PurposeThis paper aims to offer a novel viewpoint for improving performance and reliability by developing and optimizing suspension components in a Y25 bogie through material optimization based on wheel–rail interactions under variable load and track conditions.Design/methodology/approachThe suspension system, a critical component ensuring adaptation to road and load conditions in all vehicle types, is especially vital in heavy freight and passenger trains. In this context, the suspension set of the Y25 bogie – commonly used in Türkiye and Europe – was modelled using CATIA V5, and stress analyses have been performed by way of ANSYS using the finite element analysis (FEA) method. E300-520-M cast steel was selected for the bogie frame, while two different spring steels, 61SiCr7 and 51CrV4, were considered for the suspension springs. The modeled system was subjected to numerical analysis under loading conditions. The resulting stresses and displacements were compared with the mechanical properties of the selected materials to validate the design.FindingsThe results demonstrate that the mechanical strength and deformation characteristics of the suspension components vary according to the applied external loads. The stress and displacement responses of the system were found to be within the allowable limits of the selected materials, confirming the structural integrity and reliability of the design. The suspension set is deemed suitable for the prescribed material and environmental conditions, suggesting potential for practical application in real-world rail systems.Originality/valueThis research contributes to the design and optimization of bogie suspension systems using advanced CAD/CAE tools. It thinks that the material selection and numerical validation approach presented here can guide future designs in heavy load rail applications and potentially improve both safety and performance.
Transportation engineering, Railroad engineering and operation
Miguel A. Vaquero-Serrano, Francesco Borrelli, Jesus Felez
The objective of this paper is to present a novel intelligent train control system for virtual coupling in railroads based on a Learning Model Predictive Control (LMPC). Virtual coupling is an emerging railroad technology that reduces the distance between trains to increase the capacity of the line, whereas LMPC is an optimization-based controller that incorporates artificial intelligence methods to improve its control policies. By incorporating data from past experiences into the optimization problem, LMPC can learn unmodeled dynamics and enhance system performance while satisfying constraints. The LMPC developed in this paper is simulated and compared, in terms of energy consumption, with a general MPC, without learning capabilities. The simulations are divided into two main practical applications: a LMPC applied only to the rear trains (followers) and a LMPC applied to both the followers and the first front train of the convoy (leader). Within each application, the LMPC is independently tested for three railroad categories: metro, regional, and high-speed. The results show that the LMPC reduces energy consumption in all simulation cases while approximately maintaining speed and travel time. The effect is more pronounced in rail applications with frequent speed variations, such as metro systems, compared to high-speed rail. Future research will investigate the impact of using real-world data in place of simulated data.
Timo Kehrer, Robert Haines, Guido Juckeland
et al.
Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.
Manual labeling for large-scale image and video datasets is often time-intensive, error-prone, and costly, posing a significant barrier to efficient machine learning workflows in fault detection from railroad videos. This study introduces a semi-automated labeling method that utilizes a pre-trained You Only Look Once (YOLO) model to streamline the labeling process and enhance fault detection accuracy in railroad videos. By initiating the process with a small set of manually labeled data, our approach iteratively trains the YOLO model, using each cycle's output to improve model accuracy and progressively reduce the need for human intervention. To facilitate easy correction of model predictions, we developed a system to export YOLO's detection data as an editable text file, enabling rapid adjustments when detections require refinement. This approach decreases labeling time from an average of 2 to 4 minutes per image to 30 seconds to 2 minutes, effectively minimizing labor costs and labeling errors. Unlike costly AI based labeling solutions on paid platforms, our method provides a cost-effective alternative for researchers and practitioners handling large datasets in fault detection and other detection based machine learning applications.
Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt
The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.
Daniel Mendez, Paris Avgeriou, Marcos Kalinowski
et al.
Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.
Kevin Hermann, Sven Peldszus, Jan-Philipp Steghöfer
et al.
Software security is of utmost importance for most software systems. Developers must systematically select, plan, design, implement, and especially, maintain and evolve security features -- functionalities to mitigate attacks or protect personal data such as cryptography or access control -- to ensure the security of their software. Although security features are usually available in libraries, integrating security features requires writing and maintaining additional security-critical code. While there have been studies on the use of such libraries, surprisingly little is known about how developers engineer security features, how they select what security features to implement and which ones may require custom implementation, and the implications for maintenance. As a result, we currently rely on assumptions that are largely based on common sense or individual examples. However, to provide them with effective solutions, researchers need hard empirical data to understand what practitioners need and how they view security -- data that we currently lack. To fill this gap, we contribute an exploratory study with 26 knowledgeable industrial participants. We study how security features of software systems are selected and engineered in practice, what their code-level characteristics are, and what challenges practitioners face. Based on the empirical data gathered, we provide insights into engineering practices and validate four common assumptions.
In the field of railroad safety, effective detection of surface cracks is critical, necessitating reliable, high-speed non-destructive testing (NDT) methods. This study introduces a hybrid Eddy Current Testing (ECT) probe, specifically engineered for railroad inspection, to address the common issue of ’lift-off noise’ due to varying distances between the probe and test material. Unlike traditional ECT methods, this probe integrates transmit and differential receiver coils, aiming to enhance detection sensitivity and minimize lift-off impact. The study optimizes ECT probes employing different driver coils, emphasizing three main objectives: a) quantitatively evaluating each probe using signal- to-noise ratio (SNR) and outlining a real-time data processing algorithm based on SNR methodology; b) exploring the frequency range proximal to the electrical resonance of the receiver coil; c) examining sensitivity variations across varying lift-off distances. The experimental outcomes indicate that the newly designed probe with figure 8-shape driver coil significantly improves sensitivity in detecting surface cracks on railroads. It achieves an impressive SNR exceeding 100 for defects with minimal dimensions of 1 mm in width and depth. Simulation results closely align with experimental findings, validating the investigation of optimal operational frequency and lift-off distance for selected probe performance, determined to be 0.3 MHz and 0.5 mm, respectively. The realization of this project would lead to notable advancements in enhancing railroad safety by improving crack detection efficiency.
In the field of railroad safety, effective detection of surface cracks is critical, necessitating reliable, high-speed non-destructive testing (NDT) methods. This study introduces a hybrid Eddy Current Testing (ECT) probe, specifically engineered for railroad inspection, to address the common issue of 'lift-off noise' due to varying distances between the probe and test material. Unlike traditional ECT methods, this probe integrates transmit and differential receiver coils, aiming to enhance detection sensitivity and minimize lift-off impact. The research involves optimizing the ECT probe through various driver coils, focusing on three key aspects: a) explains methodology of real-time data processing algorithm; b) probing the frequency range near the receiver coil's electrical resonance, c) assessing sensitivity changes across different lift-off distances. The experimental outcomes indicate that the newly designed probe with figure 8-shape driver coil significantly improves sensitivity in detecting surface cracks on railroads. It achieves an impressive signal-to-noise ratio (SNR) exceeding 60 for defects with minimal dimensions of 0.8 mm in width and depth. This study represents a notable advancement in NDT techniques, with profound implications for enhancing railroad safety by improving crack detection efficiency.
Abstract In this article, we consider the numerical prediction of the noise emission from a wheelset in laboratory conditions. We focus on the fluid–structure interaction leading to sound emission in the fluid domain by analyzing three different methods to account for acoustic sources. These are a discretized baffled piston using the discrete calculation method (DCM), a closed cylindrical volume using the boundary element method (BEM) and radiating elastic disks in a cubic enclosure solved with the finite element method (FEM). We provide the validation of the baffled piston and the BEM using measurements of the noise emission of a railway wheel by considering ground reflections in the numerical models. Selected space-resolved waveforms are compared with experimental results as well as with a fluid–structure interaction finite element model. The computational advantage of a discretized disk mounted on a baffle and BEM compared to FEM is highlighted, and the baffled pistons limitations caused by a lack of edge radiation effects are investigated.
Participatory citizen platforms are innovative solutions to digitally better engage citizens in policy-making and deliberative democracy in general. Although these platforms have been used also in an engineering context, thus far, there is no existing work for connecting the platforms to requirements engineering. The present paper fills this notable gap. In addition to discussing the platforms in conjunction with requirements engineering, the paper elaborates potential advantages and disadvantages, thus paving the way for a future pilot study in a software engineering context. With these engineering tenets, the paper also contributes to the research of large socio-technical software systems in a public sector context, including their implementation and governance.
Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh
Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from many sources, including training data. Existing literature suggests that bugs in training data are highly prevalent, but little research has focused on understanding their impacts on the models used in software engineering tasks. In this paper, we address this research gap through a comprehensive empirical investigation focused on three types of data prevalent in software engineering tasks: code-based, text-based, and metric-based. Using state-of-the-art baselines, we compare the models trained on clean datasets with those trained on datasets with quality issues and without proper preprocessing. By analysing the gradients, weights, and biases from neural networks under training, we identify the symptoms of data quality and preprocessing issues. Our analysis reveals that quality issues in code data cause biased learning and gradient instability, whereas problems in text data lead to overfitting and poor generalisation of models. On the other hand, quality issues in metric data result in exploding gradients and model overfitting, and inadequate preprocessing exacerbates these effects across all three data types. Finally, we demonstrate the validity and generalizability of our findings using six new datasets. Our research provides a better understanding of the impact and symptoms of data bugs in software engineering datasets. Practitioners and researchers can leverage these findings to develop better monitoring systems and data-cleaning methods to help detect and resolve data bugs in deep learning systems.
Based on the analysis on structural and dynamical characteristics of movers in linear induction motors and permanent magnet synchronous motors, this paper explored the change rules of mover motions of swaying, bouncing, rolling, yawing and pitching in the starting, coasting, and braking states, by developing the dynamical models with the multi-body dynamics simulation software for ultra-high-speed (1 000 km/h) electromagnetic propulsion devices respectively in the above two drive types. The final results show that the normal force applied on the induction mover facilitates automatic lateral alignment of the mover and resistance to lateral impact, while inhibiting rolling and yawing of the mover. In the scenario of the permanent magnet mover, the normal force from the motor aligns with the direction of mover deviation. Consequently, the mover moves close to the guideway under the impact of lateral irregularity without restraining rolling and yawing effects. Due to the vertical irregularity of the guideway, the induction motor mover experienced notable vertical vibration and impact, while the vertical component force applied by the motor on the permanent magnet mover mitigates vibration to some extent. This paper concludes that running states significantly affect the vertical response and pitch motion of the two types of movers, and the maximum vertical displacement, acceleration, and impact force all occur during braking for both.
Kelechi G. Kalu, Taylor R. Schorlemmer, Sophie Chen
et al.
The primary theory of software engineering is that an organization's Policies and Processes influence the quality of its Products. We call this the PPP Theory. Although empirical software engineering research has grown common, it is unclear whether researchers are trying to evaluate the PPP Theory. To assess this, we analyzed half (33) of the empirical works published over the last two years in three prominent software engineering conferences. In this sample, 70% focus on policies/processes or products, not both. Only 33% provided measurements relating policy/process and products. We make four recommendations: (1) Use PPP Theory in study design; (2) Study feedback relationships; (3) Diversify the studied feedforward relationships; and (4) Disentangle policy and process. Let us remember that research results are in the context of, and with respect to, the relationship between software products, processes, and policies.
Stop words, which are considered non-predictive, are often eliminated in natural language processing tasks. However, the definition of uninformative vocabulary is vague, so most algorithms use general knowledge-based stop lists to remove stop words. There is an ongoing debate among academics about the usefulness of stop word elimination, especially in domain-specific settings. In this work, we investigate the usefulness of stop word removal in a software engineering context. To do this, we replicate and experiment with three software engineering research tools from related work. Additionally, we construct a corpus of software engineering domain-related text from 10,000 Stack Overflow questions and identify 200 domain-specific stop words using traditional information-theoretic methods. Our results show that the use of domain-specific stop words significantly improved the performance of research tools compared to the use of a general stop list and that 17 out of 19 evaluation measures showed better performance. Online appendix: https://zenodo.org/record/7865748
Dulaji Hidellaarachchi, John Grundy, Rashina Hoda
et al.
Context: Requirements Engineering (RE)-related activities are critical in developing quality software and one of the most human-dependent processes in software engineering (SE). Hence, identifying the impact of diverse human-related aspects on RE is crucial in the SE context. Objective: Our study explores the impact of one of the most influential human aspects, motivation on RE, aiming to deepen understanding and provide practical guidance. Method: By conducting semi-structured interviews with 21 RE-involved practitioners, we developed a theory using socio-technical grounded theory(STGT) that explains the contextual, causal, and intervening conditions influencing motivation in RE-related activities. Result: We identified strategies to enhance motivating situations or mitigate demotivating ones, and the consequences resulting from applying these strategies. Conclusion: Our findings offer actionable insights for software practitioners to manage the influence of motivation on RE and help researchers further investigate its role across various SE contexts in the future.
With the development of society and economy, subway vehicles have also become an important infrastructure of modern transportation in China. The heat release rate is an important parameter in the fire protection design, fire safety assessment and tunnel ventilation system design of rail transit vehicles. However, the existing research on the fire combustion characteristics of subway trains cannot reflect the influence of the main components in the vehicle on the fire heat release rate. In order to effectively provide guidance for subway train structure and fire protection design, a numerical calculation model of vehicle fire was established based on the actual structure of serialized Chinese standard metro trains and the combustion characteristic parameters of non-metallic combustible materials in the vehicle were measured by material combustion experiments. Numerical calculation method was used to calculate the fire spread process in the car when the main components of the car were made of materials with different combustion characteristics, and the effects of the four main components of the roof, side walls, seats and floor on the fire heat release rate of the car were compared and analyzed. The research shows that the influence of the main components in the subway car on the heat release rate is related to the order of fire spread and the spatial position of the components, and the influence degree is the roof, side wall, seat and floor.
Software plays a central role in scientific discovery. Improving how we develop and use software for research can have both broad and deep impacts on a spectrum of challenges and opportunities society faces today. The emergence of Research Software Engineer (RSE) as a role correlates with the growing complexity of scientific challenges and diversity of software team skills. In this paper, we describe research software science (RSS), an idea related to RSE, and particularly suited to research software teams. RSS promotes the use of scientific methodologies to explore and establish broadly applicable knowledge. Using RSS, we can pursue sustainable, repeatable, and reproducible software improvements that positively impact research software toward improved scientific discovery.