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Healthcare providers generally spend excessive time on administrative tasks at the expense of direct patient care. The emergence of new artificial intelligence and natural language processing technologies gives rise to innovations that could relieve them of this burden. In this paper, we present a pipeline structure for building dialogue summarization systems. Our pipeline summarizes a consultation of a patient with a care provider and automatically generates a report compliant with medical formats. Four pipeline components are used to generate a report based on audio input. The outputs of each component are analyzed to determine the most important challenges and issues. The current proof-of-concept, which was applied to eight doctor-to-patient sessions concerning ear infection, shows that automatic dialogue summarization and reporting is achievable, but requires improvements to increase completeness.

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Context & Motivation]Requirements and architectural components are designed concurrently, with the former guiding the latter, and the latter restricting the former. [Question/problem] Effective communication between requirements engineers and software architects is often experienced as problematic. [Principal ideas/results] We present the Requirements Engineering for Software Architecture (RE4SA) model with the intention to support the communication within the development team. In RE4SA, requirements are expressed as epic stories and user stories, which are linked to modules and features, respectively, as their architectural counterparts. Additionally, we provide metrics to measure the alignment between these concepts, and we also discuss how to use the model and the usefulness of the metrics by applying both to case studies. [Contribution] The RE4SA model employs widely adopted notations and allows for explicitly relating a system's requirements and architectural components, while the metrics make it possible to measure the alignment between requirements and architecture.

There is a lack of empirical evidence on software ecosystem health metrics, and a need for operationalizable metrics that describe software ecosystem characteristics. This study unveils a new approach for measuring technical variety concisely. Studies show that a high variety opens up new opportunities and thus, better niche creation, and ultimately, improves software ecosystem health. Four different ecosystems are evaluated, and compared. Variety is measured in relation to robustness, and productivity metrics of the ecosystem to uncover the influence of technical variety on software ecosystems. Technical variety indicates a positive correlation with robustness, however acceptance of this statement is not confirmed with certainty due to a weak relation. Furthermore, significant relations indicate differences between ecosystem types. CCS CONCEPTS• Software and its engineering → Software system structures.