Related papers: The Software Engineering Simulations Lab: Agentic …
Context. Nowadays there is a great deal of uncertainty surrounding the effects of experience on Requirements Engineering (RE). There is a widespread idea that experience improves analyst performance. However, there are empirical studies…
In recent years, agentic artificial intelligence (AI) systems are becoming increasingly widespread. These systems allow agents to use various tools, such as web browsers, compilers, and more. However, despite their popularity, agentic AI…
Computer-use agents have rapidly improved on real-world tasks such as web navigation, desktop automation, and software interaction, in some cases surpassing human performance. Yet even when the task and model are unchanged, an agent that…
Context: Requirements engineering (RE) is an important part of Software Engineering (SE), consisting of various human-centric activities that require the frequent collaboration of a variety of roles. Prior research has shown that…
Incorporating responsible practices into software engineering (SE) for AI is essential to ensure ethical principles, societal impact, and accountability remain at the forefront of AI system design and deployment. This study investigates the…
User eXperience (UX) is becoming increasingly important for success of software products. Yet, many companies still face various challenges in their work with UX. Part of these challenges relate to inadequate knowledge and awareness of UX…
Autonomous multi-agent AI systems are poised to transform various industries, particularly software development and knowledge work. Understanding current perceptions among professionals is crucial for anticipating adoption challenges,…
Recommender systems are the cornerstone of today's information dissemination, yet a disconnect between offline metrics and online performance greatly hinders their development. Addressing this challenge, we envision a recommendation…
In this paper, we introduce the concept of the research practice gap as it is perceived in the field of software requirements engineering. An analysis of this gap has shown that two key causes for the research-practice gap are lack of…
As AI becomes more "agentic," it faces technical and socio-legal issues it must address if it is to fulfill its promise of increased economic productivity and efficiency. This paper uses technical and legal perspectives to explain how…
LLM-based agents are rapidly being adopted for scientific data analysis, automating tasks once limited by human time and expertise. This capability is often framed as an acceleration of discovery, but it also accelerates a familiar failure…
Context: Requirements engineering process improvement (REPI) approaches have gained much attention in research and practice. Goal: So far, there is no comprehensive view on the research in REPI in terms of solutions and current state of…
AI agents are increasingly used to diagnose and mitigate failures in production systems, known as agentic Site Reliability Engineering (SRE). Current SRE benchmarks are limited to oversimplistic SRE tasks and are unfortunately hard to…
Evaluation is no longer a final checkpoint in the machine learning lifecycle. As AI systems evolve from static models to compound, tool-using agents, evaluation becomes a core control function. The question is no longer "How good is the…
Quality assurance for large-scale cyber-physical systems relies on sophisticated test activities using complex test environments investigated with the help of numerous types of simulators. As these systems grow, extensive resources are…
Designing and evaluating personalized and proactive assistant agents remains challenging due to the time, cost, and ethical concerns associated with human-in-the-loop experimentation. Existing Human-Computer Interaction (HCI) methods often…
Continuing advances in frontier model research are paving the way for widespread deployment of AI agents. Meanwhile, global interest in building large, complex systems in software, manufacturing, energy and logistics has never been greater.…
The implementation of agentic AI systems has the potential of providing more helpful AI systems in a variety of applications. These systems work autonomously towards a defined goal with reduced external control. Despite their potential, one…
LLM-based agent systems are emerging as a new software paradigm and have been widely adopted across diverse domains such as medicine, robotics, and programming. However, maintaining these systems requires substantial effort, as they are…
The rapid advancement of Generative AI has catalyzed the emergence of autonomous AI agents, presenting unprecedented challenges for enterprise computing infrastructures. Current enterprise API architectures are predominantly designed for…