Ottun, Abdul-RasheedAbdul-RasheedOttunAsadi, MehrdadMehrdadAsadiBoerger, MichellMichellBoergerTcholtchev, Nikolay VassilevNikolay VassilevTcholtchevGonçalves, JoãoJoãoGonçalvesBorovčanin, DuşanDuşanBorovčaninSiniarsk, BartlomiejBartlomiejSiniarskFlores, HuberHuberFlores2024-01-312024-01-312023https://publica.fraunhofer.de/handle/publica/45953010.1109/BigData59044.2023.10386926Modern system architectures are rapidly adopting AI-based functionality. As a result, new requirements about software trustworthiness must be considered during the entire software development life cycle of applications. While several requirement management tools are available to track and monitor requirements over time, it is still unknown to what extent these tools can cope with these new demands imposed by AI. In this paper, we contribute by performing a qualitative and quantitative analysis of different requirement management tools and their performance in managing AI-related requirements effectively. Through a rigorous analysis performed by a consortium formed by different industry and academic partners, we evaluate the suitability of five different requirement management tools. Our results indicate that while several tools are available for managing requirements, it is currently challenging to find a tool that can manage AI requirements mainly because tools do not comply with the required aspects imposed by regulatory entities. Lastly, we also shared our lessons learned and experiences from selecting requirement tools that can be used in team-based consortium projects.enRequirement EngineeringXAIAI ApplicationsTrustworthy AIRequirement Engineeringconference paper