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Despite many advances in knowledge engineering (KE), challenges remain in areas such as engineering knowledge graphs (KGs) at scale, keeping up with evolving domain knowledge, multilingualism, and multimodality. Recently, KE has used LLMs to support semi-automatic tasks, but the most effective use of LLMs to support knowledge engineers across the KE activites is still in its infancy. To explore the vision of LLM copilots for KE and change existing KE practices, we conducted a multimethod study during a KE hackathon. We investigated participants' views on the use of LLMs, the challenges they face, the skills they may need to integrate LLMs into their practices, and how they use LLMs responsibly. We found participants felt LLMs could contribute to improving efficiency when engineering KGs, but presented increased challenges around the already complex issues of evaluating the KE tasks. We discovered prompting to be a useful but undervalued skill for knowledge engineers working with LLMs, and note that natural language processing skills may become more relevant across more roles in KG construction. Integrating LLMs into KE tasks needs to be mindful of potential risks and harms related to responsible AI. Given the limited ethical training, most knowledge engineers receive solutions such as our suggested `KG cards' based on data cards could be a useful guide for KG construction. Our findings can support designers of KE AI copilots, KE researchers, and practitioners using advanced AI to develop trustworthy applications, propose new methodologies for KE and operate new technologies responsibly.

Incorporating Machine Learning (ML) into existing systems is a demand that has grown among several organizations. However, the development of ML-enabled systems encompasses several social and technical challenges, which must be addressed by actors with different fields of expertise working together. This paper has the objective of understanding how to enhance the collaboration between two key actors in building these systems: software engineers and data scientists. We conducted two focus group sessions with experienced data scientists and software engineers working on real-world ML-enabled systems to assess the relevance of different recommendations for specific technical tasks. Our research has found that collaboration between these actors is important for effectively developing ML-enabled systems, especially when defining data access and ML model deployment. Participants provided concrete examples of how recommendations depicted in the literature can benefit collaboration during different tasks. For example, defining clear responsibilities for each team member and creating concise documentation can improve communication and overall performance. Our study contributes to a better understanding of how to foster effective collaboration between software engineers and data scientists creating ML-enabled systems.

The landscape of software engineering is evolving rapidly amidst the digital transformation and the ascendancy of AI, leading to profound shifts in the role and responsibilities of software engineers. This evolution encompasses both immediate changes, such as the adoption of Language Model-based approaches in coding, and deeper shifts driven by the profound societal and environmental impacts of technology. Despite the urgency, there persists a lag in adapting to these evolving roles. By fostering ongoing discourse and reflection on Software Engineers role and responsibilities, this vision paper seeks to cultivate a new generation of software engineers equipped to navigate the complexities and ethical considerations inherent in their evolving profession.

Alongside software testing education in universities, a great extent of effort and resources are spent on software-testing training activities in industry. For example, there are several international certification schemes in testing, such as those provided by the International Software Testing Qualifications Board (ISTQB), which have been issued to more than 914K testers so far. To train the highly qualified test engineers of tomorrow, it is important for both university educators and trainers in industry to be aware of the status of software testing education in academia versus its training in industry, to analyze the relationships of these two approaches, and to assess ways on how to improve the education / training landscape. For that purpose, this paper provides a pragmatic overview of the issue, presents several recommendations, and hopes to trigger further discussions in the community, between industry and academia, on how to further improve the status-quo, and to find further best practices for more effective education and training of software testers. The paper is based on combined ~40 years of the two authors' technical experience in test engineering, and their ~30 years of experience in providing testing education and training in more than six countries.

Background: The integration of the General Data Protection Regulation (GDPR) and the Medical Device Regulation (MDR) creates complexities in conducting Data Protection Impact Assessments (DPIAs) for medical devices. The adoption of non-binding standards like ISO and IEC can harmonize these processes by enhancing accountability and privacy by design. Methods: This study employs a multidisciplinary literature review, focusing on GDPR and MDR intersection in medical devices that process personal health data. It evaluates key standards, including ISO/IEC 29134 and IEC 62304, to propose a unified approach for DPIAs that aligns with legal and technical frameworks. Results: The analysis reveals the benefits of integrating ISO/IEC standards into DPIAs, which provide detailed guidance on implementing privacy by design, risk assessment, and mitigation strategies specific to medical devices. The proposed framework ensures that DPIAs are living documents, continuously updated to adapt to evolving data protection challenges. Conclusions: A unified approach combining European Union (EU) regulations and international standards offers a robust framework for conducting DPIAs in medical devices. This integration balances security, innovation, and privacy, enhancing compliance and fostering trust in medical technologies. The study advocates for leveraging both hard law and standards to systematically address privacy and safety in the design and operation of medical devices, thereby raising the maturity of the MedTech ecosystem., Comment: 20 pages, 1 table

Turnover consists of moving into and out of professional employees in the company in a given period. Such a phenomenon significantly impacts the software industry since it generates knowledge loss, delays in the schedule, and increased costs in the final project. Despite the efforts made by researchers and professionals to minimize the turnover, more studies are needed to understand the motivation that drives Software Engineers to leave their jobs and the main strategies CEOs adopt to retain these professionals in software development companies. In this paper, we contribute a mixed methods study involving semi-structured interviews with Software Engineers and CEOs to obtain a wider opinion of these professionals about turnover and a subsequent validation survey with additional software engineers to check and review the insights from interviews. In studying such aspects, we identified 19 different reasons for software engineers' turnover and 18 more efficient strategies used in the software development industry to reduce it. Our findings provide several implications for industry and academia, which can drive future research.

[Context] The growing software development market has increased the demand for qualified professionals in Software Engineering (SE). To this end, companies must enhance their Recruitment and Selection (R&S) processes to maintain high quality teams, including opening opportunities for beginners, such as trainees and interns. However, given the various judgments and sociotechnical factors involved, this complex process of R&S poses a challenge for recent graduates seeking to enter the market. [Objective] This paper aims to identify a set of anti-patterns and recommendations for early career SE professionals concerning R&S processes. [Method] Under an exploratory and qualitative methodological approach, we conducted six online Focus Groups with 18 recruiters with experience in R&S in the software industry. [Results] After completing our qualitative analysis, we identified 12 anti-patterns and 31 actionable recommendations regarding the hiring process focused on entry level SE professionals. The identified anti-patterns encompass behavioral and technical dimensions innate to R&S processes. [Conclusion] These findings provide a rich opportunity for reflection in the SE industry and offer valuable guidance for early-career candidates and organizations. From an academic perspective, this work also raises awareness of the intersection of Human Resources and SE, an area with considerable potential to be expanded in the context of cooperative and human aspects of SE., Comment: 33 pages

The paper presents the second part of a precise description of the prototype that has been developed in the course of the ISAC project over the last two decades. This part describes the "specify-phase", while the first part describing the "solve-phase" is already published. In the specify-phase a student interactively constructs a formal specification. The ISAC prototype implements formal specifications as established in theoretical computer science, however, the input language for the construction avoids requiring users to have knowledge of logic; this makes the system useful for various engineering faculties (and also for high school). The paper discusses not only ISAC's design of the specify-phase in detail, but also gives a brief introduction to implementation with the aim of advertising the re-use of formal frameworks (inclusive respective front-ends) with their generic tools for language definition and their rich pool of software components for formal mathematics., Comment: In Proceedings ThEdu'23, arXiv:2404.03709

The mathematical theory underlying Hamiltonian mechanics is called symplectic geometry. So symplectic geometry arose from the roots of mechanics and is seen as one of the most valuable links between physics and mathematics today. Symplectic geometry in its simplest possible case is the geometry of the plane. Surprise is, we can hardly find relevant publications regarding that simple 2D case. Primary goal of this paper is to discuss the suitability of symplectic geometry in the plane for solving standard geometric and mechanical problems in physics and engineering education., Comment: 14 pages

A proper knowledge of the reality of a physical theory is important to get an understanding of empirical phenomena. Despite its extraordinary predictive successes, the orthodox (also known as the Copenhagen) theory provides an indigestible definition of quantum reality: objects (such as electrons) become part of a nebulous many-particle wave function with no properties at all, unless the property is explicitly measured. To make matters worse, orthodox theory does not define measurements in a clear way. This view of reality is foreign to most modern engineers who assume that quantum objects, like classical objects, always have real properties, independent of any measurement. Despite being contrary to the orthodox theory, the intuition of engineers is not in conflict with other quantum theories, where the observer plays no fundamental role. Good quantum intuition needs to be based on a correct knowledge of the fundamental elements of the quantum theory that is being used. We argue that engineers are actually led to the natural quantum reality offered by these alternative approaches., Comment: 6 pages and 2 figures

In recent years, Machine Learning (ML) components have been increasingly integrated into the core systems of organizations. Engineering such systems presents various challenges from both a theoretical and practical perspective. One of the key challenges is the effective interaction between actors with different backgrounds who need to work closely together, such as software engineers and data scientists. This paper presents an exploratory case study to understand the current interaction and collaboration dynamics between these roles in ML projects. We conducted semi-structured interviews with four practitioners with experience in software engineering and data science of a large ML-enabled system project and analyzed the data using reflexive thematic analysis. Our findings reveal several challenges that can hinder collaboration between software engineers and data scientists, including differences in technical expertise, unclear definitions of each role's duties, and the lack of documents that support the specification of the ML-enabled system. We also indicate potential solutions to address these challenges, such as fostering a collaborative culture, encouraging team communication, and producing concise system documentation. This study contributes to understanding the complex dynamics between software engineers and data scientists in ML projects and provides insights for improving collaboration and communication in this context. We encourage future studies investigating this interaction in other projects.

Organizations rely on machine learning engineers (MLEs) to deploy models and maintain ML pipelines in production. Due to models' extensive reliance on fresh data, the operationalization of machine learning, or MLOps, requires MLEs to have proficiency in data science and engineering. When considered holistically, the job seems staggering -- how do MLEs do MLOps, and what are their unaddressed challenges? To address these questions, we conducted semi-structured ethnographic interviews with 18 MLEs working on various applications, including chatbots, autonomous vehicles, and finance. We find that MLEs engage in a workflow of (i) data preparation, (ii) experimentation, (iii) evaluation throughout a multi-staged deployment, and (iv) continual monitoring and response. Throughout this workflow, MLEs collaborate extensively with data scientists, product stakeholders, and one another, supplementing routine verbal exchanges with communication tools ranging from Slack to organization-wide ticketing and reporting systems. We introduce the 3Vs of MLOps: velocity, visibility, and versioning -- three virtues of successful ML deployments that MLEs learn to balance and grow as they mature. Finally, we discuss design implications and opportunities for future work., Comment: arXiv admin note: text overlap with arXiv:2209.09125

The Impostor Phenomenon (IP) is widely discussed in Science, Technology, Engineering, and Mathematics (STEM) and has been evaluated in Computer Science students. However, formal research on IP in software engineers has yet to be conducted, although its impacts may lead to mental disorders such as depression and burnout. This study describes a survey that investigates the extent of impostor feelings in software engineers, considering aspects such as gender, race/ethnicity, and roles. Furthermore, we investigate the influence of IP on their perceived productivity. The survey instrument was designed using a theory-driven approach and included demographic questions, an internationally validated IP scale, and questions for measuring perceived productivity based on the SPACE framework constructs. The survey was sent to companies operating in various business sectors. Data analysis used bootstrapping with resampling to calculate confidence intervals and Mann-Whitney statistical significance testing for assessing the hypotheses. We received responses from 624 software engineers from 26 countries. The bootstrapping results reveal that a proportion of 52.7% of software engineers experience frequent to intense levels of IP and that women suffer at a significantly higher proportion (60.6%) than men (48.8%). Regarding race/ethnicity, we observed more frequent impostor feelings in Asian (67.9%) and Black (65.1%) than in White (50.0%) software engineers. We also observed that the presence of IP is less common among individuals who are married and have children. Moreover, the prevalence of IP showed a statistically significant negative effect on the perceived productivity for all SPACE framework constructs. The evidence relating IP to software engineers provides a starting point to help organizations find ways to raise awareness of the problem and improve the emotional skills of software professionals., Comment: Preprint with the original submission accepted for publication at ICSE-SEIS 2024

Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle. In this paper, we present Rankitect, a NAS software framework for ranking systems at Meta. Rankitect seeks to build brand new architectures by composing low level building blocks from scratch. Rankitect implements and improves state-of-the-art (SOTA) NAS methods for comprehensive and fair comparison under the same search space, including sampling-based NAS, one-shot NAS, and Differentiable NAS (DNAS). We evaluate Rankitect by comparing to multiple production ranking models at Meta. We find that Rankitect can discover new models from scratch achieving competitive tradeoff between Normalized Entropy loss and FLOPs. When utilizing search space designed by engineers, Rankitect can generate better models than engineers, achieving positive offline evaluation and online A/B test at Meta scale., Comment: Wei Wen and Kuang-Hung Liu contribute equally

Flux transformers and superconducting quantum interference devices (SQUIDs) are two key superconducting devices used in the cutting-edge magnetic-field sensor systems, such as magnetocardiography (MCG) and magnetoencephalography (MEG). They are superconductor integrated circuits enabled by two superconductor elements, superconductor wire and Josephson junction, based on Meissner effect and Josephson effect. The principles of SQUID sensors are unfamiliar for electronics engineers who are trained with semiconductor electronics, due to the superconductor physics. We present an introduction to SQUID sensors, ranged from superconductor elements to the superconductor-semiconductor hybrid system. It is demonstrated that, SQUIDs inside are the resistor-inductor-capacitor (RLC) network driven by Josephson currents, and share the common network equation with normal RLC circuits; SQUID outside are magnetic-field-effect transistors (MFETs), and are dual to the semiconductor FETs in signal conversions; SQUID magnetic-field sensors are linear current amplifiers, as semiconductor FET electric-field sensors are linear voltage amplifiers., Comment: 53 pages, 50 figures, Principles covered from Josephson junction to SQUID sensor system