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UI design is an integral part of software development. For many developerswho do not have much UI design experience, exposing them to a large database ofreal-application UI designs can help them quickly build up a realisticunderstanding of the design space for a software feature and get designinspirations from existing applications. However, existing keyword-based,image-similarity-based, and component-matching-based methods cannot reliablyfind relevant high-fidelity UI designs in a large database alike to the UIwireframe that the developers sketch, in face of the great variations in UIdesigns. In this article, we propose a deep-learning-based UI design searchengine to fill in the gap. The key innovation of our search engine is to traina wireframe image autoencoder using a large database of real-application UIdesigns, without the need for labeling relevant UI designs. We implement ourapproach for Android UI design search, and conduct extensive experiments withartificially created relevant UI designs and human evaluation of UI designsearch results. Our experiments confirm the superior performance of our searchengine over existing image-similarity or component-matching-based methods anddemonstrate the usefulness of our search engine in real-world UI design tasks.

Wireframe-Based UI Design Search Through Image Autoencoder