{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/55273"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/55273","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Automated Cross-Platform Code Synthesis from Web-Based Programming Resources","abstract":"For maximal market penetration, popular mobile applications are typically supported on all major platforms, including Android and iOS. Despite the vast differences in the look-and-feel of major mobile platforms, applications running on these platforms in essence provide the same core functionality. As an application is maintained and evolved, programmers need to replicate the resulting changes on all the supported platforms, a tedious and error-prone programming process. Commercial automated source-to-source translation tools prove inadequate due to the structural and idiomatic differences in how functionalities are expressed across major platforms. In this thesis, we present a new approach---Native-2-Native---that automatically synthesizes code for a mobile application to make use of native resources on one platform, based on the equivalent program transformations performed on another platform. First, the programmer modifies a mobile application's Android version to make use of some native resource, with a plugin capturing code changes. Based on the changes, the system then parameterizes a web search query over popular programming resources (e.g., Google Code, StackOverflow, etc.), to discover equivalent iOS code blocks with the closest similarity to the programmer-written Android code. The discovered iOS code block is then presented to the programmer as an automatically synthesized Swift source file to further fine-tune and subsequently integrate in the mobile application's iOS version. Our evaluation, enhancing mobile applications to make use of common native resources, shows that the presented approach can correctly synthesize more than 86% of Swift code for the subject applications' iOS versions.","abstract_html":"For maximal market penetration, popular mobile applications are typically supported on all major platforms, including Android and iOS. Despite the vast differences in the look-and-feel of major mobile platforms, applications running on these platforms in essence provide the same core functionality. As an application is maintained and evolved, programmers need to replicate the resulting changes on all the supported platforms, a tedious and error-prone programming process. Commercial automated source-to-source translation tools prove inadequate due to the structural and idiomatic differences in how functionalities are expressed across major platforms. In this thesis, we present a new approach---Native-2-Native---that automatically synthesizes code for a mobile application to make use of native resources on one platform, based on the equivalent program transformations performed on another platform. First, the programmer modifies a mobile application's Android version to make use of some native resource, with a plugin capturing code changes. Based on the changes, the system then parameterizes a web search query over popular programming resources (e.g., Google Code, StackOverflow, etc.), to discover equivalent iOS code blocks with the closest similarity to the programmer-written Android code. The discovered iOS code block is then presented to the programmer as an automatically synthesized Swift source file to further fine-tune and subsequently integrate in the mobile application's iOS version. Our evaluation, enhancing mobile applications to make use of common native resources, shows that the presented approach can correctly synthesize more than 86% of Swift code for the subject applications' iOS versions.","abstract_has_math":false,"creators":["Byalik, Antuan"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Science and Applications","degree_department":"Computer Science","school":null,"contributors":[],"advisors":[],"committee_chairs":["Tilevich, Eli"],"committee_members":["Batra, Dhruv","Raghvendra, Sharath"],"year":2015,"date_issued":"2015-08-04","date_published":"2015-08-04","updated_at":"2026-07-22T22:19:42Z","subjects":["Recommendation Systems","Code Synthesis","Mobile Computing","Android","iOS","Java","Swift"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:6052"],"render_values":[{"text":"vt_gsexam:6052","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/55273","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Tilevich, Eli"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Batra, Dhruv","Raghvendra, Sharath"]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science"]},{"key":"dc:creator","label":"Author","values":["Byalik, Antuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-08-05T08:01:05Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-08-05T08:01:05Z"]},{"key":"dc:date.issued","label":"Date","values":["2015-08-04"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science and Applications"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Recommendation Systems","Code Synthesis","Mobile Computing","Android","iOS","Java","Swift"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:6052"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/55273"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["For maximal market penetration, popular mobile applications are typically supported on all major platforms, including Android and iOS. Despite the vast differences in the look-and-feel of major mobile platforms, applications running on these platforms in essence provide the same core functionality. As an application is maintained and evolved, programmers need to replicate the resulting changes on all the supported platforms, a tedious and error-prone programming process. Commercial automated source-to-source translation tools prove inadequate due to the structural and idiomatic differences in how functionalities are expressed across major platforms. In this thesis, we present a new approach---Native-2-Native---that automatically synthesizes code for a mobile application to make use of native resources on one platform, based on the equivalent program transformations performed on another platform. First, the programmer modifies a mobile application's Android version to make use of some native resource, with a plugin capturing code changes. Based on the changes, the system then parameterizes a web search query over popular programming resources (e.g., Google Code, StackOverflow, etc.), to discover equivalent iOS code blocks with the closest similarity to the programmer-written Android code. The discovered iOS code block is then presented to the programmer as an automatically synthesized Swift source file to further fine-tune and subsequently integrate in the mobile application's iOS version. 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Based on the changes, the system then parameterizes a web search query over popular programming resources (e.g., Google Code, StackOverflow, etc.), to discover equivalent iOS code blocks with the closest similarity to the programmer-written Android code. The discovered iOS code block is then presented to the programmer as an automatically synthesized Swift source file to further fine-tune and subsequently integrate in the mobile application's iOS version. 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