arXiv:2606. 16842v1 Announce Type: cross Abstract: Teaching Software Engineering for AI-enabled systems entails addressing the integration of AI components within full-scale software architectures under realistic constraints.
By Amir Mashmool, Kishan Ravindra Sawant, Mojtaba Shahin, Nico Hochgeschwender, Rainer Koschke
arXiv:2409. 10897v3 Announce Type: replace Abstract: The increasing adoption of neural networks in learning-augmented systems highlights the growing need for model safety and robustness, especially in safety-critical domains.
By Shuowei Jin, Taobo Liao, Anuj Kalia, Xenofon Foukas, Huan Zhang, Cheng Tan, Z. Morley Mao, Francis Y. Yan
arXiv:2606. 08212v1 Announce Type: new Abstract: Solving machine learning problems is complex and typically reserved for experts.
By Lokman Saleh, Hafedh Mili, Mounir Boukadoum
arXiv:2604. 22207v2 Announce Type: replace-cross Abstract: Due to the textual and repetitive nature of many Requirements Engineering (RE) artefacts, Large Language Models (LLMs) have proven useful to automate their generation and processing.
By Anna Arnaudo, Riccardo Coppola, Maurizio Morisio, Flavio Giobergia, Andrea Bioddo, Angelo Bongiorno, Luca Dadone
arXiv:2606. 27960v1 Announce Type: cross Abstract: Software engineering is an intellectually demanding, creative discipline that juggles a web of interdependent tasks to design, build, and assure the quality of increasingly complex systems.
By Roberto Pietrantuono, Luca Giamattei, Stefano Russo
Context: Large Language Models (LLMs) offer natural-language flexibility for automated requirements elicitation but frequently generate structurally invalid requirements and logical inconsistencies, lacking formal correctness guarantees. Objectives: This study aims to eliminate logical inconsistencies and enforce structural conformance in LLM-generated requirements while quantifying the LLM's pre-validation decision uncertainty within a formal domain model.