Cláudio Lúcio do Val Lopes, João Marcus Pitta, Fabiano Belém
et al.
The integration of Artificial Intelligence (AI) into clinical settings presents a software engineering challenge, demanding a shift from isolated models to robust, governable, and reliable systems. However, brittle, prototype-derived architectures often plague industrial applications and a lack of systemic oversight, creating a ``responsibility vacuum'' where safety and accountability are compromised. This paper presents an industry case study of the ``Maria'' platform, a production-grade AI system in primary healthcare that addresses this gap. Our central hypothesis is that trustworthy clinical AI is achieved through the holistic integration of four foundational engineering pillars. We present a synergistic architecture that combines Clean Architecture for maintainability with an Event-driven architecture for resilience and auditability. We introduce the Agent as the primary unit of modularity, each possessing its own autonomous MLOps lifecycle. Finally, we show how a Human-in-the-Loop governance model is technically integrated not merely as a safety check, but as a critical, event-driven data source for continuous improvement. We present the platform as a reference architecture, offering practical lessons for engineers building maintainable, scalable, and accountable AI-enabled systems in high-stakes domains.
During the operation of maglev trains approaching stations, the electric energy generated by the linear generators propels the trains but is insufficient to meet the power demands of onboard equipment. Traditional contact power supply methods have shown deficiencies in various aspects, such as high installation and maintenance costs, as well as the safety risks associated with exposed live conductors. In contrast, wireless power transfer (WPT) technology eliminates the need for physical cable connections, allowing maglev trains to operate without mechanical contact and enhancing the safety, economic efficiency, and environmental adaptability of their auxiliary power supply system. This paper focuses on the optimized design for the magnetic coupling mechanism and resonant compensation circuit, addressing specific requirements of auxiliary WPT systems in maglev train applications including high power demands and efficiency requirements. A finite element model of a magnetic coupling mechanism with a single-transmitter multiple-receiver (STMR) configuration was established. The operational characteristics of three resonant compensation topologies (S/S, LCC/LCC, LCC/S) for WPT systems were compared and analyzed. A global multi-objective optimization design strategy was introduced based on the concept ofPareto optimal solutions. Furthermore, an 8.5 kW auxiliary WPT system prototype was built for verification. The experimental results demonstrated the proposed optimization scheme in meeting the design requirements of WPT systems for maglev trains, with an energy transfer efficiency of up to 91.9%.
Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt reuse and quality, Prompt-with-Me suggests language refinements, masks sensitive information, and extracts reusable templates from a developer's prompt library. Our taxonomy study of 1108 real-world prompts demonstrates that modern LLMs can accurately classify software engineering prompts. Furthermore, our user study with 11 participants shows strong developer acceptance, with high usability (Mean SUS=73), low cognitive load (Mean NASA-TLX=21), and reported gains in prompt quality and efficiency through reduced repetitive effort. Lastly, we offer actionable insights for building the next generation of prompt management and maintenance tools for software engineering workflows.
Althaf Shajihan, Kirill Mechitov, Girish Chowdhary
et al.
Railroad bridges are a crucial component of the U.S. freight rail system, which moves over 40 percent of the nation's freight and plays a critical role in the economy. However, aging bridge infrastructure and increasing train traffic pose significant safety hazards and risk service disruptions. The U.S. rail network includes over 100,000 railroad bridges, averaging one every 1.4 miles of track, with steel bridges comprising over 50% of the network's total bridge length. Early identification and assessment of damage in these bridges remain challenging tasks. This study proposes a physics-informed neural network (PINN) based approach for damage identification in steel truss railroad bridges. The proposed approach employs an unsupervised learning approach, eliminating the need for large datasets typically required by supervised methods. The approach utilizes train wheel load data and bridge response during train crossing events as inputs for damage identification. The PINN model explicitly incorporates the governing differential equations of the linear time-varying (LTV) bridge-train system. Herein, this model employs a recurrent neural network (RNN) based architecture incorporating a custom Runge-Kutta (RK) integrator cell, designed for gradient-based learning. The proposed approach updates the bridge finite element model while also quantifying damage severity and localizing the affected structural members. A case study on the Calumet Bridge in Chicago, Illinois, with simulated damage scenarios, is used to demonstrate the model's effectiveness in identifying damage while maintaining low false-positive rates. Furthermore, the damage identification pipeline is designed to seamlessly integrate prior knowledge from inspections and drone surveys, also enabling context-aware updating and assessment of bridge's condition.
Marina Araújo, Júlia Araújo, Romeu Oliveira
et al.
[Context] Domain knowledge is recognized as a key component for the success of Requirements Engineering (RE), as it provides the conceptual support needed to understand the system context, ensure alignment with stakeholder needs, and reduce ambiguity in requirements specification. Despite its relevance, the scientific literature still lacks a systematic consolidation of how domain knowledge can be effectively used and operationalized in RE. [Goal] This paper addresses this gap by offering a comprehensive overview of existing contributions, including methods, techniques, and tools to incorporate domain knowledge into RE practices. [Method] We conducted a systematic mapping study using a hybrid search strategy that combines database searches with iterative backward and forward snowballing. [Results] In total, we found 75 papers that met our inclusion criteria. The analysis highlights the main types of requirements addressed, the most frequently considered quality attributes, and recurring challenges in the formalization, acquisition, and long-term maintenance of domain knowledge. The results provide support for researchers and practitioners in identifying established approaches and unresolved issues. The study also outlines promising directions for future research, emphasizing the development of scalable, automated, and sustainable solutions to integrate domain knowledge into RE processes. [Conclusion] The study contributes by providing a comprehensive overview that helps to build a conceptual and methodological foundation for knowledge-driven requirements engineering.
Sonja M. Hyrynsalmi, Grischa Liebel, Ronnie de Souza Santos
et al.
The discipline of software engineering (SE) combines social and technological dimensions. It is an interdisciplinary research field. However, interdisciplinary research submitted to software engineering venues may not receive the same level of recognition as more traditional or technical topics such as software testing. For this paper, we conducted an online survey of 73 SE researchers and used a mixed-method data analysis approach to investigate their challenges and recommendations when publishing interdisciplinary research in SE. We found that the challenges of publishing interdisciplinary research in SE can be divided into topic-related and reviewing-related challenges. Furthermore, while our initial focus was on publishing interdisciplinary research, the impact of current reviewing practices on marginalized groups emerged from our data, as we found that marginalized groups are more likely to receive negative feedback. In addition, we found that experienced researchers are less likely to change their research direction due to feedback they receive. To address the identified challenges, our participants emphasize the importance of highlighting the impact and value of interdisciplinary work for SE, collaborating with experienced researchers, and establishing clearer submission guidelines and new interdisciplinary SE publication venues. Our findings contribute to the understanding of the current state of the SE research community and how we could better support interdisciplinary research in our field.
Ruhaimatu Abudu, Raj Bridgelall, Bright Parker Quayson
et al.
Cybersecurity challenges are increasing in the rail industry because of constant technological evolution that includes the Internet-of-Things, blockchains, automation, and artificial intelligence. Consequently, many railroads and supply chain stakeholders have implemented strategies and practices to address these challenges. However, the pace of cybersecurity implementation in the railroad industry is slow even as cyberthreats escalate. This systematic review incorporates bibliometric analysis to analyze 70 articles focusing on cybersecurity practices in the rail freight industry, structured around four research questions relating to: (1) challenges, (2) measures, (3) emerging trends, and (4) innovations. Key findings are that implementing cybersecurity practices in the rail freight industry comes with numerous challenges and risks. The study concludes that new threats will constantly emerge with technological advancements. Therefore, there is a need for continuous human training, collaboration, and coordination with stakeholders. This study also highlights research gaps and recommends how stakeholders can most appropriately execute cybersecurity strategies and best coordinate them with the various technological functions in the rail freight industry.
Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity and expressive detail possible in a SysML-defined model. Without that model connectivity, requirement quality can suffer due to imprecision and inconsistent terminology, frustrating communication during system development. Further integration of requirements engineering activities with MBSE contributes to the Authoritative Source of Truth while facilitating deep access to system architecture model elements for V&V activities. The Model-Based Structured Requirement SysML Profile was extended to comply with the INCOSE Guide to Writing Requirements updated in 2023 while conforming to the ISO/IEC/IEEE 29148 standard requirement statement templates. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition and requirements V&V. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to explore its applicability and value in real-world project environments. Initial results indicate that INCOSE-derived Model-Based Structured Requirements may rapidly improve requirement expression quality while complementing the NASA Systems Engineering Handbook checklist and guidance, but typical requirement management activities still have challenges related to automation and support with the system architecture modeling software.
Sayan Chatterjee, Ching Louis Liu, Gareth Rowland
et al.
The increasing popularity of AI, particularly Large Language Models (LLMs), has significantly impacted various domains, including Software Engineering. This study explores the integration of AI tools in software engineering practices within a large organization. We focus on ANZ Bank, which employs over 5000 engineers covering all aspects of the software development life cycle. This paper details an experiment conducted using GitHub Copilot, a notable AI tool, within a controlled environment to evaluate its effectiveness in real-world engineering tasks. Additionally, this paper shares initial findings on the productivity improvements observed after GitHub Copilot was adopted on a large scale, with about 1000 engineers using it. ANZ Bank's six-week experiment with GitHub Copilot included two weeks of preparation and four weeks of active testing. The study evaluated participant sentiment and the tool's impact on productivity, code quality, and security. Initially, participants used GitHub Copilot for proposed use-cases, with their feedback gathered through regular surveys. In the second phase, they were divided into Control and Copilot groups, each tackling the same Python challenges, and their experiences were again surveyed. Results showed a notable boost in productivity and code quality with GitHub Copilot, though its impact on code security remained inconclusive. Participant responses were overall positive, confirming GitHub Copilot's effectiveness in large-scale software engineering environments. Early data from 1000 engineers also indicated a significant increase in productivity and job satisfaction.
Case studies are a popular and noteworthy type of research study in software engineering, offering significant potential to impact industry practices by investigating phenomena in their natural contexts. This potential to reach a broad audience beyond the academic community is often undermined by deficiencies in reporting, particularly in the context description, study classification, generalizability, and the handling of validity threats. This paper presents a reflective analysis aiming to share insights that can enhance the quality and impact of case study reporting. We emphasize the need to follow established guidelines, accurate classification, and detailed context descriptions in case studies. Additionally, particular focus is placed on articulating generalizable findings and thoroughly discussing generalizability threats. We aim to encourage researchers to adopt more rigorous and communicative strategies, ensuring that case studies are methodologically sound, resonate with, and apply to software engineering practitioners and the broader academic community. The reflections and recommendations offered in this paper aim to ensure that insights from case studies are transparent, understandable, and tailored to meet the needs of both academic researchers and industry practitioners. In doing so, we seek to enhance the real-world applicability of academic research, bridging the gap between theoretical research and practical implementation in industry.
The paper presents the results of the justification of kinematic parameters of freight trains and diesel traction locomotives about the stopping process at the intermediate and end stations of the virtual hilly section of the high-speed railroad. There are received tabular data and graphical dependences of kinematic parameters of movement of the investigated freight trains and diesel traction locomotives about the organization of stops on the virtual hilly section of the high-speed railroad and also regression equations for determination of their numerical values which are recommended for introduction in the practice of the Uzbek railroads locomotive complex.
In order to meet the demands of the European market, a new articulated car body structure for EMUs has been developed in compliance with the requirements of the European technical specifications for interoperability (TSIs) and incorporating the installation requirements of the articulated bogies. By optimizing the force flow transfer path and adopting measures such as a two-stage buffer construction, the stress concentration was reduced on the underframe local structure to improve its load-bearing capacity. The anti-roll device and anti-hunting damper were directly bolted to the underframe side beam with an improved structural cross-section, to optimize the previous load bearing on the welded transition mount into a direct pattern on the base material of the underframe side beam, thus improving the connection reliability. Calculations were made under 29 static strength conditions for the car body, and the calculated stresses under all the conditions were less than the allowable ones. Under the over loading (AW3) condition with 1 500 kN longitudinal compression load applied to the car body, the maximum stress occurred at the lower corner of the doorway, and the calculated stress was 147.4 MPa, less than the allowable stress of 215 MPa for aluminum alloy. The base material and all welds of the car body were evaluated under 8 fatigue conditions according to the standard DVS 1608, and all the calculation results revealed a material utilization less than 1. The maximum material utilization of the base material was 0.7, which occurred at the window corner of the side wall, and the maximum material utilization of the welds was 0.86, which occurred at the welds connecting the end wall threshold to the end wall column. In addition, the car body was measured under 16 static strength test conditions, and the stress values at all the measured points were less than the allowable ones, and the safety factor was greater than or equal to 1.24, leaving a large safety margin. The calculation results and test results show that the structural strength and fatigue performance of the car body in compliance with the design requirements, with a large safety margin.
Abstract: Rail transit is a qualified form of cross-border traffic. Due to the specific functional
scope, they are often marginalized or completely omitted in strategic documents determining
the shape of the transport policy of the country or its individual regions. However, their
proper regulation in the Polish and international legal system and ensuring an attractive
transport offer can significantly affect the socio-economic development of border areas. In the
current legal status, two divergent approaches of the legislator to traffic regulation on transit
lines can be observed. One of them contains mechanisms stimulating traffic on the routes in
question, while the other quite radically limits its operation. In this publication, an attempt
was made to characterize and evaluate the legal regulations concerning the functioning of
transport in the subject matter.
Keywords: Transportation law; Railway law; Railway transport; International law; Railway;
Public transport
Highway engineering. Roads and pavements, Bridge engineering
The current study is aimed to extend the service life of axle box bushing mounted on the bogie of 160 km/h power concentrated EMUs by structural optimization, based on the failure cases of such a train component. Firstly, an analysis was made on the boundary conditions of operating cases and failure reasons of the original structure. Then, according to the analysis results, structural improvements were made in many aspects like a rational pre-compression, close-contact rubber profile, streamline cambered surface of steel parts, and suitable thickness of the rubber layer. By the contrast of finite element analysis (FEA) results between the optimized structure and original one, rubber strain decreased by 9.64% and fatigue damage value decreased by 60% for the former, contributing to an obvious improvement in the service life of bushings. Finally, the effectiveness of the optimized structure to improve fatigue performance was proved in the fatigue test, showing a tripled fatigue performance. Through the preliminary evaluation, the fatigue life of bushings in the optimized structure could be extended from 2-5 years to about 8 years, which could improve the operating reliability of the EMUs and reduce unnecessary maintenance. The optimized product has been applied in on-track operation for about 4 years, turning out satisfactory in all performance indexes. The optimization design presented provides a reference for other bushings with a large radial deformation. Specially, the radial pre-compression should be equivalent to fatigue deformation to avoid tensile load on the bushings; aiming at the bushings with a large radial displacement, the close-contact rubber profile could balance rubber surface strain and effectively relieve folds arising from loading on them, thus improve their fatigue life.
There is an increasing need to assess the correct behavior of self-adaptive and self-healing systems due to their adoption in critical and highly dynamic environments. However, there is a lack of systematic evaluation methods for self-adaptive and self-healing systems. We proposed CHESS, a novel approach to address this gap by evaluating self-adaptive and self-healing systems through fault injection based on chaos engineering (CE) [ arXiv:2208.13227 ]. The artifact presented in this paper provides an extensive overview of the use of CHESS through two microservice-based case studies: a smart office case study and an existing demo application called Yelb. It comes with a managing system service, a self-monitoring service, as well as five fault injection scenarios covering infrastructure faults and functional faults. Each of these components can be easily extended or replaced to adopt the CHESS approach to a new case study, help explore its promises and limitations, and identify directions for future research. Keywords: self-healing, resilience, chaos engineering, evaluation, artifact
The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality, engineering drawings are often blurred and distorted due to improper scanning, storage, and transmission, which may jeopardize the effectiveness of existing approaches. This paper focuses on restoring and recognizing low-quality engineering drawings, where an end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. The framework uses K-means clustering to classify different engineering drawing patches into simple and complex texture patches based on their gray level co-occurrence matrix statistics. Computer vision operations and a modified Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model are then used to improve the quality of the two types of patches, respectively. A modified Faster Region-based Convolutional Neural Network (Faster R-CNN) model is used to recognize the quality-enhanced graphical symbols. Additionally, a multi-stage task-driven collaborative learning strategy is proposed to train the modified ESRGAN and Faster R-CNN models to improve the resolution of engineering drawings in the direction that facilitates graphical symbol recognition, rather than human visual perception. A synthetic data generation method is also proposed to construct quality-degraded samples for training the framework. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%, demonstrating its superiority over previous approaches. Moreover, the framework is integrated into a widely-used power system software application to showcase its practicality.
Abstract: The work analyzed the safety of road bridges during their exploitation in the situation of maximum load. Such bridge loads occur during acceptance tests of the object when the system of cars on the roadway is programmed. Considered in the work is the situation of full load on bridges occurring during a road congestion. Then the layout of the vehicles is inherently random and several of them can form a system of maximum, local load. The results of measurements during the actual congestion created on the bridge with a suspended diagram were related to the results of numerical analyzes. A two-beam span model and a dual carriageway load system in the congestion were adopted. As an extreme case, the load from two vehicles was considered at work, but in a small bridge. A replacement load algorithm was used for comparative analysis. Keywords: Safety of road bridge, Congestion, Numerical analysis
Highway engineering. Roads and pavements, Bridge engineering
It is essential to discuss the role, difficulties, and opportunities concerning people of different gender in the field of software engineering research, education, and industry. Although some literature reviews address software engineering and gender, it is still unclear how research and practices in Asia exist for handling gender aspects in software development and engineering. We conducted a systematic literature review to grasp the comprehensive view of gender research and practices in Asia. We analyzed the 32 identified papers concerning countries and publication years among 463 publications. Researchers and practitioners from various organizations actively work on gender research and practices in some countries, including China, India, and Turkey. We identified topics and classified them into seven categories varying from personal mental health and team building to organization. Future research directions include investigating the synergy between (regional) gender aspects and cultural concerns and considering possible contributions and dependency among different topics to have a solid foundation for accelerating further research and getting actionable practices.
Development of machine learning (ML) applications is hard. Producing successful applications requires, among others, being deeply familiar with a variety of complex and quickly evolving application programming interfaces (APIs). It is therefore critical to understand what prevents developers from learning these APIs, using them properly at development time, and understanding what went wrong when it comes to debugging. We look at the (lack of) guidance that currently used development environments and ML APIs provide to developers of ML applications, contrast these with software engineering best practices, and identify gaps in the current state of the art. We show that current ML tools fall short of fulfilling some basic software engineering gold standards and point out ways in which software engineering concepts, tools and techniques need to be extended and adapted to match the special needs of ML application development. Our findings point out ample opportunities for research on ML-specific software engineering.