Proceedings Article10.1109/HMI.2016.7449190
Semi natural language algorithm to programming language interpreter
Sharvari Nadkarni,Parth S. Panchmatia,Tejas Karwa,Swapnali Kurhade +3 more
- 03 Mar 2016
- pp 1-4
7
TL;DR: An interpreter that is capable of converting algorithms in English to C code whose flexibility of interpretation has been enhanced by using synonyms and by the introduction of a personalised training model whose concept has been outlined below.
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Abstract: The conversion of an algorithm to code is still at an early stage. Effective conversion of algorithms mentioned in natural English language to code will enable programmers to focus on logic building and free them of syntactical worries, further it will also aid the visually impaired programmers. Although beneficial, implementation of such a converter encounters numerous challenges like limitations imposed due to semantics of the English language, case frames, etc. In this paper we have introduced an interpreter that is capable of converting algorithms in English to C code whose flexibility of interpretation has been enhanced by using synonyms and by the introduction of a personalised training model whose concept has been outlined below. We have defined the conceptual model along with a user scenario which demonstrates the functioning of our model.
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Citations
The Use of Natural Language Processing Approach for Converting Pseudo Code to C# Code
TL;DR: A new automatic code generator is developed that can convert pseudocode to C# programming language code and is called CodeComposer, which uses natural language processing techniques such as verb classification, thematic roles, and semantic role labeling to analyze the pseudocodes.
Sequence-to-Sequence Learning Based Conversion of Pseudo-code to Source code Using Neural Translation Approach
01 Jan 2022
TL;DR: In this paper , the authors proposed a novel pseudo-code conversion learning method that includes natural language processing-based text preprocessing and a sequence-to-sequence deep learning-based model trained with the SPoC dataset.
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Automated Assertion Generation from Natural Language Specifications
Steven J. Frederiksen,John Aromando,Michael S. Hsiao +2 more
- 01 Nov 2020
TL;DR: In this paper, a system that uses commonsense mappings to process input sentences into a normalized form is proposed, which is then used to convert the normalized sentences into an IR based on the information and context contained in the Frames.
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Computational Intelligence Model for Code Generation from Natural Language Problem Statement
A. B. Kulkarni,S. S. Karandikar,P. A. Bamhore,S. R. Gawade,Darshan Vishwasrao Medhane +4 more
- 01 Aug 2018
TL;DR: Overall, this paper proposes a knowledge-based expert system which makes use of facts and rules to build the solution of the computational intelligence model proposed in this paper.
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ForEmb: A Forth-Inspired, Real-Time Interpreter for Embedded Systems
Edwin Christian Becerra-Alvarez,Juan José Raygoza Panduro,J. Rivera-Domínguez,Susana Ortega-Cisneros,J. González-Vidal +4 more
- 18 Oct 2023
TL;DR: ForEmb is a real-time interpreter for embedded systems that successfully simulates various functions and sensor signals. It has been ported to various systems and validated with experimental measurements.
1
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