Learning GUI Completions with User-defined Constraints

Lukas Brückner, Luis A. Leiva, Antti Oulasvirta

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

45 Lataukset (Pure)

Abstrakti

A key objective in the design of graphical user interfaces (GUIs) is to ensure consistency across screens of the same product. However, designing a compliant layout is time-consuming and can distract designers from creative thinking. This paper studies layout recommendation methods that fulfill such consistency requirements using machine learning. Given a desired element type and size, the methods suggest element placements following real-world GUI design processes. Consistency requirements are given implicitly through previous layouts from which patterns are to be learned, comparable to existing screens of a software product. We adopt two recently proposed methods for this task, a Graph Neural Network (GNN) and a Transformer model, and compare them with a custom approach based on sequence alignment and nearest neighbor search (kNN). The methods were tested on handcrafted datasets with explicit layout patterns, as well as large-scale public datasets of diverse mobile design layouts. Our results show that our instance-based learning algorithm outperforms both neural network approaches. Ultimately, this work contributes to establishing smarter design tools for professional designers with explainable algorithms that increase their efficacy.

AlkuperäiskieliEnglanti
Artikkeli6
Sivut1-40
Sivumäärä40
JulkaisuACM Transactions on Interactive Intelligent Systems
Vuosikerta12
Numero1
DOI - pysyväislinkit
TilaJulkaistu - maalisk. 2022
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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