Web-based collaboration and virtual environments supported by various Web 2.0 concepts enable the application of numerous monitoring, mining and analysis tools to study human interactions and team formation processes. The composition of an effective team requires a balance between adequate skill fulfillment and sufficient team connectivity. The underlying interaction structure reflects social behavior and relations of individuals and determines to a large degree how well people can be expected to collaborate. In this paper we address an extended team formation problem that does not only require direct interactions to determine team connectivity but additionally uses implicit recommendations of collaboration partners to support even sparsely connected networks. We provide two heuristics based on Genetic Algorithms and Simulated Annealing for discovering efficient team configurations that yield the best trade-off between skill coverage and team connectivity. Our self-adjusting mechanism aims to discover the best combination of direct interactions and recommendations when deriving connectivity. We evaluate our approach based on multiple configurations of a simulated collaboration network that features close resemblance to real world expert networks. We demonstrate that our algorithm successfully identifies efficient team configurations even when removing up to 40% of experts from various social network configurations.

Refining high-level requirements into low-level ones is a common task, especially in safety-critical systems engineering. The objective is to describe every important aspect of the high-level requirement in a low-level requirement, ensuring a complete and correct implementation of the system's features. To this end, standards and regulations for safety-critical systems require reviewing the coverage of high-level requirements by all its low-level requirements to ensure no missing aspects. The challenge of supporting automatic reviews for requirements coverage originates from the distinct levels of abstraction between high-level and low-level requirements, their reliance on natural language, and the often different vocabulary used. The rise of Large Language Models (LLMs), trained on extensive text corpora and capable of contextualizing both high-level and low-level requirements, opens new avenues for addressing this challenge. This paper presents an initial study to explore the performance of LLMs in assessing requirements coverage. We employed GPT-3.5 and GPT-4 to analyze requirements from five publicly accessible data sets, determining their ability to detect if low-level requirements sufficiently address the corresponding high-level requirement. Our findings reveal that GPT-3.5, utilizing a zero-shot prompting strategy augmented with the prompt of explaining, correctly identifies complete coverage in four out of five evaluation data sets. Additionally, it exhibits an impressive 99.7% recall rate in accurately identifying instances where coverage is incomplete due to removing a single low-level requirement across our entire set of evaluation data.

Participants in current team collaborations belong to different organizations, work on multiple objectives at the same time, and frequently change locations. They use different devices and infrastructures in collaboration processes that can last from a few hours to several years. All these factors pose new challenges to the development of collaborative working environments (CWEs). Existing CWEs are unable to support emerging teams because diverse collaboration services are not well integrated or adapting to the team context. We present the inContext approach to providing a novel pervasive CWE infrastructure for emerging team forms. inContext aggregates disparate collaboration services using Web services and Semantic Web technologies and provides a platform that captures diverse dynamic aspects of team collaborations. By utilizing runtime and historical context and interaction information, adaptation techniques can be deployed to cope with the changes of emerging teams.