{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101246"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101246","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Mobile design semantics","abstract":"Given the growing number of mobile apps and their increasing impact on modern life, researchers have developed black-box approaches to mine mobile app design and interaction data. Although the data captured during interaction mining is descriptive, it does not expose the design semantics of UIs: what elements on the screen mean and how they are used. This thesis introduces an automatic approach for semantically annotating the elements comprising a UI given the data captured during interaction mining. Through an iterative open coding of 73k UI elements and 720 screens, we first created a lexical database of 24 types of UI components, 197 text button concepts, and 135 icon classes shared across apps. Using the labeled data created during this process, we learned code-based patterns to detect components, and trained a convolutional neural network which distinguishes between 99 icon classes with 94% accuracy. With this automated approach, we computed semantic annotations for the 72k unique UIs comprising the Rico dataset, assigning labels for 78% of the total visible, non-redundant elements.","abstract_html":"Given the growing number of mobile apps and their increasing impact on modern life, researchers have developed black-box approaches to mine mobile app design and interaction data. Although the data captured during interaction mining is descriptive, it does not expose the design semantics of UIs: what elements on the screen mean and how they are used. This thesis introduces an automatic approach for semantically annotating the elements comprising a UI given the data captured during interaction mining. Through an iterative open coding of 73k UI elements and 720 screens, we first created a lexical database of 24 types of UI components, 197 text button concepts, and 135 icon classes shared across apps. Using the labeled data created during this process, we learned code-based patterns to detect components, and trained a convolutional neural network which distinguishes between 99 icon classes with 94% accuracy. With this automated approach, we computed semantic annotations for the 72k unique UIs comprising the Rico dataset, assigning labels for 78% of the total visible, non-redundant elements.","abstract_has_math":false,"creators":["Liu, Thomas F"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Kumar, Ranjitha"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:42:02Z","date_published":"2018-09-04T20:42:02Z","updated_at":"2026-07-22T22:24:38Z","subjects":["design, mobile"],"languages":["en"],"rights":["Copyright 2018 Thomas Liu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101246","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kumar, Ranjitha"]},{"key":"dc:creator","label":"Author","values":["Liu, Thomas F"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:42:02Z","2020-09-05T09:15:23Z","2018-04-27","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["design, mobile"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Thomas Liu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101246"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Given the growing number of mobile apps and their increasing impact on modern life, researchers have developed black-box approaches to mine mobile app design and interaction data. 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Although the data captured during interaction mining is descriptive, it does not expose the design semantics of UIs: what elements on the screen mean and how they are used. This thesis introduces an automatic approach for semantically annotating the elements comprising a UI given the data captured during interaction mining. Through an iterative open coding of 73k UI elements and 720 screens, we first created a lexical database of 24 types of UI components, 197 text button concepts, and 135 icon classes shared across apps. Using the labeled data created during this process, we learned code-based patterns to detect components, and trained a convolutional neural network which distinguishes between 99 icon classes with 94% accuracy. 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