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As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers.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.
Silicon photonic devices often exhibit fabrication-induced variations such as over-etching, underetching, and corner rounding, which can significantly alter device performance. These variations are non-uniform and are influenced by feature size and shape. Accurate digital twins are therefore needed to predict the range of possible fabricated outcomes for a given design. In this paper, we introduce Gen-Fab, a conditional generative adversarial network (cGAN) based on Pix2Pix to predict and model uncertainty in photonic fabrication outcomes. The proposed method takes a design layout (in GDS format) as input and produces diverse high-resolution predictions similar to scanning electron microscope (SEM) images of fabricated devices, capturing the range of process variations at the nanometer scale. To enable one-to-many mapping, we inject a latent noise vector at the model bottleneck. We compare Gen-Fab against three baselines: (1) a deterministic U-Net predictor, (2) an inference-time Monte Carlo Dropout U-Net, and (3) an ensemble of varied U-Nets. Evaluations on an out-of-distribution dataset of fabricated photonic test structures demonstrate that Gen-Fab outperforms all baselines in both accuracy and uncertainty modeling. An additional distribution shift analysis further confirms its strong generalization to unseen fabrication geometries. Gen-Fab achieves the highest intersection-over-union (IoU) score of 89.8%, outperforming the deterministic U-Net (85.3%), the MC-Dropout U-Net (83.4%), and varying U-Nets (85.8%). It also better aligns with the distribution of real fabrication outcomes, achieving lower Kullback-Leibler divergence and Wasserstein distance.
Marine soil–structure interfaces are commonly encountered in marine engineering, where they are inevitably subjected to temperature variations and complex stress conditions, including static, dynamic, and creep loads. However, limited studies have addressed the temperature-dependent mechanical behavior of marine soil–structure interfaces under various loading scenarios. This study introduces a self-developed multifunctional large-scale shear apparatus that enables temperature-controlled testing of marine soil interfaces with various structural materials, including concrete, polymer grids, and polymer layers. The apparatus supports static, dynamic, and creep shear testing under precisely controlled thermal conditions. A series of shear tests were conducted on marine soil–concrete, marine soil–polymer grid, and marine soil–polymer layer interfaces to verify the device’s performance. The test results demonstrate that the apparatus can accurately and reliably capture the mechanical responses of marine soil–structure interfaces under different temperatures and loading modes. Furthermore, the results highlight the significant influence of temperature on the shear behavior of these interfaces, emphasizing the necessity of developing such equipment. The findings offer essential insights for the design, evaluation, and long-term stability of marine engineering structures, supporting the development of practical ocean solutions.
Science, General. Including nature conservation, geographical distribution
A series of true triaxial unloading tests are conducted on sandstone specimens with a single structural plane to investigate their mechanical behaviors and failure characteristics under different in situ stress states. The experimental results indicate that the dip angle of structural plane (θ) and the intermediate principal stress (σ2) have an important influence on the peak strength, cracking mode, and rockburst severity. The peak strength exhibits a first increase and then decrease as a function of σ2 for a constant θ. However, when σ2 is constant, the maximum peak strength is obtained at θ of 90°, and the minimum peak strength is obtained at θ of 30° or 45°. For the case of an inclined structural plane, the crack type at the tips of structural plane transforms from a mix of wing and anti-wing cracks to wing cracks with an increase in σ2, while the crack type around the tips of structural plane is always anti-wing cracks for the vertical structural plane, accompanied by a series of tensile cracks besides. The specimens with structural plane do not undergo slabbing failure regardless of θ, and always exhibit composite tensile-shear failure whatever the σ2 value is. With an increase in σ2 and θ, the intensity of the rockburst is consistent with the tendency of the peak strength. By analyzing the relationship between the cohesion (c), internal friction angle (φ), and θ in sandstone specimens, we incorporate θ into the true triaxial unloading strength criterion, and propose a modified linear Mogi-Coulomb criterion. Moreover, the crack propagation mechanism at the tips of structural plane, and closure degree of the structural plane under true triaxial unloading conditions are also discussed and summarized. This study provides theoretical guidance for stability assessment of surrounding rocks containing geological structures in deep complex stress environments.
Engineering geology. Rock mechanics. Soil mechanics. Underground construction
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.
Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the specific types of interactions between developers and these AI-powered tools have only recently begun to receive attention. Understanding and improving these interactions has the potential to enhance productivity, trust, and efficiency in AI-driven workflows. In this paper, we propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types, such as auto-complete code suggestions, command-driven actions, and conversational assistance. Building on this taxonomy, we outline a research agenda focused on optimizing AI interactions, improving developer control, and addressing trust and usability challenges in AI-assisted development. By establishing a structured foundation for studying developer-AI interactions, this paper aims to stimulate research on creating more effective, adaptive AI tools for software development.
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.
Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often. Model-based analyses can estimate dependability, yet in practice they are tricky to build and keep current because models are typically handcrafted. We claim that a simple connectivity-only topological model - just the service-dependency graph plus replica counts - can provide fast, low-risk availability estimates under fail-stop faults. To make this claim practical without hand-built models, we introduce model discovery: an automated step that can run in CI/CD or as an observability-platform capability, synthesizing an explicit, analyzable model from artifacts teams already have (e.g., distributed traces, service-mesh telemetry, configs/manifests) - providing an accessible gateway for teams to begin resilience testing. As a proof by instance on the DeathStarBench Social Network, we extract the dependency graph from Jaeger and estimate availability across two deployment modes and five failure rates. The discovered model closely tracks live fault-injection results; with replication, median error at mid-range failure rates is near zero, while no-replication shows signed biases consistent with excluded mechanisms. These results create two opportunities: first, to triage and reduce the scope of expensive chaos experiments in advance, and second, to generate real-time signals on the system's resilience posture as its topology evolves, preserving live validation for the most critical or ambiguous scenarios.
Paloma Guenes, Rafael Tomaz, Bianca Trinkenreich
et al.
Research shows that more than half of software professionals experience the Impostor Phenomenon (IP), with a notably higher prevalence among women compared to men. IP can lead to mental health consequences, such as depression and burnout, which can significantly impact personal well-being and software professionals' productivity. This study investigates how IP manifests among software professionals across intersections of gender with race/ethnicity, marital status, number of children, age, and professional experience. Additionally, it examines the well-being of software professionals experiencing IP, providing insights into the interplay between these factors. We analyzed data collected through a theory-driven survey (n = 624) that used validated psychometric instruments to measure IP and well-being in software engineering professionals. We explored the prevalence of IP in the intersections of interest. Additionally, we applied bootstrapping to characterize well-being within our field and statistically tested whether professionals of different genders suffering from IP have lower well-being. The results show that IP occurs more frequently in women and that the prevalence is particularly high among black women as well as among single and childless women. Furthermore, regardless of gender, software engineering professionals suffering from IP have significantly lower well-being. Our findings indicate that effective IP mitigation strategies are needed to improve the well-being of software professionals. Mitigating IP would have particularly positive effects on the well-being of women, who are more frequently affected by IP.
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.
Fatima Gugouch, Meriem Meknassi, Mohamed Elghorba
et al.
In several industrial applications, plastic, composites and ceramics replace a number of metallic structures such as copper, aluminum and steel. Most recently installed piping water and gas systems in the world are made of thermoplastic due to its advantages, for example, low cost, ease of fabrication and corrosion resistance. In this work, the chosen material is chlorinated polyvinyl chloride (CPVC); the best one used to transport cold and hot water beside simplicity of installation. Notwithstanding, the pipes in service are submitted to different loads, related to environmental, thermal and mechanical effects which procure to mechanisms of degradation. The aim of this article is to assess the effect of the defect on CPVC pipes, through a study of the defect criticality in the form of semi-elliptical, then the ability to predict defected pipes residual life. Therefore unexpected and sudden failure caused by pipes accelerated damage. Therefore, we performed burst tests on both pre-damaged CPVC pipes and virgin ones. To lead our work in this paper, interested in the damage modeling and the failure analysis of CPVC pipes, we adapted the model of unified theory static damage, originally developed in fatigue. We used burst pressure tests to identify the limits of three damage progression phases and, in turn, to calculate the critical life fraction at which these flaws become harmful. Furthermore, we identify the critical depth for the studied defect. These results and techniques enable industrialist s to anticipate this structures service life under these conditions after that set up a robust system of maintenance to ensure a reliable and safe functioning of the structure.
Mechanical engineering and machinery, Structural engineering (General)
This is the third part of a short series of paper, revisiting some classical concepts of Linear Elastic Fracture Mechanics. Based on the solution for the single edge notched strip, discussed in Part-II, the present study deals with the stress field developed in a stretched finite strip, weakened by two symmetric edge notches. The notches are of parabolic shape, approximating the configuration of a rounded V-notch, varying from almost semicircular edge cavities to “mathematical” edge cracks of zero distance between their lips. The solution is obtained combining Muskhelishvili’s complex potentials technique with a procedure for “stress-neutralization” of specific areas of the loaded strip. To simplify the procedure, the notches are assumed to be “shallow” (short) so that they do not affect each other. Once the complex potentials are obtained, the stress field variations are plotted along strategic loci of the strip and along the periphery of the notches. Attention is paid to the stress field developed around the bases (tips or crowns) of the two notches, providing relatively simple formulae for the critical tensile stress. In addition, the respective stress concentration factor k is obtained for blunt notches, while in the case the edge discontinuities become “mathematical” cracks, a simple expression is given for the mode-I stress intensity factor KI at the tip of the crack. It is revealed that the assumption of “shallow” notches suffices a quite efficient solution for the overall stress field in finite strips.
Mechanical engineering and machinery, Structural engineering (General)
Shinde Bharti M., Sayyad Atteshamuddin S., Naik Nitin S.
Static response of simply supported functionally graded (FG) shallow shells using a new higher-order shear and normal deformation theory is focused in this article. The effects of transverse strains and stresses on the bending response of FG shell are considered by the present theory. The current theory considers the impacts of transverse normal and shear deformations that meet the requirements for traction-free boundary conditions. The virtual work principle is applied to the mathematical formulation of the present theory. The simply supported doubly curved shallow shell problems under the static transverse load are analyzed using Navier’s solution technique. To verify the existing theory, the current results are, whenever possible, compared with those that have already been published. Additionally, a few benchmark results are presented in this article that will be helpful to researchers in the future.
In this work, a numerical study of a thermal performance of water flow inside a dimpled pipe. The effect of three types of dimples (circular, square and rhombus) studied in the numerical simulation. A commercial program called ANSYS was used to model the flow through a circular pipe .The three-dimensional governing differential equations of mass, momentum, and energy were used together with the (K − ε ) model to evaluate the impact of dimples on a turbulent flow and the velocity field. The study was carried out in the Reynolds number (Re) range (2500–15000). The research results demonstrate that the presence of a dimple on the pipe surface greatly increases the rate of heat transmission and the friction factor compared to a normal smooth pipe. Also, the numerical study demonstrated that the Nusselt number (Nu) in case of circular dimples at diameter (4 , 6 and 8) mm was (22, 28 and 43) % greater than the smooth surface. It is discovered that the improved pipe with circular dimples have a benefit for increased heat transmission efficiency compared with the square and rhombus dimples. Additionally , circular dimples have the ability to supply the lowest friction factor (f) when compared to other types of dimple. The pipe with circular dimples with D= 4mm , at Reynolds number 2500 provided the largest thermal performance criterion (PEC) value about 1.44.
Anton Chepurnenko, Vasilina Turina, Vladimir Akopyan
Artificial neural networks (ANN) have a great promise in predicting the load-bearing capacity of building structures. The purpose of this work was to develop ANN models to determine the ultimate load of eccentrically compressed concrete-filled steel tubular (CFST) columns of circular cross-sections, which operated on the widest possible range of input parameters. Short columns were considered for which the amount of deflection does not affect the bending moment. A feedforward network was selected as the neural network type. The input parameters of the neural networks were the outer diameter of the columns, the thickness of the pipe wall, the yield strength of steel, the compressive strength of concrete and the relative eccentricity. Artificial neural networks were trained on synthetic data generated based on a theoretical model of the limit equilibrium of CFST columns. Two ANN models were created. When training the first model, the ultimate loads were determined at a given eccentricity of the axial force without taking into account additional random eccentricity. When training the second model, additional random eccentricity was taken into account. The total volume of the training dataset was 179,025 samples. Such a large training dataset size has never been used before. The training dataset covers a wide range of changes in the characteristics of the pipe metal and concrete of the core, pipe diameters and wall thicknesses, as well as eccentricities of the axial force. The trained models are characterized by high mean square error (MSE) scores. The correlation coefficients between the predicted and target values are very close to 1. The ANN models were tested on experimental data for 81 eccentrically compressed samples presented in five different works and 265 centrally compressed samples presented in twenty-six papers.
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.
David M. Ruggiero, Evan C. Bentz, Gian Michele Calvi
et al.
AbstractA growing body of research has shown that reversed cyclic shear loading of reinforced concrete (RC) causes effects that are not accounted for in monotonic behavioral models. Notable among these effects are a reduction in shear strength and significant plastic offsets. This paper presents the General Crack Component Model, a rational, mechanics‐based model that explicitly considers the constitutive behavior of cracks in RC. Cracked RC is treated as a series–parallel system of bonded and unbonded regions, where the crack interfaces have both crack closing hysteresis and a kinematic contact constraint. Validation was performed using data from monotonic and reversed cyclic experiments on panel elements, and has shown that this analytical model is able to accurately capture the salient features of reversed cyclic shear behavior.