Empirical research in reverse engineering and software protection is crucial for evaluating the efficacy of methods designed to protect software against unauthorized access and tampering. However, conducting such studies with professional reverse engineers presents significant challenges, including access to professionals and affordability. This paper explores the use of students as participants in empirical reverse engineering experiments, examining their suitability and the necessary training; the design of appropriate challenges; strategies for ensuring the rigor and validity of the research and its results; ways to maintain students' privacy, motivation, and voluntary participation; and data collection methods. We present a systematic literature review of existing reverse engineering experiments and user studies, a discussion of related work from the broader domain of software engineering that applies to reverse engineering experiments, an extensive discussion of our own experience running experiments ourselves in the context of a master-level software hacking and protection course, and recommendations based on this experience. Our findings aim to guide future empirical studies in RE, balancing practical constraints with the need for meaningful, reproducible results.
Esteban Parra, Sonia Haiduc, Preetha Chatterjee
et al.
Peer review is the main mechanism by which the software engineering community assesses the quality of scientific results. However, the rapid growth of paper submissions in software engineering venues has outpaced the availability of qualified reviewers, creating a growing imbalance that risks constraining and negatively impacting the long-term growth of the Software Engineering (SE) research community. Our vision of the Future of the SE research landscape involves a more scalable, inclusive, and resilient peer review process that incorporates additional mechanisms for: 1) attracting and training newcomers to serve as high-quality reviewers, 2) incentivizing more community members to serve as peer reviewers, and 3) cautiously integrating AI tools to support a high-quality review process.
The discussion around AI-Engineering, that is, Software Engineering (SE) for AI-enabled Systems, cannot ignore a crucial class of software systems that are increasingly becoming AI-enhanced: Those used to enable or support the SE process, such as Computer-Aided SE (CASE) tools and Integrated Development Environments (IDEs). In this paper, we study the energy efficiency of these systems. As AI becomes seamlessly available in these tools and, in many cases, is active by default, we are entering a new era with significant implications for energy consumption patterns throughout the Software Development Lifecycle (SDLC). We focus on advanced Machine Learning (ML) capabilities provided by Large Language Models (LLMs). Our proposed approach combines Retrieval-Augmented Generation (RAG) with Prompt Engineering Techniques (PETs) to enhance both the quality and energy efficiency of LLM-based code generation. We present a comprehensive framework that measures real-time energy consumption and inference time across diverse model architectures ranging from 125M to 7B parameters, including GPT-2, CodeLlama, Qwen 2.5, and DeepSeek Coder. These LLMs, chosen for practical reasons, are sufficient to validate the core ideas and provide a proof of concept for more in-depth future analysis.
The heterogeneity in the organization of software engineering (SE) research historically exists, i.e., funded research model and hands-on model, which makes software engineering become a thriving interdisciplinary field in the last 50 years. However, the funded research model is becoming dominant in SE research recently, indicating such heterogeneity has been seriously and systematically threatened. In this essay, we first explain why the heterogeneity is needed in the organization of SE research, then present the current trend of SE research nowadays, as well as the consequences and potential futures. The choice is at our hands, and we urge our community to seriously consider maintaining the heterogeneity in the organization of software engineering research.
The software engineering researchers from countries with smaller economies, particularly non-English speaking ones, represent valuable minorities within the software engineering community. As researchers from Poland, we represent such a country. We analyzed the ICSE FOSE (Future of Software Engineering) community survey through reflexive thematic analysis to show our viewpoint on key software community issues. We believe that the main problem is the growing research-industry gap, which particularly impacts smaller communities and small local companies. Based on this analysis and our experiences, we present a set of recommendations for improvements that would enhance software engineering research and industrial collaborations in smaller economies.
Abstract Statistical distribution of residual fatigue life (RFL) of railway axles under given loading was computed using the Monte Carlo method by considering random variation of the selected input parameters. Experimental data for the EA4T railway axle steel, the loading spectrum, the press fit loading and the residual stress induced by surface hardening were considered in the crack propagation simulations. Usually, the material properties measured by tensile tests are considered to be the most informative source of material data. Under fatigue loading, however, the crack growth rates near the threshold are the most critical data. Two important influencing factors on these crack growth rates are presented: first, the air humidity and, second, the near-surface residual stress. The typical variation of these parameters in operation may change the RFL by one or two orders of magnitude. Experimentally obtained crack growth thresholds and residual stress profiles are highly affected by the used methodology. Therefore, the obtained input data may be located anywhere within a large scatter, while the experimenters are completely unaware of it. This can lead to dangerously non-conservative situations, e.g. when the thresholds are measured in a laboratory under humid air conditions and then applied to predictions of RFLs of axles operated in winter in low air humidity. This is significant for the topic of inspection interval optimisation. The results of experiments done on real 1:1 railway axles were close to the most frequent value found in the histogram of the numerically computed RFLs.
Agile software development relies on self-organized teams, underlining the importance of individual responsibility. How developers take responsibility and build ownership are influenced by external factors such as architecture and development methods. This paper examines the existing literature on ownership in software engineering and in psychology, and argues that a more comprehensive view of ownership in software engineering has a great potential in improving software team's work. Initial positions on the issue are offered for discussion and to lay foundations for further research.
Large Language Models (LLMs) are increasingly integrated into software applications, giving rise to a broad class of prompt-enabled systems, in which prompts serve as the primary 'programming' interface for guiding system behavior. Building on this trend, a new software paradigm, promptware, has emerged, which treats natural language prompts as first-class software artifacts for interacting with LLMs. Unlike traditional software, which relies on formal programming languages and deterministic runtime environments, promptware is based on ambiguous, unstructured, and context-dependent natural language and operates on LLMs as runtime environments, which are probabilistic and non-deterministic. These fundamental differences introduce unique challenges in prompt development. In practice, prompt development remains largely ad hoc and relies heavily on time-consuming trial-and-error, a challenge we term the promptware crisis. To address this, we propose promptware engineering, a new methodology that adapts established Software Engineering (SE) principles to prompt development. Drawing on decades of success in traditional SE, we envision a systematic framework encompassing prompt requirements engineering, design, implementation, testing, debugging, evolution, deployment, and monitoring. Our framework re-contextualizes emerging prompt-related challenges within the SE lifecycle, providing principled guidance beyond ad-hoc practices. Without the SE discipline, prompt development is likely to remain mired in trial-and-error. This paper outlines a comprehensive roadmap for promptware engineering, identifying key research directions and offering actionable insights to advance the development of prompt-enabled systems.
The rapid emergence of generative AI models like Large Language Models (LLMs) has demonstrated its utility across various activities, including within Requirements Engineering (RE). Ensuring the quality and accuracy of LLM-generated output is critical, with prompt engineering serving as a key technique to guide model responses. However, existing literature provides limited guidance on how prompt engineering can be leveraged, specifically for RE activities. The objective of this study is to explore the applicability of existing prompt engineering guidelines for the effective usage of LLMs within RE. To achieve this goal, we began by conducting a systematic review of primary literature to compile a non-exhaustive list of prompt engineering guidelines. Then, we conducted interviews with RE experts to present the extracted guidelines and gain insights on the advantages and limitations of their application within RE. Our literature review indicates a shortage of prompt engineering guidelines for domain-specific activities, specifically for RE. Our proposed mapping contributes to addressing this shortage. We conclude our study by identifying an important future line of research within this field.
Tim Wittenborg, Ildar Baimuratov, Ludvig Knöös Franzén
et al.
The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-based approaches to managing aerospace engineering knowledge. By advancing these principles, research, and industry can achieve more efficient design processes, enhanced collaboration, and a stronger commitment to sustainable aviation.
Large language model-specific inference engines (in short as \emph{LLM inference engines}) have become a fundamental component of modern AI infrastructure, enabling the deployment of LLM-powered applications (LLM apps) across cloud and local devices. Despite their critical role, LLM inference engines are prone to bugs due to the immense resource demands of LLMs and the complexities of cross-platform compatibility. However, a systematic understanding of these bugs remains lacking. To bridge this gap, we present the first empirical study on bugs in LLM inference engines. We mine official repositories of 5 widely adopted LLM inference engines, constructing a comprehensive dataset of 929 real-world bugs. Through a rigorous open coding process, we analyze these bugs to uncover their symptoms, root causes, commonality, fix effort, fix strategies, and temporal evolution. Our findings reveal six bug symptom types and a taxonomy of 28 root causes, shedding light on the key challenges in bug detection and location within LLM inference engines. Based on these insights, we propose a series of actionable implications for researchers, inference engine vendors, and LLM app developers, along with general guidelines for developing LLM inference engines.
The Moroccan railroad connecting the cities of Fez and Taza, east of the country, is located on a corridor with a relatively affordable topography, but its soil is primarily marl formations. Since the embankments supporting the track are made of reused local materials, several signs of deterioration were observed on some of these structures after years of operation. Observations made during on-site visits range from minor deformations of track-related operating elements to disturbances in rail leveling, forcing a substantial drop in train speeds and urgent interventions to correct said levels. Three specific points on this railway were this study’s focus, which are the kilometric points KP8+600, KP38+700, and KP52+900. The numbers indicate their distance from the city of Fez. Our study, whose objective is to find solutions that are both sustainable and non-restrictive to operations, was based on an instability catalyst/trigger approach, in which predisposing factors and a triggering event were precisely identified for each of the cases studied based on our observations. This approach put forth solutions that were adequate for each of the aforementioned locations. Stability was evaluated through safety factor calculus before and after incorporation of the solution, confirming both the improvement and the satisfactory level of security newly obtained. A deformation assessment was also conducted before and after implementing said solutions. Obtained values were within the admissible range in all three cases. The improved structures’ satisfactory safety and the deformations obtained for the proposed solutions validated the catalyst/trigger approach as a better method for solving problems related to earthworks instability compared to the use of conventional resolving methods, which can prove both inefficient and unsustainable.
With the rapid development of electrified railroad, the pantograph-catenary system’ s(PCS) current-carrying performance has become a crucial factor limiting the safety and dependability of high-speed train operation. From the perspective of control theory, an adaptive backstepping controller based on Wavelet Neural Network(VVNN) is proposed to stabilize the contact force and improve the current-carrying performance of the system. Specifically, the virtual control laws are adopted, Lyapunov functions are selected by stepwise inversion recursion, the unknown parts of the model are approximated by WNN separately, and the errors generated by approximation of WNNs are compensated by introducing robust term. The parameter adaptive law for the WNN and the update rule for the robust term are obtained by derivation of Lyapunov function. The corresponding stability analysis is also carried out. The simulation results show that the backstepping control strategy based on WNN can compensate the influence of uncertainty well, reduce the fluctuation of the contact load of the PCS effectively and improve the current-carrying performance.
Suspended monorail trains represent an important component of urban rail transit and this mode of transportation is regarded as promising for applications in China. This paper begins with an introductory interview of the development of suspended monorail trains and subsequently analyzes the technical schemes of the Optics Valley Photon suspended monorail trains from various perspectives, including the general design, bogie system, car body system, electrical system, braking system, and network system. It further elaborates on key performance aspects such as safety, intelligence, low-carbon emissions, and environmental protection. The Optics Valley Photon suspended monorail trains have been successfully verified through simulation testing and experiments at both the component and vehicle levels. Furthermore, actual train operation on the track has demonstrated overall performance in alignment with industry application requirements.
As simple and economical heat dissipation devices, copper-water heat pipes are widely used in all walks of life. Studying their reliability is greatly significant to the thermal management of equipment, especially the cooling of power electronic devices with high thermal sensitivity. This study focused on heat pipe radiators used in several rail transit lines that have been operated for at least 10 years in different regions. A series of experiments were conducted for evaluation in multiple aspects including isothermal performance, temperature resistance, power transfer, and vacuum degree, along with laboratory tests of the working fluid. The analysis of results show that, thanks to the stable working fluid, the copper-water heat pipes generally have a service life of more than 10 years. Furthermore, a low probability of failure was identified, primarily associated with wall damage and air ingress during the pipe sealing process. Therefore, recommendations are offered for heat pipe manufacturer to improve the performance and reliability of heat pipes by refining the pipe sealing process.
Rudrajit Choudhuri, Ambareesh Ramakrishnan, Amreeta Chatterjee
et al.
Generative AI (genAI) tools (e.g., ChatGPT, Copilot) have become ubiquitous in software engineering (SE). As SE educators, it behooves us to understand the consequences of genAI usage among SE students and to create a holistic view of where these tools can be successfully used. Through 16 reflective interviews with SE students, we explored their academic experiences of using genAI tools to complement SE learning and implementations. We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students. We validated our findings through member checking and triangulation with instructors. Our findings provide practical considerations of where and why genAI should (not) be used in the context of supporting SE students.
The relevance of the research topic. Wherever slavery existed, people attempted to escape, and American history is no exception. Sometimes such efforts took on organized and institutionalized forms, a notable example of which is the so-called Underground Railroad, a secret and organized system of resistance to enslavement by facilitating the escape of African Americans to northern states and other territories. In the chosen context of the research, the Underground Railroad can rightfully be considered one of the first mass movements for human rights not only in the United States, but also in the world.The purpose of the research is to reveal the main aspects of the functioning of the Underground Railroad, since this problem is extremely poorly covered by domestic science.The research is based on a scientific analysis of biographical data, literary sources, legal documents, materials from periodicals and has been carried out by applying the principle of historicism, comparative historical, problem-chronological, biographical and descriptive methods.The research results demonstrate that, in order to prevent human trafficking, individuals, families, and communities with anti-slavery attitude created preconditions for the formation of a large-scale institutionalized system that stretched from the Canadian provinces of Quebec and Ontario east to the Atlantic coast, south to Florida and the Caribbean, and west to the border enclaves of Kansas, Texas, and Mexico.On the basis of the research results, it has been concluded that the term «Underground Railroad», although it does not reflect the specifics of its activities, denotes a very real historical phenomenon. The organization and activities of the Underground Railroad became an important component in the difficult task of eradicating slavery in the United States.
For decades, much software engineering research has been dedicated to devising automated solutions aimed at enhancing developer productivity and elevating software quality. The past two decades have witnessed an unparalleled surge in the development of intelligent solutions tailored for software engineering tasks. This momentum established the Artificial Intelligence for Software Engineering (AI4SE) area, which has swiftly become one of the most active and popular areas within the software engineering field. This Future of Software Engineering (FoSE) paper navigates through several focal points. It commences with a succinct introduction and history of AI4SE. Thereafter, it underscores the core challenges inherent to AI4SE, particularly highlighting the need to realize trustworthy and synergistic AI4SE. Progressing, the paper paints a vision for the potential leaps achievable if AI4SE's key challenges are surmounted, suggesting a transition towards Software Engineering 2.0. Two strategic roadmaps are then laid out: one centered on realizing trustworthy AI4SE, and the other on fostering synergistic AI4SE. While this paper may not serve as a conclusive guide, its intent is to catalyze further progress. The ultimate aspiration is to position AI4SE as a linchpin in redefining the horizons of software engineering, propelling us toward Software Engineering 2.0.
Úkolem probíhajícího výzkumu je určení vlivu počátečních imperfekcí na ztrátu stability příčně zatěžované válcové skořepiny. Článek popisuje probíhající druhou fázi experimentálního ověření, která aktuálně sestává z ověření nově vyrobených experimentálních vzorků. Z tohoto důvodu se tento článek zaměřuje pouze na variantu modelu bez úmyslně vytvořené počáteční imperfekce. Porovnávány jsou výsledky geometricky a materiálově nelineární numerické analýzy (GMNA) s výstupy zatěžování dvou experimentálních modelů v univerzálním zkušebním stroji. Měřítkem shody je pak hodnota mezního zatížení, při kterém dochází ke ztrátě stability posuzované příčně zatěžované válcové skořepiny. Dále je posuzován i průběh a shoda jednotlivých zatěžovacích křivek. Pokud bude potvrzena dostatečně blízká shoda výsledků numerických analýz s výstupy nových experimentálních vzorků, pak bude umožněn následující krok druhé fáze experimentálního ověření, který bude spočívat v realizaci experimentů vzorků s úmyslně vytvořenou geometrickou počáteční imperfekcí.
Railroad engineering and operation, Industrial engineering. Management engineering