An Introduction to Software Ecosystems

This chapter defines and presents different kinds of software ecosystems. The focus is on the development, tooling and analytics aspects of software ecosystems, i.e., communities of software developers and the interconnected software components (e.g., projects, libraries, packages, repositories, plug-ins, apps) they are developing and maintaining. The technical and social dependencies between these developers and software components form a socio-technical dependency network, and the dynamics of this network change over time. We classify and provide several examples of such ecosystems. The chapter also introduces and clarifies the relevant terms needed to understand and analyse these ecosystems, as well as the techniques and research methods that can be used to analyse different aspects of these ecosystems.Comment: Preprint of chapter "An Introduction to Software Ecosystems" by Tom Mens and Coen De Roover, published in the book "Software Ecosystems: Tooling and Analytics" (eds. T. Mens, C. De Roover, A. Cleve), 2023, ISBN 978-3-031-36059-6, reproduced with permission of Springer. The final authenticated version of the book and this chapter is available online at: https://doi.org/10.1007/978-3-031-36060-

Single-Case Pilot Study For Longitudinal Analysis Of Referential Failures And Sentiment In Schizophrenic Speech From Client-Centered Psychotherapy Recordings

Though computational linguistic analyses have revealed the presence of distinctly characteristic language features in schizophrenic disordered speech, the relative stability of these language features in longitudinal samples is still unknown. This longitudinal pilot study analyzed schizophrenic disordered speech data from the archival therapy audio recordings of one patient spanning 23 years. End-to-end Neural Coreference Resolution software was used to analyze transcribed speech data from three therapy sessions to identify ambiguous pronouns, referred to as referential failures, which were reviewed and confirmed by multiple raters. Speech samples were analyzed using Google Cloud Natural Language API software for sentiment variables (i.e., score, valence, and magnitude). Referential failures and sentiment variables were analyzed within each session and all sessions combined to study the relationships between these variables within single sessions and over a span of 23 years. Results and implications for this study are discussed

Spartan Daily, March 27, 1957

Volume 44, Issue 99https://scholarworks.sjsu.edu/spartandaily/12455/thumbnail.jp