This paper investigates how software professionals perceive the economic implications of diversity in software engineering teams. Motivated by a gap in software engineering research, which has largely emphasized socio-technical and process-related outcomes, we adopted a qualitative interview approach to capture practitioners' reasoning about diversity in relation to economic and market-oriented considerations. Based on interviews with ten software professionals, our analysis indicates that diversity is perceived as economically relevant through its associations with cost reduction and containment, revenue generation, time to market, process efficiency, innovation, and market alignment. Participants typically grounded these perceptions in concrete project experiences rather than abstract economic reasoning, framing diversity as a practical resource that supports project delivery, competitiveness, and organizational viability. Our findings provide preliminary empirical insights into how economic aspects of diversity are understood in software engineering practice.
Vibe coding, the much-touted use of AI techniques for programming, faces two overwhelming obstacles: the difficulty of specifying goals ("prompt engineering" is a form of requirements engineering, one of the toughest disciplines of software engineering); and the hallucination phenomenon. Programs are only useful if they are correct or very close to correct. The solution? Combine the creativity of artificial intelligence with the rigor of formal specification methods and the power of formal program verification, supported by modern proof tools.
AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be addressed before automated software engineering reaches its full potential. It should be possible to reach high levels of automation where humans can focus on the critical decisions of what to build and how to balance difficult tradeoffs while most routine development effort is automated away. Reaching this level of automation will require substantial research and engineering efforts across academia and industry. In this paper, we aim to discuss progress towards this in a threefold manner. First, we provide a structured taxonomy of concrete tasks in AI for software engineering, emphasizing the many other tasks in software engineering beyond code generation and completion. Second, we outline several key bottlenecks that limit current approaches. Finally, we provide an opinionated list of promising research directions toward making progress on these bottlenecks, hoping to inspire future research in this rapidly maturing field.
Knowledge engineering is the process of creating and maintaining knowledge-producing systems. Throughout the history of computer science and AI, knowledge engineering workflows have been widely used given the importance of high-quality knowledge for reliable intelligent agents. Meanwhile, the scope of knowledge engineering, as apparent from its target tasks and use cases, has been shifting, together with its paradigms such as expert systems, semantic web, and language modeling. The intended use cases and supported user requirements between these paradigms have not been analyzed globally, as new paradigms often satisfy prior pain points while possibly introducing new ones. The recent abstraction of systemic patterns into a boxology provides an opening for aligning the requirements and use cases of knowledge engineering with the systems, components, and software that can satisfy them best. This paper proposes a vision of harmonizing the best practices in the field of knowledge engineering by leveraging the software engineering methodology of creating reference architectures. We describe how a reference architecture can be iteratively designed and implemented to associate user needs with recurring systemic patterns, building on top of existing knowledge engineering workflows and boxologies. We provide a six-step roadmap that can enable the development of such an architecture, providing an initial design and outcome of the definition of architectural scope, selection of information sources, and analysis. We expect that following through on this vision will lead to well-grounded reference architectures for knowledge engineering, will advance the ongoing initiatives of organizing the neurosymbolic knowledge engineering space, and will build new links to the software architectures and data science communities.
Egor Klimov, Muhammad Umair Ahmed, Nikolai Sviridov
et al.
Bus factor (BF) is a metric that tracks knowledge distribution in a project. It is the minimal number of engineers that have to leave for a project to stall. Despite the fact that there are several algorithms for calculating the bus factor, only a few tools allow easy calculation of bus factor and convenient analysis of results for projects hosted on Git-based providers. We introduce Bus Factor Explorer, a web application that provides an interface and an API to compute, export, and explore the Bus Factor metric via treemap visualization, simulation mode, and chart editor. It supports repositories hosted on GitHub and enables functionality to search repositories in the interface and process many repositories at the same time. Our tool allows users to identify the files and subsystems at risk of stalling in the event of developer turnover by analyzing the VCS history. The application and its source code are publicly available on GitHub at https://github.com/JetBrains-Research/bus-factor-explorer. The demonstration video can be found on YouTube: https://youtu.be/uIoV79N14z8
Robert Bocchino, Arne Nordmann, Allison Thackston
et al.
Robotics software is pushing the limits of software engineering practice. The 3rd International Workshop on Robotics Software Engineering held a panel on "the best practices for robotic software engineering". This article shares the key takeaways that emerged from the discussion among the panelists and the workshop, ranging from architecting practices at the NASA/Caltech Jet Propulsion Laboratory, model-driven development at Bosch, development and testing of autonomous driving systems at Waymo, and testing of robotics software at XITASO. Researchers and practitioners can build on the contents of this paper to gain a fresh perspective on their activities and focus on the most pressing practices and challenges in developing robotics software today.
As software engineering research becomes more concerned with the psychological, sociological and managerial aspects of software development, relevant theories from reference disciplines are increasingly important for understanding the field's core phenomena of interest. However, the degree to which software engineering research draws on relevant social sciences remains unclear. This study therefore investigates the use of social science theories in five influential software engineering journals over 13 years. It analyzes not only the extent of theory use but also what, how and where these theories are used. While 87 different theories are used, less than two percent of papers use a social science theory, most theories are used in only one paper, most social sciences are ignored, and the theories are rarely tested for applicability to software engineering contexts. Ignoring relevant social science theories may (1) undermine the community's ability to generate, elaborate and maintain a cumulative body of knowledge; and (2) lead to oversimplified models of software engineering phenomena. More attention to theory is needed for software engineering to mature as a scientific discipline.
Acoustic quality and, in wider terms, global comfort are crucial aspects for the design of built environments. There are objective and subjective parameters, from different disciplines, which may be used for contributing to the definition of global comfort. Concerning restaurants, dining out represents an opportunity for spending quality time in good company; therefore, a multidisciplinary approach is required to the designer in order to create a unique experience for all senses. In this paper, factors involved in the assessment of the global comfort are presented. Vie en.ro.se Ingegneria has worked for the improvement of global comfort in many restaurants, such as Fratelli Briganti’s Restaurant. The project was designed and defined starting from the observations collected through a Customer Satisfaction questionnaire, together with the results of acoustics and lighting measurements carried out on site. According to the outcomes, the intervention has interested different disciplines: acoustics, lighting engineering, and thermo engineering. At the end of the renovation, the Fratelli Briganti’s Restaurant has been reopened and the same Customer Satisfaction questionnaire has been distributed. The outcomes show a general improvement of the comfort conditions.
Given the increasing attention of the scientific literature on the achievement of high levels of acoustic comfort in classrooms, this work aims to establish the accuracy of analytical prediction methods present in standards and in literature for the calculation of the main intelligibility parameters inside speech rooms, with particular reference to the acoustics of university classrooms. The predictive methods will be evaluated both for their calculation speed and their reliability with the aim of possibly allowing their better future use in the preliminary analysis of classrooms. The values found by the analytical prediction methods were then compared with those measured in field in five different classrooms of the School of Engineering of the University of Pisa and with the values provided by the Italian regulations, in order to be able to directly evaluate the possibility of their use. The results found show that the methods reported here can find reliable values for a preliminary estimation in view of the compliance with the current regulations, but clearly cannot replace more accurate survey systems.
Temperature monitoring during the life time of heat source components in engineering systems becomes essential to guarantee the normal work and the working life of these components. However, prior methods, which mainly use the interpolate estimation to reconstruct the temperature field from limited monitoring points, require large amounts of temperature tensors for an accurate estimation. This may decrease the availability and reliability of the system and sharply increase the monitoring cost. To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with limited monitoring points unsupervisedly. First, we define the TFR-HSS task mathematically, and numerically model the task, and hence transform the task as an image-to-image regression problem. Then this work develops the deep reversible regression model which can better learn the physical information, especially over the boundary. Finally, considering the physical characteristics of heat conduction as well as the boundary conditions, this work proposes the physics-informed reconstruction loss including four training losses and jointly learns the deep surrogate model with these losses unsupervisedly. Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness of the proposed method.
The use of conceptual models to foster requirements engineering has been proposed and evaluated as beneficial for several decades. For instance, goal-oriented requirements engineering or the specification of scenarios are commonly done using conceptual models. Bringing such model-based requirements engineering approaches into industrial practice typically requires industrial training. In this paper, we report lessons learned from a training program for teaching industry professionals model-based requirements engineering. Particularly, we as educators and learners report experiences from designing the training program, conducting the actual training, and applying the instructed material in our day-to-day work. From these findings we provide guidelines for educators designing requirements engineering courses for industry professionals.
Abstract: In this field of comparative research study, comparison of two car model, a standard car and an optimized car with respect to aerodynamic analysis/aeroacoustics analysis with the help of CFD software Ansys 2019 R2 version is taken in consideration to compare the results and get to know if the optimized car model has reduction in the generation of aerodynamic noise when it travels at four different speeds (30 m/s, 40 m/s, 50m/s and 60m/s). For carried out aeroacoustics/aerodynamic noise analysis two main model are used turbulence model and an acoustics model. For turbulence model and K-epsilon model is used as it is widely used for getting turbulence generation and for acoustics model a Broadband noise model is used to generate the results through numerical simulation and data sources. First a standard sedan car is modelled in Onshape CAD tool and aeroacoustics analysis is carried out on this standard sedan car to get to know the source of aerodynamic noise. From the standard car results made changes in the sedan car geometry like giving fillets to point/sharp edges of wheel arcs, front bumper, hood-line, fender and start of roofline from A-pillar, providing under-flush, optimizing A-pillar beam and optimizing outside rear view mirror and making them fully camera integrated mirror to reduce wake. After optimizing standard geometry carried out aerodynamic analysis with the same four different speed given for standard car (30 m/s, 40m/s, 50m/s and 60 m/s). Generated contour plots and isosurface from CFX for flow characteristics and acoustic sound source with various model like Proudman’s acoustic power level in Db, Curle surface acoustic power level in decibels (dB) and also, Lilley S total noise source to show the sources of noise and how many decibels of noise is generated from those sources. Maximum of 100 decibels of noise is generated from the front bumper and a minimum of 80 decibels were monitored in the results process after comparing the results with standard car which has noise nearly 120 decibels with high fluctuations of turbulence kinetic energy and decrease in its pressure level. Keyword: Aeroacoustics, Aerodynamic noise, Aeolin sound, Fluid dynamics, NVH (noise, vibration and harshness) and CFD (computational fluid dynamics).
Quantum software plays a critical role in exploiting the full potential of quantum computing systems. As a result, it has been drawing increasing attention recently. This paper defines the term "quantum software engineering" and introduces a quantum software life cycle. The paper also gives a generic view of quantum software engineering and discusses the quantum software engineering processes, methods, and tools. Based on these, the paper provides a comprehensive survey of the current state of the art in the field and presents the challenges and opportunities we face. The survey summarizes the technology available in the various phases of the quantum software life cycle, including quantum software requirements analysis, design, implementation, test, and maintenance. It also covers the crucial issues of quantum software reuse and measurement.
Vibhatha Abeykoon, Niranda Perera, Chathura Widanage
et al.
Data engineering is becoming an increasingly important part of scientific discoveries with the adoption of deep learning and machine learning. Data engineering deals with a variety of data formats, storage, data extraction, transformation, and data movements. One goal of data engineering is to transform data from original data to vector/matrix/tensor formats accepted by deep learning and machine learning applications. There are many structures such as tables, graphs, and trees to represent data in these data engineering phases. Among them, tables are a versatile and commonly used format to load and process data. In this paper, we present a distributed Python API based on table abstraction for representing and processing data. Unlike existing state-of-the-art data engineering tools written purely in Python, our solution adopts high performance compute kernels in C++, with an in-memory table representation with Cython-based Python bindings. In the core system, we use MPI for distributed memory computations with a data-parallel approach for processing large datasets in HPC clusters.
Valley states, labeling the frequency extrema in momentum space, carry a new degree of freedom (valley pseudospin) for topological transport of sound in sonic crystals. Recently, the field of valley acoustics has become a hotspot due to its potentials in developing various topological-insulator-based devices. In most previous works, topological valley transport is implemented at the interfaces of two connected artificial crystals. With respect to the interface, the mirror symmetry of crystal structures supports either even-mode or odd-mode valley states. In this work, we propose a physical insight of transforming one hexagonal crystal into a virtual lattice by utilizing the mirror operation of rigid or soft boundaries, which greatly reduces the dimension of the acoustic structure and provides a possible way to implement the programmable routing of topological propagation. We investigate two cases that the rigid and soft boundaries are introduced either at the edge or inside a single hexagonal crystal. Our results clearly demonstrate the high-transmission valley transport along the folded boundaries, where reflection or scattering is prohibited at the sharp bending or corners due to topological protection. Three functional devices are exemplified, which are single-crystal-based topological delay-line filter, delay-line switcher and beam splitter. Our work reveals the inherent relation between the field symmetries of valley states and structural symmetries of sonic crystals. Programmable routing of topological sound transport through boundary engineering provides a platform for developing integrated and versatile topological-insulator-based devices.
Background: The state of the art in software engineering consists of a myriad of contributions and the gaps between them; it is difficult to characterize. Questions: In order to help understanding the state of the art, can we identify gaps in our knowledge that are at a very general, widely relevant level? Which research directions do these gaps suggest? Method: 54 expert interviews with senior members of the ICSE community, evaluated qualitatively using elements of Grounded Theory Methodology. Results: Our understanding of complexity, of good-enoughness, and of developers' strengths is underdeveloped. Some other relevant factors' relevance is apparently not clear. Software engineering is not yet an evidence-based discipline. Conclusion: More software engineering research should concern itself with emergence phenomena, with how engineering tradeoffs are made, with the assumptions underlying research works, and with creating certain taxonomies. Such work would also allow software engineering to become more evidence-based.
Deep-Learning(DL) applications have been widely employed to assist in various tasks. They are constructed based on a data-driven programming paradigm that is different from conventional software applications. Given the increasing popularity and importance of DL applications, software engineering practitioners have some techniques specifically for them. However, little research is conducted to identify the challenges and lacks in practice. To fill this gap, in this paper, we surveyed 195 practitioners to understand their insight and experience in the software engineering practice of DL applications. Specifically, we asked the respondents to identify lacks and challenges in the practice of the development life cycle of DL applications. The results present 13 findings that provide us with a better understanding of software engineering practice of DL applications. Further, we distil these findings into 7 actionable recommendations for software engineering researchers and practitioners to improve the development of DL applications.