Spreadsheet table transformations from examples

By William R. Harris, Sumit Gulwani. 2011

202 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Software, Signal Processing

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Citations in the corpus

A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “Improve Data Data Cleaning [158], [159], [160], [161], [162], [163], [164], [165], [166] Re-labeling [119]”
Predicting a Correct Program in Programming by Example (2015, background): “There have been many recent proposals for domain-specific languages for PBE systems in the domains of string, table, numbers, and layout manipulations [1, 8, 12, 25, 26].”
Program Synthesis Using Natural Language (2015, background): “has included domains for manipulating strings [13, 48], numbers [49], and tables [21].”
On the difficulty of benchmarking inductive program synthesis methods (2017, background): “As such, it is able to quickly create simple programs for one-o repetitive tasks in spreadsheet applications for non-programmers [4, 5, 20, 21].”
Synthesizing data structure transformations from input-output examples (2015, background): “Existing synthesis techniques that require only input-output examples are often restricted to programs over basic data types like numbers [29], strings [11], and tables [13], as opposed to recursive data structures.”
Component-based synthesis for complex APIs (2017, result): “Similar to many programming-byexample (PBE) approaches, SYPET requires users to provide partial specifications as input-output examples [2, 7, 11, 14, 18, 31, 39].”
Spreadsheet data manipulation using examples (2012, methodology): “Each synthesizer takes the role of the forum expert, removing a human from the interaction loop and enabling users to solve their problems in a few seconds instead of few days.”
NLyze: interactive programming by natural language for spreadsheet data analysis and manipulation (2014, unknown): “Recent work on PBE by Gulwani et.al. has included domains for manipulating strings [7, 35], numbers [36], and tables [10].”
Synthesizing transformations on hierarchically structured data (2016, background): “Many approaches in this area are focused on non-hierarchical data types like numbers [32], strings [15], tables [6, 16].”
Synthesis of loop-free programs (2011, background): “, Jungloid [26]), bitvector algorithms, geometry constructions [14, 18], text-editing [24, 38] and table-manipulation programs [16].”
Learning Semantic String Transformations from Examples (2012, background): “The work in [10] describes a programming by example technology for learning layout transformations on tables.”
FlashMeta: a framework for inductive program synthesis (2015, methodology): “D 4 as a Uniﬁcation of Prior Work In our case studies, we observed that most prior work in domain-speciﬁc inductive synthesis [2, 3, 5, 7, 8, 16, 21, 22, 28, 32, 33, 38] can be cast as instances of D 4 methodology, in which the original synthesis algorithms naturally arise from the properties of…”
Foofah: Transforming Data By Example (2017, methodology): “Our experiments were designed in a way similar to that used by an influential work in spreadsheet data transformation, ProgFromEx [17], as well as other related work in data transformation [4, 24].”