Often software engineering classes have the student concentrate on designing and planning the project but stop short of actual student team development of code. This leads to criticism by employers of new graduates that they are missing skills in working in teams and coordinating multiple overlapping changes to a code base. Additionally, students that are not actively experiencing team development are unprepared to understand and modify existing legacy-code bases written by others. This paper presents a new approach to teaching undergraduate software engineering that emphasizes not only software engineering methodology but also experiencing development as a member of a team and modifying a legacy code base. Our innovative software engineering course begins with learning the fundamentals of software engineering, followed by examining an existing framework of a social media application. The students are then grouped into multiple software teams, each focusing on a different aspect of the app. The separate teams must define requirements, design, and provide documentation on the services. Using an Agile development approach, the teams incrementally add to the code base and demonstrate features as the application evolves. Subsequent iterations of the class pick up the prior students code base, providing experience working with a legacy code base. Preliminary results of using this approach at the university are presented in this paper including quantitative analysis. Analysis of student software submissions to the cloud-based code repository shows student engagement and contributions over the span of the course. Positive student evaluations show the effectiveness of applying the principles of software engineering to the development of a complex solution in a team environment. Keywords: Software engineering, teaching, college computer science, innovative methods, agile.
Quantum computing, particularly in the area of quantum optimization, is steadily progressing toward practical applications, supported by an expanding range of hardware platforms and simulators. While Software Engineering (SE) optimization has a strong foundation, which is exemplified by the active Search-Based Software Engineering (SBSE) community and numerous classical optimization methods, the growing complexity of modern software systems and their engineering processes demands innovative solutions. This Systematic Literature Review (SLR) focuses specifically on studying the literature that applies quantum or quantum-inspired algorithms to solve classical SE optimization problems. We examine 77 primary studies selected from an initial pool of 2083 publications obtained through systematic searches of six digital databases using carefully crafted search strings. Our findings reveal concentrated research efforts in areas such as SE operations and software testing, while exposing significant gaps across other SE activities. Additionally, the SLR uncovers relevant works published outside traditional SE venues, underscoring the necessity of this comprehensive review. Overall, our study provides a broad overview of the research landscape, empowering the SBSE community to leverage quantum advancements in addressing next-generation SE challenges.
AI agents have recently shown significant promise in software engineering. Much public attention has been transfixed on the topic of code generation from Large Language Models (LLMs) via a prompt. However, software engineering is much more than programming, and AI agents go far beyond instructions given by a prompt. At the code level, common software tasks include code generation, testing, and program repair. Design level software tasks may include architecture exploration, requirements understanding, and requirements enforcement at the code level. Each of these software tasks involves micro-decisions which can be taken autonomously by an AI agent, aided by program analysis tools. This creates the vision of an AI software engineer, where the AI agent can be seen as a member of a development team. Conceptually, the key to successfully developing trustworthy agentic AI-based software workflows will be to resolve the core difficulty in software engineering - the deciphering and clarification of developer intent. Specification inference, or deciphering the intent, thus lies at the heart of many software tasks, including software maintenance and program repair. A successful deployment of agentic technology into software engineering would involve making conceptual progress in such intent inference via agents. Trusting the AI agent becomes a key aspect, as software engineering becomes more automated. Higher automation also leads to higher volume of code being automatically generated, and then integrated into code-bases. Thus to deal with this explosion, an emerging direction is AI-based verification and validation (V & V) of AI generated code. We posit that agentic software workflows in future will include such AIbased V&V.
Engineering design risks could cause unaffordable losses, and thus risk assessment plays a critical role in engineering design. On the other hand, the high complexity of modern engineering designs makes it difficult to assess risks effectively and accurately due to the complex two-way, dynamic causal-effect risk relations in engineering designs. To address this problem, this paper proposes a new risk assessment method called token fuzzy cognitive map (Token-FCM). Its basic idea is to model the two-way causal-risk relations with the FCM method, and then augment FCM with a token mechanism to model the dynamics in causal-effect risk relations. Furthermore, the fuzzy sets and the group decision-making method are introduced to initialize the Token-FCM method so that comprehensive and accurate risk assessments can be attained. The effectiveness of the proposed method has been demonstrated by a real example of engine design for a horizontal directional drilling machine.
Allysson Allex Araújo, Marcos Kalinowski, Daniel Graziotin
This paper explores the intricate challenge of understanding and measuring software engineer behavior. More specifically, we revolve around a central question: How can we enhance our understanding of software engineer behavior? Grounded in the nuanced complexities addressed within Behavioral Software Engineering (BSE), we advocate for holistic methods that integrate quantitative measures, such as psychometric instruments, and qualitative data from diverse sources. Furthermore, we delve into the relevance of this challenge within national and international contexts, highlighting the increasing interest in understanding software engineer behavior. Real-world initiatives and academic endeavors are also examined to underscore the potential for advancing this research agenda and, consequently, refining software engineering practices based on behavioral aspects. Lastly, this paper addresses different ways to evaluate the progress of this challenge by leveraging methodological skills derived from behavioral sciences, ultimately contributing to a deeper understanding of software engineer behavior and software engineering practices.
Alvaro M. Aparicio-Morales, Enrique Moguel, Luis Mariano Bibbo
et al.
Quantum computing represents a revolutionary computational paradigm with the potential to address challenges beyond classical computers' capabilities. The development of robust quantum software is indispensable to unlock the full potential of quantum computing. Like classical software, quantum software is expected to be complex and extensive, needing the establishment of a specialized field known as Quantum Software Engineering. Recognizing the regional focus on Latin America within this special issue, we have boarded on an in-depth inquiry encompassing a systematic mapping study of existing literature and a comprehensive survey of experts in the field. This rigorous research effort aims to illuminate the current landscape of Quantum Software Engineering initiatives undertaken by universities, research institutes, and companies across Latin America. This exhaustive study aims to provide information on the progress, challenges, and opportunities in Quantum Software Engineering in the Latin American context. By promoting a more in-depth understanding of cutting-edge developments in this burgeoning field, our research aims to serve as a potential stimulus to initiate pioneering initiatives and encourage collaborative efforts among Latin American researchers.
While the methodological rigor of computing research has improved considerably in the past two decades, quantitative software engineering research is hampered by immature measures and inattention to theory. Measurement-the principled assignment of numbers to phenomena-is intrinsically difficult because observation is predicated upon not only theoretical concepts but also the values and perspective of the research. Despite several previous attempts to raise awareness of more sophisticated approaches to measurement and the importance of quantitatively assessing reliability and validity, measurement issues continue to be widely ignored. The reasons are unknown, but differences in typical engineering and computer science graduate training programs (compared to psychology and management, for example) are involved. This chapter therefore reviews key concepts in the science of measurement and applies them to software engineering research. A series of exercises for applying important measurement concepts to the reader's research are included, and a sample dataset for the reader to try some of the statistical procedures mentioned is provided.
The temperature-controlling characteristics of a thermal mechanical package (TMP) are crucial to the experiments in inertial confinement fusion. This study addresses the semi- and holo-TMP structures. The influences of relative factors, including the pressure of helium inside the hohlraum, width of bulge loops, and power of heat rings, on the temperature-controlling characteristics of TMP are investigated systematically. The results show that a semi-TMP structure has a better temperature-control performance than a holo-structure under the same thermal conditions. Increasing the pressure of helium makes a positive but limited contribution to the TMP temperature-control effect. The presence of bulge loops enhances the heat transfer between the TMP and gold hohlraum, thereby improving the temperature-control performance. In addition, the widths of the bulge loops slightly improve the temperature-controlling effect. The temperature response of the gold hohlraum to variations in the heat ring power exhibits clear differences between the semi- and holo-TMP structures. As the heating power increases, the maximum temperature difference of the gold hohlraum increases linearly at a rate of 0.38 K/W and 0.25 K/W for the semi- and the holo-TMP structures, respectively, which indicates a better temperature-controlling effect on the semi-TMP structure.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
In today's world, many cities are embracing cutting-edge technology and transforming into "smart cities". These emerging innovations are revolutionizing the standard of living for people, and as a result, smart city infrastructure development has become a major focus for city planners and policymakers worldwide. The goal is to create more livable, sustainable, and efficient urban environments, and software engineering plays a crucial role in achieving this. In this article, we will delve into what makes a city "smart" and what it means for the future. We will explore the software engineering roadmap for smart city infrastructure development, highlighting the goals and challenges that come with this innovative approach to urban planning. Our aim is to provide valuable insights into the importance of software engineering in achieving successful smart city infrastructure development. As cities continue to grow and evolve, it is essential to adopt new technologies that can help us build smarter, more sustainable communities. Smart city initiatives are paving the way for a brighter future, and software engineering is at the forefront of this movement. By understanding the software engineering roadmap for smart city infrastructure development, we can work towards creating more livable, efficient, and sustainable urban environments for generations to come.
Mohammad Nurur Rahman, Mehedi Hasan, Md Jahidul Islam Faisal
Syngas or synthesis gas is very demanding gas mixture mainly containing Hydrogen (H2) and Carbon monoxide (CO) gas which is used in producing of Hydrogen fuel, Methanol, Ammonia. Syngas can be produced from renewable resource like biomass. Sawdust is one of the common, less costly waste biomass found in sawmill. Generally, sawdust is used for burning to make heat for cooking but pyrolysis and Gasification process can turn it as biofuel which is more economic and have versatile usability. By simulating the gasification of sawdust through Aspen plus software, the amount of formation of syngas can be calculated. R-yield or yield reactor is used to calculate the production of syngas for 100% conversion of Biomass into Biofuel as R-yield as ideal reactor, need to specify the basic yield based on ultimate analysis of sawdust. R-Gibb reactor is used to calculate the total heat required completing the whole process found in combustion reaction and dryer is used to dry wet biomass using combustion heat. Produced syngas contains not only H2, CO rather than the mixture of H2, CO, CO2, CH4, C2H4, N2, NH3, H2S, H2O, solid Carbon and ash content. The simulation conducted on gasification clearly gives the information that’s by entering about 1000 kg sawdust in a gasifier with 1358 kg air, temperature raised about 700° C, after thermal conversion about 2218.5 kg syngas, 111.2 kg H2O and 28.3 kg ash would have found. Primarily, simple flash separator is used to calculate the separated ash content and solid carbon. Cryogenic distillation can be used for individual gas separation. Chemical Engineering Research Bulletin 23(2023): 90-94
Metal-organic frameworks demonstrate significant application potential in sorption refrigeration driven by low-grade thermal energy owing to their ultrahigh specific surface area, large pore volume, and adjustable structure. Among the most structurally stable MOFs, ZIF-8(Zn) exhibits excellent structural stability and stable sorption properties. Herein, the grand canonical Monte Carlo molecular simulation method is adopted to simulate the sorption reaction between ZIF-8(Zn) and NH3, which is described by the universal force field and transferable potentials for phase equilibria force field parameters, respectively. Using both molecular simulation results and the adsorption refrigeration thermodynamic cycle model, we investigate the adsorption and refrigeration performances of the ZIF-8(Zn)/NH3 working fluid pair. The results show that the isothermal ammonia sorption capacity of ZIF-8(Zn) increases with pressure and that the sorption capacity reaches 0.305 g/g and 0.231 g/g at 298 Kand 398 K, respectively. Moreover, the total heat of sorption at the same temperature increases with pressure owing to the increase in the reaction heat of the NH3 intermolecular interaction. The isothermal sorption heat from the interaction between ZIF-8(Zn) and NH3 is relatively stable at different pressures. Furthermore, the sorption density distribution diagram shows that numerous NH3 molecules are adsorbed at the metal sites. Some NH3 molecules fail to pass through the small channels of ZIF-8(Zn), whereas others accumulated inside the six-membered ring cage of ZIF-8(Zn) after passing through the holes. Finally, the sorption refrigeration system demonstrates stable refrigeration performance and good adaptability to refrigeration temperatures. Ideally, the coefficient of performance (COP) should be 0.43 when the refrigeration temperature is 283 K (ventilation conditions), and it should be able to reach 0.38 when the refrigeration temperature is 243 K (freezing conditions).
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Frosting (especially severe frosting) in air-source heat pump (ASHP) units leads to a substantial decrease in operating performance. The development of frost-suppression ASHPs (FSASHPs) is key to ensuring their efficient application and healthy development. In this study, a field test study was conducted using a residential demonstration project in Kangding City, Sichuan Province. The study compared an FSASHP and a conventional ASHP unit to explore the frosting suppression and operating performances and analyze the economy of the FSASHP unit. The results showed that the effects of frosting suppression and the improvement of heating performances were substantial for the FSASHP unit. The coefficients of performance of the FSASHP unit were 21%–37.3% higher, with annual costs 13% lower compared to those of the conventional ASHP unit. The payback period of the FSASHP unit was approximately 1 year, and the FSASHP unit had better technical and economic efficiency. This study was helpful for the efficient application of ASHP units in severe frosting regions.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Jiadi Zhang, Mohammad Reza Amini, Ilya Kolmanovsky
et al.
This paper presents the results of a model predictive controller (MPC) development for diesel engine air-path regulation. The control objective is to track the intake manifold pressure and exhaust gas recirculation (EGR) rate targets by manipulating the EGR valve and variable geometry turbine (VGT) while satisfying state and control constraints. The MPC controller is designed and verified using a high-fidelity engine model in GT-Power. The controller exploits a low-order rate-based linear parameter-varying (LPV) model for prediction which is identified from transient response data generated by the GT-Power model. It is shown that transient engine thermal dynamics influence the airpath dynamics, specifically the intake manifold pressure response, however, MPC demonstrates robustness against inaccuracies in modeling these thermal dynamics. In particular, we show that MPC can be successfully implemented using a rate-based prediction model with two inputs (EGR and VGT positions) identified from data with steady-state wall temperature dynamics, however, closed-loop performance can be improved if a prediction model (i) is identified from data with transient thermal dynamics, and (ii) has the fuel injection rate as extra model input. Further, the MPC calibration process across the engine operating range to achieve improved performance is addressed. As the MPC calibration is shown to be sensitive to the operating conditions, a fast calibration process is proposed.
In recent years Open Innovation (OI) has gained much attention and made firms aware that they need to consider the open environment surrounding them. To facilitate this shift Requirements Engineering (RE) needs to be adapted in order to manage the increase and complexity of new requirements sources as well as networks of stakeholders. In response we build on and advance an earlier proposed software engineering framework for fostering OI, focusing on stakeholder management, when to open up, and prioritization and release planning. Literature in open source RE is contrasted against recent findings of OI in software engineering to establish a current view of the area. Based on the synthesized findings we propose a research agenda within the areas under focus, along with a framing-model to help researchers frame and break down their research questions to consider the different angles implied by the OI model.
Biological hydrogels play important physiological roles in the body. These hydrogels often contain ordered subdomains that provide mechanical toughness and other tissue-specific functionality. Filamentous bacteriophages are nanofilaments with a high aspect ratio that can self-assemble into liquid crystalline domains that could be designed to mimic ordered biological hydrogels and can thus find applications in biomedical engineering. We have previously reported hydrogels of pure cross-linked liquid crystalline filamentous phage formed at very high concentrations exhibiting a tightly packed microstructure and high stiffness. In this work, we report a method for inducing self-assembly of filamentous phage into liquid crystalline hydrogels at concentrations that are several orders of magnitude below that of lyotropic liquid crystal formation, thus creating structural order but a less densely packed microstructure. Hybrid hydrogels of M13 phage and bovine serum albumin (0.25 w/v%) were formed and shown to adsorb up to 16× their weight in water. Neither component gelled on its own at the low concentrations used, suggesting synergistic action between the two components in the formation of the hydrogel. The hybrid hydrogels exhibited repetitive self-healing under physiological conditions and at room temperature, autofluorescence in three channels, and antibacterial activity toward Escherichia coli host cells. Furthermore, the hybrid hydrogels exhibited a more than 2× higher ability to pack water compared to BSA-only hydrogels and 2× lower compression modulus compared to tightly packed M13-only hydrogels, suggesting that our method could be used to create hydrogels with tunable mechanical properties and pore structure through the addition of globular proteins, while maintaining bioactivity and microscale structural order.
To ensure that the concentration of indoor pollutants in a laboratory animal room is maintained at a low level, all-fresh-air systems with large air volumes are typically used in traditional air-conditioning systems, which results in high energy consumption for air conditioning. The fresh air volume used to control the concentration of pollutants is considerably higher than that used to control other parameters; therefore, this study proposes an air-conditioning system that realizes independent control of temperature, humidity, and pollutants. The proposed system reduces the fresh air volume and energy consumption of a heating, ventilation, and air-conditioning system. A cage-type laboratory animal room is used as an example; the purification effect of the proposed system is simulated using CFD software, and the system’s energy-saving performance is investigated. The results show that for a fresh air change rate of 8.53 h-1, the circulating air change rate is 13 h-1, and the supply circulating air temperature is 19.4 ℃，the indoor temperature, humidity, and pollutant concentration requirements can be met to ensure the normal survival of mice. Compared with the traditional all-fresh-air system, the fresh air volume and energy consumption of the proposed system is decreased by 57.4% and 47.9%, respectively.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Software engineers need relevant and up-to-date architectural knowledge (AK), in order to make well-founded design decisions. However, finding such AK is quite challenging. One pragmatic approach is to search for AK on the web using traditional search engines (e.g. Google); this is common practice among software engineers. Still, we know very little about what AK is retrieved, from where, and how useful it is. In this paper, we conduct an empirical study with 53 software engineers, who used Google to make design decisions using the Attribute-Driven-Design method. Based on how the subjects assessed the nature and relevance of the retrieved results, we determined how effective web search engines are to find relevant architectural information. Moreover, we identified the different sources of AK on the web and their associated AK concepts.
There is an ongoing debate in computer science how algorithms should best be studied. Some scholars have argued that experimental evaluations should be conducted, others emphasize the benefits of formal analysis. We believe that this debate less of a question of either-or, because both views can be integrated into an overarching framework. It is the ambition of this paper to develop such a framework of algorithm engineering with a theoretical foundation in the philosophy of science. We take the empirical nature of algorithm engineering as a starting point. Our theoretical framework builds on three areas discussed in the philosophy of science: ontology, epistemology and methodology. In essence, ontology describes algorithm engineering as being concerned with algorithmic problems, algorithmic tasks, algorithm designs and algorithm implementations. Epistemology describes the body of knowledge of algorithm engineering as a collection of prescriptive and descriptive knowledge, residing in World 3 of Popper's Three Worlds model. Methodology refers to the steps how we can systematically enhance our knowledge of specific algorithms. In this context, we identified seven validity concerns and discuss how researchers can respond to falsification. Our framework has important implications for researching algorithms in various areas of computer science.
Robots are being applied in a vast range of fields, leading researchers and practitioners to write tasks more complex than in the past. The robot software complexity increases the difficulty of engineering the robot's software components with quality requirements. Researchers and practitioners have applied software engineering (SE) approaches and robotic domains to address this issue in the last two decades. This study aims to identify, classify and evaluate the current state-of-the-art Software Engineering for Robotic Systems (SERS). We systematically selected and analyzed 50 primary studies extracted from an automated search on Scopus digital library and manual search on the two editions of the RoSE workshop. We present three main contributions. Firstly, we provide an analysis from three following perspectives: demographics of publication, SE areas applied in robotics domains, and RSE findings. Secondly, we show a catalogue of research studies that apply software engineering techniques in the robotic domain, classified with the SWEBOK guide. We have identified 5 of 15 software engineering areas from the SWEBOK guide applied explicitly in robotic domains. The majority of the studies focused on the development phase (design, models and methods and construction). Testing and quality software areas have little coverage in SERS. Finally, we identify research opportunities and gaps in software engineering for robotic systems for future studies.