) has produced numerous benchmarks for tasks such as code generation and bug repair. However, this proliferation has led to major challenges: (1) fragmented knowledge across tasks, (2) difficulty in selecting contextually relevant benchmarks, (3) lack of standardization in benchmark creation, and (4) flaws that limit utility. Addressing these requires a dual approach: systematically mapping existing benchmarks for informed selection and defining unified guidelines for robust, adaptable benchmark development. We conduct a review of 247 studies, identifying 273 AI4SE benchmarks since 2014. We categorize them, analyze limitations, and expose gaps in current practices. Building on these insights, we introduce BenchScout, an extensible semantic search tool for locating suitable benchmarks. BenchScout employs automated clustering with contextual embeddings of benchmark-related studies, followed by dimensionality reduction. In a user study with 22 participants, BenchScout achieved usability, effectiveness, and intuitiveness scores of 4.5, 4.0, and 4.1 out of 5. To improve benchmarking standards, we propose BenchFrame, a unified framework for enhancing benchmark quality. Applying BenchFrame to HumanEval yielded HumanEvalNext, featuring corrected errors, improved language conversion, higher test coverage, and greater difficulty. Evaluating 10 state-of-the-art code models on HumanEval, HumanEvalPlus, and HumanEvalNext revealed average pass-at-1 drops of 31.22% and 19.94%, respectively, underscoring the need for continuous benchmark refinement. We further examine BenchFrame's scalability through an agentic pipeline and confirm its generalizability on the MBPP dataset. All review data, user study materials, and enhanced benchmarks are publicly released.
Software development relies heavily on text-based communication, making sentiment analysis a valuable tool for understanding team dynamics and supporting trustworthy AI-driven analytics in requirements engineering. However, existing sentiment analysis tools often perform inconsistently across datasets from different platforms, due to variations in communication style and content. In this study, we analyze linguistic and statistical features of 10 developer communication datasets from five platforms and evaluate the performance of 14 sentiment analysis tools. Based on these results, we propose a mapping approach and questionnaire that recommends suitable sentiment analysis tools for new datasets, using their characteristic features as input. Our results show that dataset characteristics can be leveraged to improve tool selection, as platforms differ substantially in both linguistic and statistical properties. While transformer-based models such as SetFit and RoBERTa consistently achieve strong results, tool effectiveness remains context-dependent. Our approach supports researchers and practitioners in selecting trustworthy tools for sentiment analysis in software engineering, while highlighting the need for ongoing evaluation as communication contexts evolve.
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.
NIU Zihao 1, 2, ZHU Zhende 3, QUE Xiangcheng 3, XIE Xinghua 4, JIN Kai 1, 2
With the construction and commissioning of major hydropower projects represented by Baihetan of Jinsha River, it is of great significance to clarify the mechanical and seepage characteristics of engineering rock mass under complex stress environment with high confining pressure and high water pressure. Based on the field survey data and the structural characteristics of the columnar jointed basalt of dam foundation, two kinds of columnar joint similar material model samples with different dip angles β, quadrangular prisms and hexagonal prisms, are prepared, and the true triaxial stress-seepage coupling tests are carried out. The test results show that the columnar jointed rock mass with different cross-section characteristics has strong permeability anisotropy, and the permeability coefficient k is positively correlated with β at different loading stages. During the true triaxial loading process, the volume strain εV of the sample can be used as an effective characterization parameter of k. At the volume compression stage, k shows a low level, and at the volume expansion stage k shows a rapid growth trend. The final failure mode of the samples exhibits three typical forms, and the most dangerous failure mode is the structural failure dominated by the shear slip failure of the joint surface, which mainly occurs in the samples with β=45°, 60°. Correspondingly, the lateral support of this kind of rock mass should be strengthened in the construction design of surrounding rock of tunnels and rock mass of dam foundation.
Engineering geology. Rock mechanics. Soil mechanics. Underground construction
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.
Joern Ploennigs, Markus Berger, Martin Mevissen
et al.
Quantum computing is a new computational paradigm with the potential to solve certain computationally challenging problems much faster than traditional approaches. Civil engineering encompasses many computationally challenging problems, which leads to the question of how well quantum computing is suitable for solving civil engineering problems and how much impact and implications to the field of civil engineering can be expected when deploying quantum computing for solving these problems. To address these questions, we will, in this paper, first introduce the fundamentals of quantum computing. Thereupon, we will analyze the problem classes to elucidate where quantum computing holds the potential to outperform traditional computers and, focusing on the limitations, where quantum computing is not considered the most suitable solution. Finally, we will review common complex computation use cases in civil engineering and evaluate the potential and the limitations of being improved by quantum computing.
The article proposes the development of an automated lighting control system for special purpose objects, for example, a theater stage or a concert hall. Stage lighting plays an important role in the respective art and lamps are one of the types of lighting devices. The developed system is focused on modern LED lamps, which have the following advantages compared to traditional lamps: much lower power consumption, much longer average «life», much less heat dissipation. The system was built with the use of a microcontroller, which made it possible to improve its characteristics based on the well-known and mastered microcontroller architecture by supplementing it with specialized blocks implemented taking into account the features of the control object. A structural and circuit electrical scheme of the lighting control system has been developed. The basis of the scheme is the Atmel ATmega162 microcontroller of the AVR family, which has the widest set of functions. An algorithm for the lighting control system was also developed and a program for a microcontroller using the C programming language was written.
Marco Nicola Mastrone, Lucas Hildebrand, Constantin Paschold
et al.
The circular layout and the kinematics of planetary gearboxes result in characteristic oil flow phenomena. The goal of this paper is to apply a new remeshing strategy, based on the finite volume method, on the numerical analysis of a planetary gearbox and its evaluation of results as well as its validation. The numerical results are compared with experimental data acquired on the underlying test rig with high-speed camera recordings. By use of a transparent housing cover, the optical access in the front region of the gearbox is enabled. Different speeds of the planet carrier and immersion depths are considered. A proper domain partitioning and a specifically suited mesh-handling strategy provide a highly efficient numerical model. The open-source software OpenFOAM<sup>®</sup> is used.