Open Access
Automatic algorithm specification to source code translation
Suvam Mukherjee,Tamal Chakrabarti +1 more
- 01 Jan 2011
TL;DR: This paper describes a translation program that can create a piece of executable code, given the code’s algorithmic specification, and convert it to a specified language (be it C, Java, or any other language).
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Abstract: Computers have become all-pervasive, and are being used in a variety of areas like Microbiology, Astronomy, Social Sciences and many others. In almost all these areas, algorithmic solutions to problems are common. However, most programming languages have certain idiosyncrasies. This is why people who don’t have a good background in computer programming have difficulty in writing good, efficient programs. Moreover, there are many programming languages which allow coding in a variety of paradigms. Though it is easy for someone trained in Computer Science to convert a program from one language to another, it is less so for people in other fields. In this paper, we describe a translation program that can create a piece of executable code, given the code’s algorithmic specification. This program allows the user to specify his/her code using an easy-to-understand, simple-to-write and more or less immutable pseudo code specification. The program will then check the pseudo code for errors, and convert it to a specified language (be it C, Java, or any other language). The program may easily be extended to accommodate different languages. Our program allows the user to focus on just the algorithm, and not on issues related to implementation.
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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.
Automatic code generation with business logic by capturing attributes from user interface via XML
Sunil D Rathod
- 01 Mar 2016
TL;DR: The tool - “Rapid Project Builder” (RPB) addresses the problems thereby avoiding the time consuming task of coding the same business logic repeatedly.
9
Semi natural language algorithm to programming language interpreter
Sharvari Nadkarni,Parth S. Panchmatia,Tejas Karwa,Swapnali Kurhade +3 more
- 03 Mar 2016
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.
8
Machine Learning for Translating Pseudocode to Python: A Comprehensive Review
S. Tiwari,M G Thushara +1 more
- 17 May 2023
TL;DR: In this paper , an NLP transformer model is used to translate pseudocode into Python code automatically. But the model is trained on a large dataset of pseudoccode examples and corresponding Python code translations.
4
A Fast Software Project Development Tool with Generic Xml Generation
Sunil D. Rathod,S. D. Joshi +1 more
- 12 Feb 2015
TL;DR: The tool “Rapid Project Builder” (RPB) is used to avoid time consumed for coding the same business logic repeatedly and will also perform automatic Code Generation (ACG) in specific language of developer’s requirement.
References
On the shortest spanning subtree of a graph and the traveling salesman problem
Joseph B. Kruskal
- 01 Feb 1956
TL;DR: Kurosh and Levitzki as discussed by the authors, on the radical of a general ring and three problems concerning nil rings, Bull Amer Math Soc vol 49 (1943) pp 913-919 10 -, On the structure of algebraic algebras and related rings.
A guided tour to approximate string matching
TL;DR: This work surveys the current techniques to cope with the problem of string matching that allows errors, and focuses on online searching and mostly on edit distance, explaining the problem and its relevance, its statistical behavior, its history and current developments, and the central ideas of the algorithms.
Multiple DNA Sequence Alignment Based on Genetic Algorithms and Divide-and-Conquer Techniques
TL;DR: A new method is presented using genetic algorithms and divide-and-conquer techniques to choose optimal cut points of multiple DNA sequences and the experimental results show that the proposed method is better than the existing method for dealing with multiple DNA sequence alignment.
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