Predicting a Correct Program in Programming by Example

By Rishabh Singh, Sumit Gulwani. 2015

91 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Software, Code Generation & Program Synthesis

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In Information Systems

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Citations in the corpus

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation (2021, methodology): “The program synthesis [47, 58, 62, 74, 17, 40, 90] extends the text-to-code generation task aims to generate programs from a specification [21], such as pseudocode, natural language description, and input/output examples.”
DeepCoder: Learning to Write Programs (2016, methodology): “…input-output examples to program properties; and 3. experiments that show an order of magnitude speedup over standard program synthesis techniques, which makes this approach feasible for solving problems of similar difﬁculty as the simplest problems that appear on programming competition websites.”
FlashMeta: a framework for inductive program synthesis (2015, background): “Designing a good ranking function is a challenging open problem; one promising approach is using machine learning [34], but it requires a sufﬁcient training set of synthesis scenarios.”
Foofah: Transforming Data By Example (2017, methodology): “[43–45], as well as FlashFill [12, 38] and BlinkFill [36] are built for syntactic transformation.”
Jigsaw: Large Language Models meet Program Synthesis (2021, methodology): “The literature on using machine learning for program synthesis is vast [8, 17, 18, 21, 22, 25, 28, 35, 41] and we restrict to works which are closest to Jigsaw (synthesizing code for large APIs using large models and multi-modal specifications).”
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples (2018, methodology): “Gulwani (2011) and later Polozov & Gulwani (2015) used it to build PROSE, a commercially successful domain-agnostic system for PBE.”
Automatic Program Synthesis of Long Programs with a Learned Garbage Collector (2018, result): “This is in contrast to some recent work [8, 5, 16], focused on string processing, where success is measured on unseen samples.”