Open Access
Syntax-guided synthesis
Rajeev Alur,Rastislav Bodik,Garvit Juniwal,Milo M. K. Martin,Mukund Raghothaman,Sanjit A. Seshia,Rishabh Singh,Emina Torlak,Abhishek Udupa,Armando Solar-Lezama +9 more
TLDR
This work describes three different instantiations of the counter-example-guided-inductive-synthesis (CEGIS) strategy for solving the synthesis problem, reports on prototype implementations, and presents experimental results on an initial set of benchmarks.Abstract:
National Science Foundation (U.S.) (Expeditions in Computing Project ExCAPE Award CCF 1138996)read more
Citations
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Proceedings ArticleDOI
Synthesizing data structure transformations from input-output examples
TL;DR: A method for example-guided synthesis of functional programs over recursive data structures given a set of input-output examples that synthesizes a program in a functional language with higher-order combinators like map and fold.
Proceedings ArticleDOI
FlashMeta: a framework for inductive program synthesis
Oleksandr Polozov,Sumit Gulwani +1 more
TL;DR: The FlashMeta framework implements a novel program synthesis methodology, allowing synthesizer developers to generate an efficient synthesizer from the mere DSL definition (if properties of the DSL operators have been modeled), and found that 10+ existing industrial-quality mass-market applications based on PBE can be cast as instances of D4.
Proceedings ArticleDOI
Automatic software repair: a survey
TL;DR: A new class of approaches, namely program repair techniques, whose key idea is to try to automatically repair software systems by producing an actual fix that can be validated by the testers before it is finally accepted, or that is adapted to properly fit the system.
Proceedings ArticleDOI
Reactive synthesis from signal temporal logic specifications
TL;DR: A counterexample-guided inductive synthesis approach to controller synthesis for cyber-physical systems subject to signal temporal logic (STL) specifications, operating in potentially adversarial nondeterministic environments is presented.
Proceedings Article
Neuro-Symbolic Program Synthesis
TL;DR: Neuro-Symbolic Program Synthesis (NSPSS) as discussed by the authors is based on two neural modules: the cross correlation I/O network and the recursive-Reverse-Recursive Neural Network (R3NN).
References
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Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Programs for Machine Learning
Steven L. Salzberg,Alberto Segre +1 more
TL;DR: In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.
Journal ArticleDOI
Language identification in the limit
TL;DR: It was found that theclass of context-sensitive languages is learnable from an informant, but that not even the class of regular languages is learningable from a text.
Book
Algorithmic Program Debugging
TL;DR: An algorithm that can fix a bug that has been identified, and integrate it with the diagnosis algorithms to form an interactive debugging system that can debug programs that are too complex for the Model Inference System to synthesize.
Journal ArticleDOI
Counterexample-guided abstraction refinement for symbolic model checking
TL;DR: An automatic iterative abstraction-refinement methodology that extends symbolic model checking to large hardware designs and devise new symbolic techniques that analyze such counterexamples and refine the abstract model correspondingly.