Software engineers use code-fluent large language models (LLMs) to help explain unfamiliar code, yet LLM explanations are not adapted to engineers' diverse problem-solving needs. We prompted an LLM to adapt to five problem-solving style types from an inclusive design method, the Gender Inclusiveness Magnifier (GenderMag). We ran a user study with software engineers to examine the impact of explanation adaptations on software engineers' perceptions, both for explanations which matched and mismatched engineers' problem-solving styles. We found that explanations were more frequently beneficial when they matched problem-solving style, but not every matching adaptation was equally beneficial; in some instances, diverse engineers found as much (or more) benefit from mismatched adaptations. Through an equity and inclusivity lens, our work highlights the benefits of having an LLM adapt its explanations to match engineers' diverse problem-solving style values, the potential harms when matched adaptations were not perceived well by engineers, and a comparison of how matching and mismatching LLM adaptations impacted diverse engineers.
Neurodiversity describes variation in brain function among people, including common conditions such as Autism spectrum disorder (ASD), Attention deficit hyperactivity disorder (ADHD), and dyslexia. While Software Engineering (SE) literature has started to explore the experiences of neurodivergent software engineers, there is a lack of research that compares their challenges to those of neurotypical software engineers. To address this gap, we analyze existing data from the 2022 Stack Overflow Developer survey that collected data on neurodiversity. We quantitatively compare the answers of professional engineers with ASD (n=374), ADHD (n=1305), and dyslexia (n=363) with neurotypical engineers. Our findings indicate that neurodivergent engineers face more difficulties than neurotypical engineers. Specifically, engineers with ADHD report that they face more interruptions caused by waiting for answers, and that they less frequently interact with individuals outside their team. This study provides a baseline for future research comparing neurodivergent engineers with neurotypical ones. Several factors in the Stack Overflow survey and in our analysis are likely to lead to conservative esti
With the rapid rise of AI coding agents, the fundamental premise of what it means to be a software engineer is in question. In this vision paper, we examine what it means for an AI agent to be considered a software engineer and then critically think about what makes such an agent trustworthy. Grounded in established definitions of SE (SE) and informed by recent research on agentic AI systems, we conceptualise AI software engineers as participants in human-AI SE teams composed of human software engineers and AI agents, and we distinguish trustworthiness as a key property of these systems and actors rather than a subjective human attitude. Extending on historical perspectives and emerging visions, we identify key dimensions that contribute to the trustworthiness of AI software engineers, spanning technical quality, transparency and accountability, epistemic humility, and societal and ethical alignment. Beyond defining these dimensions, we address a critical but underexplored challenge: how trustworthiness can be operationalised in practice. We therefore introduce the notion of evidence-centric inspection, arguing that developers should evaluate selective signals and justifications of
In recent years, the field of software engineering has experienced a considerable increase in demand for competent experts, resulting in an increased demand for platforms that connect software engineers and facilitate collaboration. In response to this necessity, in this paper we present a project to solve the lack of a proper one-stop connection platform for software engineers and promoting collaborative learning and upskilling. The idea of the project is to develop a web-based application (NEXAS) that would facilitate connecting and collaborating between software engineers. The application would perform algorithmic matching to suggest user connections based on their technical profiles and interests. The users can filter profiles, discover open projects, and form collaboration groups. Using this application will enable users to connect with peers having similar interests, thereby creating a community network tailored exclusively for software engineers.
In modern engineering practice, human engineers collaborate in specialized teams to design complex products, with each expert completing their respective tasks while communicating and exchanging results and data with one another. While this division of expertise is essential for managing multidisciplinary complexity, it demands substantial development time and cost. Recently, we introduced OpenFOAMGPT (1.0, 2.0), which functions as an autonomous AI engineer for computational fluid dynamics, and turbulence.ai, which can conduct end-to-end research in fluid mechanics draft publications and PhD theses. Building upon these foundations, we present Engineering.ai, a platform for teams of AI engineers in computational design. The framework employs a hierarchical multi-agent architecture where a Chief Engineer coordinates specialized agents consisting of Aerodynamics, Structural, Acoustic, and Optimization Engineers, each powered by LLM with domain-specific knowledge. Agent-agent collaboration is achieved through file-mediated communication for data provenance and reproducibility, while a comprehensive memory system maintains project context, execution history, and retrieval-augmented doma
Dyslexia is a common learning disorder that primarily impairs an individual's reading and writing abilities. In adults, dyslexia can affect both professional and personal lives, often leading to mental challenges and difficulties acquiring and keeping work. In Software Engineering (SE), reading and writing difficulties appear to pose substantial challenges for core tasks such as programming. However, initial studies indicate that these challenges may not significantly affect their performance compared to non-dyslexic colleagues. Conversely, strengths associated with dyslexia could be particularly valuable in areas like programming and design. However, there is currently no work that explores the experiences of dyslexic software engineers, and puts their strengths into relation with their difficulties. To address this, we present a qualitative study of the experiences of dyslexic individuals in SE. We followed the basic stage of the Socio-Technical Grounded Theory method and base our findings on data collected through 10 interviews with dyslexic software engineers, 3 blog posts and 153 posts on the social media platform Reddit. We find that dyslexic software engineers especially str
Social identity is a concept from psychology that refers to the part of an individual's identity that derives from their group membership(s). In this paper, we explore social identity in members of the professional community of Research Software Engineers (RSEs). Using a mixed-methods approach, our study combined computational linguistic analysis and inferential statistics to examine over 28,000 social media posts, 1,700 blogs, and survey responses from 381 professional RSEs. The findings highlight the emergence of a collective RSE identity and demonstrate its role in shaping professional wellbeing. This study contributes an interdisciplinary perspective by integrating social psychology and software engineering to show how a professional identity evolves and why it matters.
Quantum mechanics, the fundamental theory that governs the behaviour of matter and energy at microscopic scales, forms the foundation of quantum computing and quantum information science. As quantum technologies progress, software engineers must develop a conceptual understanding of quantum mechanics to grasp its implications for computing. This article focuses on fundamental quantum mechanics principles for software engineers, including wave-particle duality, superposition, entanglement, quantum states, and quantum measurement. Unlike traditional physics-oriented discussions, this article focuses on computational perspectives, assisting software professionals in bridging the gap between classical computing and emerging quantum paradigms.
Artificial Intelligence (AI) tools such as GitHub Copilot and ChatGPT are increasingly used in software engineering (SE) for tasks such as code, test, and documentation generation. However, engineers often face uncertainty about when to trust, refine, or discard AI-generated artifacts. We present a pragmatic workflow, complemented by a four-quadrant decision model, that formalizes how developers iteratively prompt, inspect, refine, and, when needed, fall back to manual work. The workflow and decision model were derived from a grey literature review and field observations across three industrial settings in Türkiye and Azerbaijan. Two real-world scenarios demonstrate the workflow's practical value, showing how engineers navigate key decision points when using AI. Our approach offers lightweight, structured guidance to support more deliberate and quality-aware use of AI tools in everyday SE tasks.
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.
Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.
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 f
This systematic literature review aims to investigate the impact of artificial intelligence (AI) on the labour force in software engineering, with a particular focus on the skills needed for future software engineers, the impact of AI on the demand for software engineering skills, and the future of work for software engineers. The review identified 42 relevant publications through a comprehensive search strategy and analysed their findings. The results indicate that future software engineers will need to be competent in programming and have soft skills such as problem-solving and interpersonal communication. AI will have a significant impact on the software engineering workforce, with the potential to automate many jobs currently done by software engineers. The role of a software engineer is changing and will continue to change in the future, with AI-assisted software development posing challenges for the software engineering profession. The review suggests that the software engineering profession must adapt to the changing landscape to remain relevant and effective in the future.
Software engineers working in large projects must navigate complex information landscapes. Change Impact Analysis (CIA) is a task that relies on engineers' successful information seeking in databases storing, e.g., source code, requirements, design descriptions, and test case specifications. Several previous approaches to support information seeking are task-specific, thus understanding engineers' seeking behavior in specific tasks is fundamental. We present an industrial case study on how engineers seek information in CIA, with a particular focus on traceability and development artifacts that are not source code. We show that engineers have different information seeking behavior, and that some do not consider traceability particularly useful when conducting CIA. Furthermore, we observe a tendency for engineers to prefer less rigid types of support rather than formal approaches, i.e., engineers value support that allows flexibility in how to practically conduct CIA. Finally, due to diverse information seeking behavior, we argue that future CIA support should embrace individual preferences to identify change impact by empowering several seeking alternatives, including searching, bro
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
Causal diagrams are logic and graphical tools that depict assumptions about presumed causal relations. Such diagrams have proven effective in tackling a variety of problems in social sciences and epidemiology research yet remain foreign to civil engineers. Unlike the traditional means of examining relationships via multivariable regression, causal diagrams can identify the presence of confounders, colliders, and mediators. Thus, this paper hopes to introduce the big ideas behind causal diagrams (specifically, directed acyclic graphs (DAGs)) and how to create and apply such diagrams to several civil engineering problems. Findings from the presented case studies indicate that civil engineers can successfully use causal diagrams to improve their understanding of complex causation relations, thereby accelerating research and practical efforts.
Reverse engineering is a complex process essential to software-security tasks such as vulnerability discovery and malware analysis. Significant research and engineering effort has gone into developing tools to support reverse engineers. However, little work has been done to understand the way reverse engineers think when analyzing programs, leaving tool developers to make interface design decisions based only on intuition. This paper takes a first step toward a better understanding of reverse engineers' processes, with the goal of producing insights for improving interaction design for reverse engineering tools. We present the results of a semi-structured, observational interview study of reverse engineers (N=16). Each observation investigated the questions reverse engineers ask as they probe a program, how they answer these questions, and the decisions they make throughout the reverse engineering process. From the interview responses, we distill a model of the reverse engineering process, divided into three phases: overview, sub-component scanning, and focused experimentation. Each analysis phase's results feed the next as reverse engineers' mental representations become more conc
Capital goods such as complex medical equipment, trains and manufacturing machinery are essential to their users' business, and thus have stringent up-time requirements. Responsive maintenance is crucial for meeting these requirements, which in turn relies on the timely availability of both spare parts and service engineers. Spare parts management for maintenance is well-studied in the research literature, but managing the service engineers has received relatively little attention. In this paper, we consider a network of geographically distributed capital goods, maintained by a set of service engineers who can respond quickly to machine breakdowns. We are interested in the question which service engineers to dispatch to what breakdowns, and how to relocate these engineers to maintain good coverage. We propose and evaluate a range of scalable dispatching and relocation heuristics inspired by the extensive research literature in the domain of emergency medical services. We compare the proposed heuristics against each other using comprehensive simulation experiments, and benchmark the best combination of dispatching and relocation heuristics against the optimal policy. We find that th
The changes on abiotic features of ecosystems have rarely been taken into account by population dynamics models, which typically focus on trophic and competitive interactions between species. However, understanding the population dynamics of organisms that must modify their habitats in order to survive, the so-called ecosystem engineers, requires the explicit incorporation of abiotic interactions in the models. Here we study a model of ecosystem engineers that is discrete both in space and time, and where the engineers and their habitats are arranged in patches fixed to the sites of regular lattices. The growth of the engineer population is modeled by Ricker equation with a density-dependent carrying capacity that is given by the number of modified habitats. A diffusive dispersal stage ensures that a fraction of the engineers move from their birth patches to neighboring patches. We find that dispersal influences the metapopulation dynamics only in the case that the local or single-patch dynamics exhibits chaotic behavior. In that case, it can suppress the chaotic behavior and avoid extinctions in the regime of large intrinsic growth rate of the population.
The object is to the reasonable selection of the ICT tools for formation of ecological competence. Pressing task is constructive and research approach to preparation of future engineers to performance of professional duties in order to make them capable to develop engineering projects independently and exercise control competently. Subject of research: the theoretical justification of competence system of future mining engineers. Methods: source analysis on the problem of ecological competence formation. Results: defining the structure of environmental competence of future mining engineers. Conclusion: the relevance of the material covered in the article, due to the need to ensure the effectiveness of the educational process in the preparation of the future mining engineers.