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Auflistung nach Autor:in "Chazette, Larissa"

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  • Konferenzbeitrag
    Designing Software Transparency: A Multidisciplinary Endeavor
    (Softwaretechnik-Trends Band 40, Heft 1, 2020) Chazette, Larissa; Busch, Melanie; Schrapel, Maximilian; Korte, Kai; Schneider, Kurt
    Software systems support end-users in making daily decisions, thanks to ubiquituous computing. As long as the system behaves in accordance to users’ expectations, the user continues to trust the system. In the case of unexpected software behavior, there is often a lack of transparency and comprehensibility, since the user cannot understand the reason for the resulting behavior. An extensive variety of requirements can be involved when we consider software transparency. These requirements are, most of the times, interrelated in a complex network, influencing each other. In this paper, we explore the opportunities of a multidisciplinary work and the possible challenges in achieving software transparency. We use a navigation scenario to explore the possibilities in terms of a system. The expected result of this joint dialogue is to identify challenges and constraints in the requirements operationalization, in the requirements process itself, and discuss adequate ways to overcome them.
  • Konferenzbeitrag
    On the Subjectivity of Emotions in Software Projects: How Reliable are Pre-Labeled Data Sets for Sentiment Analysis? (Summary)
    (Software Engineering 2023, 2023) Herrmann, Marc; Obaidi, Martin; Chazette, Larissa; Klünder, Jil
    Social aspects (e.g., the sentiment of developers) are important for software development. In order to automatically analyze sentiments, sentiment analysis tools use machine learning methods that require data sets labeled according to emotion or polarity. As these labeled data sets strongly influence the tools’ accuracy, we investigate whether the labels match developers’ perceptions. For this purpose, we conducted an international survey with 94 participants who labeled 100 statements. We compare the median as well as every single participant’s perception with the labels. The results show that the median perception of all participants coincides with the predefined labels for 62.5% of the statements, and that the difference between the single participant’s ratings and the labels is even worse. This summary refers to the paper with the title “On the subjectivity of emotions in software projects: How reliable are pre-labeled data sets for sentiment analysis?” [He22b]. It was published in the Journal of Systems and Software (JSS) in 2022 peer-reviewed.
  • Konferenzbeitrag
    Requirements Engineering für Erklärbare Systeme
    (Ausgezeichnete Informatikdissertationen 2022 (Band D23), 2023) Chazette, Larissa
    Digitale Systeme berühren fast alle Bereiche des alltäglichen Lebens, daher gewinnt die Qualität der Interaktion zwischen Menschen und Systemen immer stärker an Bedeutung. Erklärbarkeit bezeichnet die Fähigkeit eines Systems, Informationen zu geben, um einen bestimmten Aspekt eines Systems in einem bestimmten Kontext verständlich zu kommunizieren. Erklärbarkeit ist somit zu einer wichtigen Qualitätsanforderung geworden. Um Systeme zu entwickeln, müssen Softwareingenieure wissen, wie sie abstrakte Qualitätsziele in konkrete, reale Lösungen umsetzen können. Das Requirements-Engineering bietet hier einen strukturierten Ansatz, Qualitätsanforderungen besser zu verstehen und zu operationalisieren. Aktuell gibt es keine theoretische Basis und Empfehlungen für das Requirements Engineering, für den Entwurf von erklärbaren Systemen. Um diese Lücken zu schließen, schafft diese Dissertation zunächst die theoretische Basis und schlägt aufbauend darauf Maßnahmen vor.