{"id":238212,"date":"2016-09-01T00:00:00","date_gmt":"2016-09-01T07:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/blinkfill-semi-supervised-programming-by-example-for-syntactic-string-transformations\/"},"modified":"2018-10-16T20:02:31","modified_gmt":"2018-10-17T03:02:31","slug":"blinkfill-semi-supervised-programming-by-example-for-syntactic-string-transformations","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/blinkfill-semi-supervised-programming-by-example-for-syntactic-string-transformations\/","title":{"rendered":"BlinkFill: Semi-supervised Programming By Example for Syntactic String Transformations"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The recent Programming By Example (PBE) techniques such as FlashFill have shown great promise for enabling end-users to perform data transformation tasks using input-output examples. Since examples are inherently an under-specification, there are typically a large number of hypotheses conforming to the examples, and the PBE techniques suffer from scalability issues for finding the intended program amongst the large space.<\/p>\n<p>We present a semi-supervised learning technique to significantly reduce this ambiguity by using the logical information present in the input data to guide the synthesis algorithm. We develop a data structure InputDataGraph to succinctly represent a large set of logical patterns that are shared across the input data, and use this graph to efficiently learn substring expressions in a new PBE system BlinkFill. We evaluate BlinkFill on 207 real-world benchmarks and show that BlinkFill is significantly faster (on average 41x) and requires fewer input-output examples (1.27 vs 1.53) to learn the desired transformations in comparison to FlashFill.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The recent Programming By Example (PBE) techniques such as FlashFill have shown great promise for enabling end-users to perform data transformation tasks using input-output examples. Since examples are inherently an under-specification, there are typically a large number of hypotheses conforming to the examples, and the PBE techniques suffer from scalability issues for finding the intended [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"risin","user_id":"33413"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"PVLDB, 42nd International Conference on Very Large Data Bases (VLDB 2016)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"PVLDB, 42nd International 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