Oct 2026· Lecture notes in business information processing
Software Engineering Techniques and Practices
Abstract
Abstract Context: The active involvement of users and customers in agile software development remains a persistent challenge in practice. For this reason, it is important that students in higher education become familiar with good practices in Agile Requirements Engineering during their studies. Objective: Our objective is to support students in learning how to perform requirements analysis by applying agile practices within a stakeholder simulation using AI Personas. At the same time, the approach fosters a critical understanding of the limitations and ethical considerations associated with the use of Generative Artificial Intelligence (GenAI) in this context. Method: In our courses, we employ a stakeholder simulation using GenAI, in which students conduct interviews with AI Personas through a provided meta-prompt. Based on the outcomes of these interviews, students apply agile practices (e.g., story mapping or impact mapping) to document requirements. The use of GenAI is subsequently reflected upon in a structured group discussion. Results: Through this approach, students gain practical experience by applying state-of-the art agile practices for requirements elicitation and documentation while simultaneously developing an understanding of the technical and ethical limitations associated with the use of generative AI. Conclusion: We have applied this approach over several terms and found that using a meta-prompt provides flexibility, allowing us to remain independent of specific large language model providers.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9