Neural circuits undergo developmental processes which can be influenced by experience. Here we explore a bio-inspired development process to form the connections in a network used for locality sensitive hashing. The network is a simplified model of the insect mushroom body, which has sparse connections from the input layer to a second layer of higher dimension, forming a sparse code. In previous versions of this model, connectivity between the layers is random. We investigate whether the performance of the hash, evaluated in nearest neighbour query tasks, can be improved by process of developing the connections, in which the strongest input dimensions in successive samples are wired to each successive coding dimension. Experiments show that the accuracy of searching for nearest neighbours is improved, although performance is dependent on the parameter values and datasets used. Our approach is also much faster than alternative methods that have been proposed for training the connections in this model. Importantly, the development process does not impact connections built at an earlier stage, which should provide stable coding results for simultaneous learning in a downstream network.

Migrating a set of software variants into a software product line (SPL) is an expensive and potentially challenging endeavor. Indeed, SPL engineering can significantly impact a company's development process and often requires changes to established developer practices. The work presented in this paper stems from a collaboration with a Small and Medium-sized Enterprise (SME) that decided to migrate its existing code base into an SPL. In this study, we conducted an in-depth evaluation of the company's current development processes and practices, as well as the anticipated benefits and risks associated with the migration. Key stakeholders involved in software development participated in this evaluation to provide insight into their perceptions of the migration and their potential resistance to change. This paper describes the design of the interviews conducted with these stakeholders and presents an analysis of the results. Among the qualitative findings, we observed that all participants, regardless of their role in the development process, identified benefits of the migration relevant to their own activities. Furthermore, our results suggest that an effective risk mitigation strategy involves keeping stakeholders informed and engaged throughout the process, preserving as many good practices as possible, and actively involving them in the migration to ensure a smooth transition and minimize potential challenges.

Generative AI (GenAI) has recently emerged as a groundbreaking force in Software Engineering, capable of generating code, identifying bugs, recommending fixes, and supporting quality assurance. While its use in coding tasks shows considerable promise, applying GenAI across the entire Software Development Life Cycle (SDLC) has not yet been fully explored. Critical uncertainties in areas such as reliability, accountability, security, and data privacy demand deeper investigation and coordinated action. The GENIUS project, comprising over 30 European industrial and academic partners, aims to address these challenges by advancing AI integration across all SDLC phases. It focuses on GenAI's potential, the development of innovative tools, and emerging research challenges, actively shaping the future of software engineering. This vision paper presents a shared perspective on the future of GenAI-driven software engineering, grounded in cross-sector dialogue as well as experiences and findings within the GENIUS consortium. The paper explores four central elements: (1) a structured overview of current challenges in GenAI adoption across the SDLC; (2) a forward-looking vision outlining key technological and methodological advances expected over the next five years; (3) anticipated shifts in the roles and required skill sets of software professionals; and (4) the contribution of GENIUS in realising this transformation through practical tools and industrial validation. This paper focuses on aligning technical innovation with business relevance. It aims to inform both research agendas and industrial strategies, providing a foundation for reliable, scalable, and industry-ready GenAI solutions for software engineering teams.

The operation of humanoid robotics is an essential field of research with many practical and competitive applications. Many of these systems, however, do not invest heavily in developing a non-expert-centered graphical user interface (GUI) for operation. The focus of this research is to develop a scalable GUI that is tailored to be simple and intuitive so non-expert operators can control the robot through a FIRA-regulated obstacle course. Using common practices from user interface development (UI) and understanding concepts described in human-robot interaction (HRI) and other related concepts, we will develop a new interface with the goal of a non-expert teleoperation system.

A BSTRACT In this work, we present a benchmark that consists of Jupyter notebooks development trajectories and allows measuring how large language models (LLMs) can leverage runtime information for predicting code output and code generation. We demonstrate that the current generation of LLMs performs poorly on these tasks and argue that there exists a significantly understudied domain in the development of code-based models, which involves incorporating the runtime context. 1 I NTRODUCTION Recent developments in code completion and generation have been significant. Over the past several years, the field has progressed from generating relatively simple programs (Chen et al., 2021) to solving real-world issues within software repositories (Jimenez et al., 2023). However, most studies in this area are based on static snapshots of code (Jiang et al., 2024), with only a small body of research exploring the potential of leveraging dynamic code properties, such as runtime information and memory state, for code generation (Chen et al., 2024). A key reason for this limitation is that common programming environments rarely allow code generation during execution, which is when runtime information can be gathered.

In modern product development, Computer Aided Design and Engineering (CAD/E) plays a key role to turn innovative ideas and visions into tangible and manufacturable designs. Digital 2D and 3D geometry representations of objects on different levels of granularity are required in various intermediate development steps, for example aesthetic discussions, design quality evaluations based on simulation tools, and design feasibility checks. For these steps, development teams include various roles such as requirement engineers, style designers, Computer-Aided Design (CAD) experts, simulation domain experts, and quality assurance teams who create a product cooperatively. Stakeholders in these roles utilize software tools to implement digital representations of products, also referred to as digital twins. This process receives an increasing amount of support in the form of Artificial Intelligence (AI) methods. For example, data science methods provide efficient ways to improve the problem understanding, e.g., by calculating design sensitivities towards a certain performance aspect [Gräning and Sendhoff, 2014], or displaying the distribution of design variations in the solution space using clustering [Lanfermann et al., 2020].

Policymakers are increasingly using development cost and compute as proxies for AI model capabilities and risks. Recent laws have introduced regulatory requirements that are contingent on specific thresholds. However, technical ambiguities in how to perform this accounting could create loopholes that undermine regulatory effectiveness. This paper proposes seven principles for designing practical AI cost and compute accounting standards that (1) reduce opportunities for strategic gaming, (2) avoid disincentivizing responsible risk mitigation, and (3) enable consistent implementation across companies and jurisdictions.

GitHub Copilot is transforming software development by automating tasks and boosting productivity through AI-driven code generation. In this paper, we con-duct a literature survey to synthesize insights on Copilot's impact on productivity and security. We review academic journal databases, industry reports, and official docu-mentation to highlight key findings and challenges. While Copilot accelerates coding and prototyping, concerns over security vulnerabilities and intellectual property risks persist. Drawing from the literature, we provide a perspective on best practices and future directions for responsible AI adoption in software engineering, offering action-able insights for developers and organizations to integrate Copilot effectively while maintaining high standards of quality and security.

Rens Dimmendaal & Banjong Raksaphakdee / Better Images of AI / Medicines (flipped) / Licenced by CC-BY 4.0 The potential of using artificial intelligence in drug discovery and development has sparked both excitement and skepticism among scientists, investors and the general public. "Artificial intelligence is taking over drug development," claim some companies and researchers. Over the past few years, interest in using AI to design drugs and optimize clinical trials has driven a surge in research and investment. AI-driven platforms like AlphaFold, which won the 2024 Nobel Prize for its ability to predict the structure of proteins and design new ones, showcase AI's potential to accelerate drug development. AI in drug discovery is "nonsense," warn some industry veterans. They urge that "AI's potential to accelerate drug discovery needs a reality check," as AI-generated drugs have yet to demonstrate an ability to address the 90% failure rate of new drugs in clinical trials.

Neural circuits undergo developmental processes which can be influenced by experience. Here we explore a bio-inspired development process to form the connections in a network used for locality sensitive hashing. The network is a simplified model of the insect mushroom body, which has sparse connections from the input layer to a second layer of higher dimension, forming a sparse code. In previous versions of this model, connectivity between the layers is random. We investigate whether the performance of the hash, evaluated in nearest neighbour query tasks, can be improved by process of developing the connections, in which the strongest input dimensions in successive samples are wired to each successive coding dimension.