Although current computer-aided software engineering tools support developers in composing a program, there is no doubt that more flexible supportive tools are needed to address the increases in the complexity of programs. This need can be met by automating the intellectual activities that are carried out by humans when composing a program. This paper aims to automate the composition of a programming language code from pseudocode, which is viewed here as a translation process for a natural language text, as pseudocode is a formatted text in natural English language. Based on this view, a new automatic code generator is developed that can convert pseudocode to C# programming language code. This new automatic code generator (ACG), which is called CodeComposer, uses natural language processing (NLP) techniques such as verb classification, thematic roles, and semantic role labeling (SRL) to analyze the pseudocode. The resulting analysis of linguistic information from these techniques is used by a semantic rule-based mapping machine to perform the composition process. CodeComposer can be viewed as an intelligent computer-aided software engineering (I_CASE) tool. An evaluation of the accuracy of CodeComposer using a binomial technique shows that it has a precision of 88%, a recall of 91%, and an F-measure of 89%.

/pdf/the-use-of-natural-language-processing-approach-for-4y058gv4iq.pdf

The Use of Natural Language Processing Approach for Converting Pseudo Code to C# Code

Pseudo-code refers to an informal means of representing algorithms that do not require the exact syntax of a computer programming language. Pseudo-code helps developers and researchers represent their algorithms using human-readable language. Generally, researchers can convert the pseudo-code into computer source code using different conversion techniques. The efficiency of such conversion methods is measured based on the converted algorithm&#x2019;s correctness. Researchers have already explored diverse technologies to devise conversion methods with higher accuracy. This paper proposes 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. We conducted an extensive experiment on our designed algorithm using descriptive bilingual understudy scoring and compared our results with state-of-the-art techniques. Result analysis shows that our approach is more accurate and efficient than other existing conversion methods in terms of several performances metrics. Furthermore, the proposed method outperforms the existing approaches because our method utilizes two Long-Short-Term-Memory networks that might increase the accuracy. 

Sequence-to-Sequence Learning Based Conversion of Pseudo-code to Source code Using Neural Translation Approach

We explore contemporary natural language processing (NLP) techniques for converting NL specifications found in design documents directly to an temporal logic-like intermediate representation (IR). Generally, attempts to use NLP for assertion generation have relied on restrictive sentence formats and grammars as well as being difficult to handle new sentence formats. We tackle these issues by first implementing a system that uses commonsense mappings to process input sentences into a normalized form. Then we use frame semantics to convert the normalized sentences into an IR based on the information and context contained in the Frames. Through this we are able to handle a large number of sentences from real datasheets allowing for complex formats using temporal conditions, property statements, and compound statements; all order agnostic. Our system can also be easy extended by modifying an external, rather than internal, commonsense knowledge-base to handle new sentence formats without requiring code changes or intimate knowledge of the algorithms used.

Automated Assertion Generation from Natural Language Specifications

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

ForEmb, a real-time interpreter, was implemented and ported to various systems, including a Raspberry Pi Pico board, the design process of the interpreter is described in detail. ForEmb successfully simulated an information signal -using sine, cosine and exponential functions- with an absolute error percentage of 0.18% compared to the response of MATLAB and a variance of 1.06×10−8. The CMP and CKP sensor signals for Neon 2.0 L engine were also emulated by implementing them into ForEmb running on a RP2040 microcontroller; experimental measurements of injection timing validate real-time implementation.

ForEmb: A Forth-Inspired, Real-Time Interpreter for Embedded Systems

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.

Semi natural language algorithm to programming language interpreter

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Semi natural language algorithm to programming language interpreter