Andreas Rausch, Stefan Wittek, Tobias Geger
et al.
Generative AI and agentic tools are reshaping agile software development, yet many engineering curricula still teach agile methods and AI competencies separately and largely lecture-based. This paper presents a project-based AI Engineering curriculum designed to prepare students for AI-driven agile development by integrating agile practices and AI-enabled engineering throughout the program. We contribute (1) the curriculum concept and guiding principles, (2) a case study of interdisciplinary, AI-enabled agile student projects, and (3) early evidence from a mixed-methods evaluation. In our case study, second-semester bachelor students work in teams over seven two-week sprints on a realistic software product. AI tools are embedded into everyday agile engineering tasks - requirements clarification, backlog refinement, architectural reasoning, coding support, testing, and documentation - paired with reflection on human responsibility and quality. Initial results indicate that the integrated approach supports hands-on competence development in AI-assisted engineering. Key observations highlight the need for agile teaching adaptations due to rapid tool evolution, the critical role of oral verification to ensure foundational learning. We close with lessons learned and recommendations for educators designing agile project-based curricula in the age of AI.
Background. As digital technologies increasingly shape social domains such as healthcare, public safety, entertainment, and education, software engineering has engaged with ethical and political concerns primarily through the notion of algorithmic fairness. Aim. This study challenges the limits of software engineering approaches to fairness by analyzing how fairness is conceptualized in the human sciences. Methodology. We conducted two secondary studies, exploring 45 articles on algorithmic fairness in software engineering and 25 articles on fairness from the humanities, and compared their findings to assess cross-disciplinary insights for ethical technological development. Results. The analysis shows that software engineering predominantly defines fairness through formal and statistical notions focused on outcome distribution, whereas the humanities emphasize historically situated perspectives grounded in structural inequalities and power relations, with differences also evident in associated social benefits, proposed practices, and identified challenges. Conclusion. Perspectives from the human sciences can meaningfully contribute to software engineering by promoting situated understandings of fairness that move beyond technical approaches and better account for the societal impacts of technologies.
The emergence of Large Code Models (LCMs) has transformed software engineering (SE) automation, driving significant advancements in tasks such as code generation, source code documentation, code review, and bug fixing. However, these advancements come with trade-offs: achieving high performance often entails exponential computational costs, reduced interpretability, and an increasing dependence on data-intensive models with hundreds of billions of parameters. In this paper, we propose Neurosymbolic Software Engineering, in short NSE, as a promising paradigm combining neural learning with symbolic (rule-based) reasoning, while strategically introducing a controlled source of chaos to simulate the complex dynamics of real-world software systems. This hybrid methodology aims to enhance efficiency, reliability, and transparency in AI-driven software engineering while introducing controlled randomness to adapt to evolving requirements, unpredictable system behaviors, and non-deterministic execution environments. By redefining the core principles of AI-driven software engineering automation, NSE lays the groundwork for solutions that are more adaptable, transparent, and closely aligned with the evolving demands of modern software development practices.
With the rise of large language models (LLMs), researchers are increasingly exploring their applications in var ious vertical domains, such as software engineering. LLMs have achieved remarkable success in areas including code generation and vulnerability detection. However, they also exhibit numerous limitations and shortcomings. LLM-based agents, a novel tech nology with the potential for Artificial General Intelligence (AGI), combine LLMs as the core for decision-making and action-taking, addressing some of the inherent limitations of LLMs such as lack of autonomy and self-improvement. Despite numerous studies and surveys exploring the possibility of using LLMs in software engineering, it lacks a clear distinction between LLMs and LLM based agents. It is still in its early stage for a unified standard and benchmarking to qualify an LLM solution as an LLM-based agent in its domain. In this survey, we broadly investigate the current practice and solutions for LLMs and LLM-based agents for software engineering. In particular we summarise six key topics: requirement engineering, code generation, autonomous decision-making, software design, test generation, and software maintenance. We review and differentiate the work of LLMs and LLM-based agents from these six topics, examining their differences and similarities in tasks, benchmarks, and evaluation metrics. Finally, we discuss the models and benchmarks used, providing a comprehensive analysis of their applications and effectiveness in software engineering. We anticipate this work will shed some lights on pushing the boundaries of LLM-based agents in software engineering for future research.
This paper introduces prompted software engineering (PSE), which integrates prompt engineering to build effective prompts for language-based AI models, to enhance the software development process. PSE enables the use of AI models in software development to produce high-quality software with fewer resources, automating tedious tasks and allowing developers to focus on more innovative aspects. However, effective prompts are necessary to guide software development in generating accurate, relevant, and useful responses, while mitigating risks of misleading outputs. This paper describes how productive prompts should be built throughout the software development cycle.
The rapid advancements in large language models (LLMs) have greatly expanded the potential for automated code-related tasks. Two primary methodologies are used in this domain: prompt engineering and fine-tuning. Prompt engineering involves applying different strategies to query LLMs, like ChatGPT, while fine-tuning further adapts pre-trained models, such as CodeBERT, by training them on task-specific data. Despite the growth in the area, there remains a lack of comprehensive comparative analysis between the approaches for code models. In this paper, we evaluate GPT-4 using three prompt engineering strategies -- basic prompting, in-context learning, and task-specific prompting -- and compare it against 17 fine-tuned models across three code-related tasks: code summarization, generation, and translation. Our results indicate that GPT-4 with prompt engineering does not consistently outperform fine-tuned models. For instance, in code generation, GPT-4 is outperformed by fine-tuned models by 28.3% points on the MBPP dataset. It also shows mixed results for code translation tasks. Additionally, a user study was conducted involving 27 graduate students and 10 industry practitioners. The study revealed that GPT-4 with conversational prompts, incorporating human feedback during interaction, significantly improved performance compared to automated prompting. Participants often provided explicit instructions or added context during these interactions. These findings suggest that GPT-4 with conversational prompting holds significant promise for automated code-related tasks, whereas fully automated prompt engineering without human involvement still requires further investigation.
Sander Thuijsman, Gökhan Kahraman, Alireza Mohamadkhani
et al.
Supervisory control design of cyber-physical systems has many challenges. Model-based systems engineering can address these, with solutions originating from various disciplines. We discuss several tools, each state-of-the-art in its own discipline, offering functionality such as specification, synthesis, and verification. Integrating such mono-disciplinary tools in a multi-disciplinary workflow is a major challenge. We present Analytics as a Service, built on the Arrowhead framework, to connect these tools and make them interoperable. A seamless integration of the tools has been established through a service-oriented architecture: The engineer can easily access the functionality of the tools from a single interface, as translation steps between equivalent models for the respective tools are automated.
Hashini Gunatilake, John Grundy, Ingo Mueller
et al.
Empathy is widely used in many disciplines such as philosophy, sociology, psychology, health care. Ability to empathise with software end-users seems to be a vital skill software developers should possess. This is because engineering successful software systems involves not only interacting effectively with users but also understanding their true needs. Empathy has the potential to address this situation. Empathy is a predominant human aspect that can be used to comprehend decisions, feelings, emotions and actions of users. However, to date empathy has been under-researched in software engineering (SE) context. In this position paper, we present our exploration of key empathy models from different disciplines and our analysis of their adequacy for application in SE. While there is no evidence for empathy models that are readily applicable to SE, we believe these models can be adapted and applied in SE context with the aim of assisting software engineers to increase their empathy for diverse end-user needs. We present a preliminary taxonomy of empathy by carefully considering the most popular empathy models from different disciplines. We encourage future research on empathy in SE as we believe it is an important human aspect that can significantly influence the relationship between developers and end-users.
In this paper, we present ChaosETH, a chaos engineering approach for resilience assessment of Ethereum blockchain clients. ChaosETH operates in the following manner: First, it monitors Ethereum clients to determine their normal behavior. Then, it injects system call invocation errors into one single Ethereum client at a time, and observes the behavior resulting from perturbation. Finally, ChaosETH compares the behavior recorded before, during, and after perturbation to assess the impact of the injected system call invocation errors. The experiments are performed on the two most popular Ethereum client implementations: GoEthereum and Nethermind. We assess the impact of 22 different system call errors on those Ethereum clients with respect to 15 application-level metrics. Our results reveal a broad spectrum of resilience characteristics of Ethereum clients w.r.t. system call invocation errors, ranging from direct crashes to full resilience. The experiments clearly demonstrate the feasibility of applying chaos engineering principles to blockchain systems.
Nuclear structure models built from phenomenological mean fields, the effective nucleon-nucleon interactions (or Lagrangians), and the realistic bare nucleon-nucleon interactions are reviewed. The success of covariant density functional theory (CDFT) to describe nuclear properties and its influence on Brueckner theory within the relativistic framework are focused upon. The challenges and ambiguities of predictions for unstable nuclei without data or for high-density nuclear matter, arising from relativistic density functionals, are discussed. The basic ideas in building an ab initio relativistic density functional for nuclear structure from ab initio calculations with realistic nucleon-nucleon interactions for both nuclear matter and finite nuclei are presented. The current status of fully self-consistent relativistic Brueckner-Hartree-Fock (RBHF) calculations for finite nuclei or neutron drops (ideal systems composed of a finite number of neutrons and confined within an external field) is reviewed. The guidance and perspectives towards an ab initio covariant density functional theory for nuclear structure derived from the RBHF results are provided.
Nowadays, most companies need to collect, store, and manage personal information in order to deliver their services. Accordingly, privacy has emerged as a key concern for these companies since they need to comply with privacy laws and regulations. To deal with them properly, such privacy concerns should be considered since the early phases of system design. Ontologies have proven to be a key factor for elaborating high-quality requirements models. However, most existing work deals with privacy as a special case of security requirements, thereby missing essential traits of this family of requirements. In this paper, we introduce COPri, a Core Ontology for Privacy requirements engineering that adopts and extends our previous work on privacy requirements engineering ontology that has been mined through a systematic literature review. Additionally, we implement, validate and then evaluate our ontology.
Oscar Javier Hernandez, Sonia Bacca, Kyle Andrew Wendt
The discovery of the proton-radius puzzle and the subsequent deuteron-radius puzzle is fueling an on-going debate on possible explanations for the difference in the observed radii obtained from muonic atoms and from electron-nucleus systems. Atomic nuclei have a complex internal structure that must be taken into account when analyzing experimental spectroscopic results. Ab initio nuclear structure theory provided the so far most precise estimates of important corrections to the Lamb shift in muonic atoms and is well poised to also investigate nuclear structure corrections to the hyperfine splitting in muonic atoms. Independently on whether the puzzle is due to beyond-the-standard-model physics or not, nuclear structure corrections are a necessary theoretical input to any experimental extraction of electric and magnetic radii from precise muonic atom measurements. Here, we review the status of the calculations performed by the TRIUMF-Hebrew University group, focusing on the deuteron, and discuss preliminary results on magnetic sum rules calculated with two-body currents at next-to-leading order. Two-body currents will be an important ingredient in future calculations of nuclear structure corrections to the hyperfine splitting in muonic atoms.
In requirements engineering for recommender systems, software engineers must identify the data that drives the recommendations. This is a labor-intensive task, which is error-prone and expensive. One possible solution to this problem is the adoption of automatic recommender system development approach based on a general recommender framework. One step towards the creation of such a framework is to determine the type of data used in recommender systems. In this paper, a systematic review has been conducted to identify the type of user and recommendation data items needed by a general recommender system. A user and item model is proposed, and some considerations about algorithm specific parameters are explained. A further goal is to study the impact of the fields of big data and Internet of things on the development of recommender systems.
We present an estimate of the nuclear electric polarizability of the 6He halo nucleus based on six-body microscopic calculations. Wave functions are obtained from semi-realistic two-body interactions using the hyperspherical harmonics expansion method. The polarizability is calculated as a sum rule of the dipole response function using the Lanczos algorithm and also by integrating the photo-absorption cross section calculated via the Lorentz integral transform method. We obtain alpha_E=1.00(14) fm^3, which is much smaller than the published value 1.99(40) fm^3 extracted from experimental data. This points towards a potential disagreement between microscopic theories and experimental observations.
Interplay between the dependence of symmetry energy on density and the variation of nucleonic densities across nuclear surface is discussed. That interplay gives rise to the mass dependence of the symmetry coefficient in an energy formula. Charge symmetry of the nuclear interactions allows to introduce isoscalar and isovector densities that are approximately independent of the magnitude of neutron-proton asymmetry.
We show that the nuclear supersymmetry model (n-susy) in its extended version, predicts correlations in the nuclear structure matrix elements which characterize transfer reactions between nuclei that belong to the same supermultiplet. These correlations are related to the fermionic generators of the superalgebra and if verified experimentally can provide a direct test of the model.
We have built an effective Walecka-type hadronic Lagrangian in which the hadron masses and the density dependence of the coupling constants are deduced from the quark dynamics using a Nambu-Jona-Lasinio model. In order to stabilize nuclear matter an eight-quark term has been included. The parameters of this Nambu-Jona-Lasinio model have been determined using the meson properties in the vacuum but also in the medium through the omega meson mass in nuclei measured by the TAPS collaboration. Realistic properties of nuclear matter have been obtained.
We study the thermodynamics of asymmetric nuclear matter using a mean field approximation with a Skyrme effective interaction, in order to establish its phase diagram and more particularly the influence of isospin on the order of the transition. A new statistical method is introduced to study the thermodynamics of a multifluid system, keeping only one density fixed the others being replaced by their intensive conjugated variables. In this ensemble phase coexistence reduces to a simple one dimensional Maxwell construction. For a fixed temperature under a critical value, a coexistence line is obtained in the plane of neutron and proton chemical potentials. Along this line the grand potential presents a discontinuous slope showing that the transition is first order except at the two ending points where it becomes second order. This result is not in contradiction with the already reported occurrence of a continuous transformation when a constant proton fraction is imposed. Indeed, the proton fraction being an order parameter in asymmetric matter, the constraint can only be fulfilled by gradual phase mixing along the first-order phase transition line leading to a continuous pressure.