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.
Particle accelerators represent some of the most sophisticated engineering achievements of our time. Their construction requires a unique combination of physics insight and mechanical engineering expertise. The aim of this paper is to provide young mechanical engineers with an introduction to the principles, methods, and challenges associated with the mechanical design of accelerators. The lecture upon which this proceeding is based emphasized the translation of functional requirements into engineering specifications, the critical importance of robust and reliable design, and the need for precisely defined drawings supported by international standards such as ISO GPS and GD&T. Through illustrative examples and a practical case inspired by CERN's existing components, the paper underlines the necessity of anticipating lifecycle demands, ensuring manufacturability, and safeguarding operational reliability. Particular emphasis is placed on the contractual value of 2D drawings, the practical application of functional dimensioning, and tolerance chain analysis. By reviewing common pitfalls and exploring best practices, the paper seeks to orient engineers towards design choices that balance cost, reliability, and performance.
This paper explores the integration of a hands-on assignment in Mechanical Engineering studies, such as in the Mechanics of Machines course, with a focus on commonly used mechanisms like the four-bar and slider-crank mechanisms. Recognizing the challenges students face in connecting theoretical models to real-world applications, we implemented an innovative approach that utilizes affordable and easily accessible machinery toys. This approach consists of multiple carefully designed in-class and after-class tasks. Students actively assemble, identify, and quantitatively analyze the mechanisms in their assigned toys using the theoretical knowledge from the course lectures. The successful identification and analysis of kinematic pairs and mechanisms reflect a positive impact on students’ learning outcomes. Feedback from students indicated enhanced interest, comprehension, and confidence in their understanding of mechanical systems. The results demonstrate the effectiveness of this hands-on assignment in bridging the gap between theory and practice in mechanical engineering education, while also providing insights for future improvements in assignment design and timing. The proposed hands-on assignment can be easily replicated in any classroom setting, including remote learning environments.
ABSTRACT Reverse engineering in mechanical engineering refers to the process of deconstructing a product or system to understand its design, components, and functionalities, often for the purpose of replicating, modifying, or improving it. This process involves various techniques such as scanning, 3D modelling, and analysis of materials and structures to extract valuable information from a physical object. The applications of reverse engineering are vast, ranging from the production of spare parts for obsolete machinery to the development of new products based on existing designs. It also plays a critical role in innovation, enabling engineers to identify design flaws, enhance performance, and reduce manufacturing costs by reinterpreting and improving existing systems. Reverse engineering typically begins with capturing the geometry of the object, often through methods like laser scanning or computed tomography (CT) imaging, which generate highly accurate digital representations. These models can then be analyzed and modified using CAD (Computer-Aided Design) software. Keywords: Reverse Engineering, Mechanical Components, 3D Scanning, CAD, Material Analysis, Product Replication, Design Optimization, Aerospace, Automotive, Manufacturing,
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.
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.
Ashis Kumar Mandal, Md Nadim, Chanchal K. Roy
et al.
Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques that expedite software development. Finally, we discuss the opportunities and challenges in quantum-driven software engineering and QSE. Our study reveals that quantum machine learning (QML) and quantum optimization have substantial potential to address classical software engineering tasks, though this area is still limited. Current QSE tools and techniques lack robustness and maturity, indicating a need for more focus. One of the main challenges is that quantum computing has yet to reach its full potential.
Garlic, a globally cherished spice from the Allium Sativum L. family, holds immense importance in Indian agriculture as the second-largest producer worldwide. With India contributing 10.4% to the global garlic production, totaling 3.27 million metric tons in 2022, the physical properties of garlic become integral in engineering design. This research navigates through experiments on garlic clove bulk density, considering moisture levels, and explores the mechanical aspects affecting agricultural and machinery applications. Moisture content considerations for storage, odor/flavor impact, and even the friction coefficient concerning bulb size underscore the complexity of garlic's engineering dynamics. By unraveling these properties, this study aims to fuel advancements in cultivation practices, machinery design, and processing technologies, enhancing efficiency and product quality in the agricultural and food engineering sectors.
The coordination of multiple processes in roadbed construction can achieve corresponding engineering goals, and the reasonable scheduling of process combination equipment is conducive to the reduction of overall engineering costs and the improvement of overall construction efficiency. Based on genetic algorithm, this paper uses genetic algorithm with penalty function to optimize the selection of mechanical equipment for different processes of several objectives in highway construction. By establishing an optimization model with the goal of reducing construction costs and construction power, and using the construction requirements of each equipment as constraint conditions, multi-objective optimization is carried out using genetic algorithm settings to find the optimal mechanical equipment process scheduling plan. Case analysis shows that using genetic algorithm for mechanical equipment optimization can output the optimal results of minimum cost and minimum energy consumption while considering the completion of the project. Compared with genetic algorithm, the improved niche genetic algorithm with penalty function can achieve optimal scheduling and reduce the overall number of iterations.
With the rapid expansion of the market for hybrid vehicles, the development of dedicated hybrid powertrain engines has become an important research direction. This study focuses on the techniques to improve the fuel economy of dedicated hybrid powertrain operating conditions of 2000 rpm, 110 Nm. Turbocharging can enhance the performance and fuel economy of gasoline engines while reducing emissions. However, intake boosting increases the pressure and temperature of the mixture in the cylinder at the compression end timing, leading to a higher risk of knock, which is not conducive to the safety and fuel consumption reduction of gasoline engines. Low-temperature combustion with mixture dilution can effectively reduce the occurrence of knock and NOx emissions. In this study, the effects of EGR and VVT strategies as different dilution methods on the combustion of turbocharged gasoline engines were investigated. The experimental results indicate that both EGR and VVT can achieve mixture dilution in the cylinder, reducing the combustion temperature and thus lowering NOx emissions. Furthermore, advancing the ignition timing results in CA50 trending towards the optimal fuel consumption point, shortening the combustion duration CA10–90, and reducing ISFC. Compared to VVT, EGR can better suppress knock, leading to an earlier ignition phase CA50, a shorter combustion duration, and ultimately less fuel consumption. The research findings provide technical support for the optimization of dedicated hybrid powertrain operating conditions.
Three-dimensional (3D) printing of cementitious materials is an attractive technology for structural applications due to decreased construction time, design versatility, and possible superior mechanical performance and durability. However, challenges exist in processing because components rely on green strength and stiffness to resist instability during construction. This research investigates cementitious mix design and extrusion techniques to advance engineering of 3D printed composites. In the first phase, extrudable mix designs were developed. In the second phase, the mechanical engineering design of the extruder (pump pressure, motor properties, rotating versus sliding piston, bearing design, and gantry stiffness) was investigated. The study found: (1) methylcellulose can provide shape stability, (2) sand is challenging due to phase migration under high pressures, and (3) a piston-driven extruder capable of providing pressures exceeding 345 kPa (50 psi) is needed to print these mix designs.
The 3rd International Conference on Advances in Materials, Machinery, Electrical Engineering (AMMEE 2024) held at the city of Tianjin, China, during June 29-30, 2024, and provides two days' focus on the science and technology that are the basis for mechanical engineering and industrial informatics. The theme of the plenary session is “Materials, Machinery, Electrical Engineering” featuring invited speakers who will further explore this topic that is so significant for materials, machinery, electrical engineering. Concurrent sessions and a poster session will cover a wide range of topics and issues, including both contributed papers and special sessions developed on specific themes, all with a central focus of materials, machinery, electrical engineering. This 3rd AMMEE received a total of 71 submissions. And more than 20 submissions have been accepted by our reviewers. By submitting a paper to this AMMEE conference, the authors agree to the review process and understand that papers undergo a peer-review process. Manuscripts will be reviewed by appropriately qualified experts in the field selected by the conference committee, who will give detailed comments and-if the submission gets accepted-the authors submit a revised version that takes into account this feedback. All papers are reviewed using a double-blind review process: authors declare their names and affiliations in the manuscript for the reviewers to see, but reviewers do not know each other's identities, nor do the authors receive information about who has reviewed their manuscript. We would like to express their sincere appreciation and thanks to all the members of the AMMEE 2024 Conference Technical Program Committee for their tremendous efforts. They are indebted to the referees for their constructive comments on the papers. Without their dedication, it was impossible to have a successful AMMEE 2024 and a high quality volume of the conference proceedings. Jiayi Zhong and Axel Sikora Conference Organizing Committee Tianjin, China
As a large-scale construction machinery, the crane is widely used in enterprise production, construction and other processes, and greatly improve the efficiency of production. With the wide application of hoisting machinery, its mechanical accidents occur frequently, which brings hidden danger to people s life safety. In the following, the author according to his own work experience, on the safety of hoisting machinery inspection and technology talk about his own point of view.