Browsing by Author "Wibergh, Karin"

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    Automatic refactoring for Agda
    (2019-04-24) Wibergh, Karin; Göteborgs universitet/Institutionen för data- och informationsteknik; University of Gothenburg/Department of Computer Science and Engineering
    The task of making changes to an existing code base to improve performance, legibility, or extensibility while preserving behaviour is important to virtually any program. Many times this involves making changes requiring a great deal of typing in various places, which is tedious and error-prone. Consequently, programs known as refactoring engines are used to take over the predictable parts of this task. However, although common for imperative and object-oriented languages, refactoring engines for functional languages like Haskell are rare and those for dependently typed languages are nonexistent. This project lays the groundwork for a refactoring engine for Agda by describing useful refactorings and a handful of implementation strategies.
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    TENSE IN JAPANESE THANKS. A comparison between the 1970s and 2021
    (2021-06-21) Wibergh, Karin; University of Gothenburg/Department of Languages and Literatures; Göteborgs universitet/Institutionen för språk och litteraturer
    In this study, we compare how the two expressions arigatou gozaimasu (present tense) and arigatou gozaimashita (past tense) are used during sessions of the Diet of Japan in the 1970s and in 2021. We find that usage, apart from having generally increased, has shifted from predominantly past tense to mainly present tense. This shift is mostly resisted in the case where an expression of thanks is accompanied by an explicitly voiced desire to end a conversation or a topic. We believe that this is caused by Japanese people understanding the use of the past tense as a form of ending marker. In all other situations, we observe a substantial influence of personal preference on the choice of tense, which may or may not be caused by demographic factors. In consequence, we urge that future research use sample sizes large enough to avoid spurious results caused by an unfortunate selection of samples.