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Automated Retrieval of Graphical User Interface Prototypes from Natural Language Requirements
Kolthoff, Kristian
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Bartelt, Christian
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Ponzetto, Simone Paolo
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DOI:
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https://doi.org/10.1007/978-3-030-80599-9_33
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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2021
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Buchtitel:
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NLDB 2021 : 26th International Conference on Natural Language & Information Systems, Saarbrücken, Germany, June 23–25, 2021 ; proceedings
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Titel einer Zeitschrift oder einer Reihe:
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Lecture Notes in Computer Science
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Band/Volume:
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12801
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Seitenbereich:
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376-384
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Veranstaltungstitel:
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NLDB 2021
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Veranstaltungsort:
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Online
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Veranstaltungsdatum:
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23.-25.06.2021
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Ort der Veröffentlichung:
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Berlin [u.a.]
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Verlag:
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Springer
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ISBN:
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978-3-030-80598-2 , 978-3-030-80599-9
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ISSN:
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0302-9743 , 1611-3349
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
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Fachgebiet:
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004 Informatik
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Abstract:
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High-fidelity Graphical User Interface (GUI) prototyping represents a suitable approach for allowing to clarify and refine requirements elicitated from customers. In particular, GUI prototypes can facilitate to mitigate and reduce misunderstandings between customers and developers, which may occur due to the ambiguity and vagueness of informal Natural Language (NL). However, employing high-fidelity GUI prototypes is more time-consuming and expensive compared to other simpler GUI prototyping methods. In this work, we propose a system that automatically processes Natural Language Requirements (NLR) and retrieves fitting GUI prototypes from a semi-automatically created large-scale GUI repository for mobile applications. We extract several text segments from the GUI hierarchy data to obtain textual representations for the GUIs. To achieve ad-hoc GUI retrieval from NLR, we adopt multiple Information Retrieval (IR) approaches and Automatic Query Expansion (AQE) techniques. We provide an extensive and systematic evaluation of the applied IR and AQE approaches for their effectiveness in terms of GUI retrieval relevance on a manually annotated dataset of NLR in the form of search queries and User Stories (US). We found that our GUI retrieval performs well in the conducted experiments and discuss the results.
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