Context-Aware Systems and Applications, and Nature of Computation and Communication. 8th EAI International Conference, ICCASA 2019, and 5th EAI International Conference, ICTCC 2019, My Tho City, Vietnam, November 28-29, 2019, Proceedings

Research Article

Text to Code: Pseudo Code Generation

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  • @INPROCEEDINGS{10.1007/978-3-030-34365-1_3,
        author={Altaf Din and Awais Adnan},
        title={Text to Code: Pseudo Code Generation},
        proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 8th EAI International Conference, ICCASA 2019, and 5th EAI International Conference, ICTCC 2019, My Tho City, Vietnam, November 28-29, 2019, Proceedings},
        proceedings_a={ICCASA \& ICTCC},
        year={2019},
        month={12},
        keywords={Pseudo code generation NL to programming code Intelligent system Code extraction from text},
        doi={10.1007/978-3-030-34365-1_3}
    }
    
  • Altaf Din
    Awais Adnan
    Year: 2019
    Text to Code: Pseudo Code Generation
    ICCASA & ICTCC
    Springer
    DOI: 10.1007/978-3-030-34365-1_3
Altaf Din1,*, Awais Adnan2,*
  • 1: AWKUM
  • 2: IMSciences
*Contact email: altafkhattak@gmail.com, awaisadnan@gmail.com

Abstract

The evolutions in programming from machine language to these days programming software have made it easy, to some extent, to develop software but it is not as easy as programming in natural language. In order to transfer natural language text to any programming language code, it felt necessary to first transform natural language text into pseudo code algorithm then with the help of right API library, such algorithms can be transform into any programming language code. The main aim of this research work is to produce pseudo code from text however this work is very loosely bound to natural language processing. Main components of this proposed work is text analyser that utilizes language tools (spelling check, grammar check) to remove type errors and then eliminate different ambiguities. For this step of ambiguity removal, an adaptive solution is proposed that learning from manual assistance. Once the text is cleared, pattern matching techniques is applied to it and later on parsed into a pseudo code. The concept model is tested with user scenario approach and also practically implemented by developing a prototype. This model is examined using 100 examples of different categories and achieved 73.