From program synthesis to optimal program synthesis

Reyna, Joaquin

doi:10.1109/nafips.2011.5752038

Cited by 1 publication

(3 citation statements)

References 12 publications

(14 reference statements)

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“…Abstract finite tree automata used for expressing an initial program. [66,67] Logic with approximation approaches to solve the synthesis problem. [68][69][70][71][72][73] Optimization approaches are used to solve the synthesis problem.…”

Section: [49]mentioning

confidence: 99%

“…[68][69][70][71][72][73] Optimization approaches are used to solve the synthesis problem. [33,48,[64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] Constraint solving approach, SAT/SMT-based techniques to solve the synthesis problem. [80][81][82][83] Neural networks, deep learning, machine leaning and its related techniques to solve the synthesis problem.…”

Section: [49]mentioning

confidence: 99%

“…The approximation problem usually results in the discovery of approximate solutions (the nearest solution) to the optimal one. Synthesis approaches in this category aim to automatically produce optimal programs that approximately meet a desired correctness specification with certain attributes, for example, the fastest program [66,67]. According to [67], a collection of large problems, where candidate programs have a search space that is too large and hard to explore, is introduced as a PARROT benchmark suite.…”

Section: Search Strategymentioning

confidence: 99%

See 2 more Smart Citations

Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Subahi¹

2020

Computers

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Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, there have been remarkable advancements in the Artificial Intelligent (AI) domain. The rise in advanced ML techniques has been remarkable. Deep Learning (DL), for instance, is considered an example of a currently attractive research field that has led to advances in the areas of ML and NLP. With this advancement, there is a need to gain greater benefits from these approaches to cognify synthesis processes for next-generation model-driven engineering (MDE) framework. In this work, a systematic domain analysis is conducted to explore the extent to the automatic generation of code can be enabled via the next generation of cognified MDE frameworks that support recent DL and NLP techniques. After identifying critical features that might be considered when distinguishing synthesis systems, it will be possible to introduce a conceptual design for the future involving program synthesis/MDE frameworks. By searching different research database sources, 182 articles related to program synthesis approaches and their applications were identified. After defining research questions, structuring the domain analysis, and applying inclusion and exclusion criteria on the classification scheme, 170 out of 182 articles were considered in a three-phase systematic analysis, guided by some research questions. The analysis is introduced as a key contribution. The results are documented using feature diagrams as a comprehensive feature model of program synthesis showing alternative techniques and architectures. The achieved outcomes serve as motivation for introducing a conceptual architectural design of the next generation of cognified MDE frameworks.

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