There is a gap observed between the natural language (NL) of speech and writing a program to generate code. Programmers should know the syntax of the programming language in order to code. The aim of the proposed model is to do away with the syntactic structure of a programming language and the user can specify the instructions in human interactive form, using either text or speech. The designed solution is an application based on speech recognition and user interaction to make coding faster and efficient. Lexical, syntax and semantic analysis is performed on the user’s instructions and then the code is generated. C is used as the programming language in the proposed model. The code editor is a web page and the user instructions are sent to a Flask server for processing. Using Python libraries NLTK and ply libraries, conversion of human language data to programmable C codes is done and the code is returned to the client. Lex is used for tokenization and the LALR parser of Yacc processes the syntax specifications to generate an output procedure. The results are recorded and analyzed for time taken to convert the NL commands to code and the efficiency of the implementation is measured with accuracy, precision and recall.

A Compiler-based Approach for Natural Language to Code Conversion

Computers have become an integral part of the scientific world. The real-life problems, we face, are dealt with an algorithmic approach. Algorithms being independent of programming language, they can be developed using any natural spoken language that a person is comfortable with. However, the problem lies in implementing it. People who do not have a programming background often face difficulty in writing an efficient code to implement their algorithm. In this paper, we describe a technique to translate any algorithm written in a step-wise pattern, to an executable code in a particular programming language (here Java). Our proposed system would allow the users to provide their algorithm in a step-wise manner in their natural language. Since the algorithm has no structural specification, it takes the load off the user and converts the algorithm into a program.

http://ieeexplore.ieee.org/iel7/7589474/7724213/07724425.pdf

Automated system for code generation from unstructured algorithm

The rise in legislation and the need for task automation systems has resulted in an amplified requirement for software development. This is aimed at improving the accuracy and efficiency of reading and interpreting laws in legal activities, allowing for enhanced precision and swiftness. Currently, there is a need for analysts to interpret written legislation and then encode it into computer programs, which often leads to errors. As cryptocurrencies gain popularity, there is growing interest in utilizing Blockchain technology in the legal field. Specifically, smart contracts can be used to integrate business rules into laws and automate blockchain management. However, the process of writing fast and high-quality code can be improved by leveraging artificial intelligence based techniques such as natural language processing (NLP) to help practitioners. Despite reviewing the current state-of-the-art, there is a lack of existing work that combines smart contracts and NLP in the context of legislation analysis. This study work generates intelligent code from legislation analysis, utilizing NLP and Blockchain for this purpose. In this research work a pilot prototype for smart contract generation is developed and initial code samples are presented. This work demonstrates a promising results with 96% accuracy in Name Entity Recognition (NER) and also highlights the importance of smart contract generation through NLP. 

Smart Contract Generation through NLP and Blockchain for Legal Documents

Coding in a programming language can indeed be a meticulous and a less interesting task, especially when compared to the fluidity of spoken or written communication. Each programming language has its own set of syntax and rules, demanding expertise to effectively write code in it. Through this study, a compiler based approach to translate Natural Language into Python code is proposed. This endeavor could revolutionize access to programming by making it more intuitive and inclusive. The research involves working with different phases of the compiler including the lexical analyser, syntax and the semantic analyser phase in combination with Natural Language Processing techniques. By automating this process, we aim to streamline programming for a wider audience, removing barriers to entry and making coding more accessible to all.

Transcribing Natural Language to Python Code: A Compiler Based Approach

Computers have become an integral part of the scientific world. The real-life problems are dealt with an algorithmic approach. Algorithms being independent of programming language, they can be developed using any natural spoken language that a person is comfortable with. However, the problem lies in implementing it. The computational intelligence model proposed in this paper approaches such problem by carrying out mapping at Semantic level using Natural Language Processing and ontology and applying Ontology Matching techniques to derive an automatic translator of natural language problem statement into the artificial language (here Java). The intermediate steps of translation are processed by using corpus of English for developing some techniques for mapping linguistic constructs to programming structures. The modern NLP techniques can make possible the conversion of natural language statements to a programming language. Overall, this paper proposes a knowledge-based expert system which makes use of facts and rules to build the solution.

Computational Intelligence Model for Code Generation from Natural Language Problem Statement

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Computational Intelligence Model for Code Generation from Natural Language Problem Statement