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AUTOMATED CODE SYNTHESIS USING DEEP LEARNING TECHNIQUES

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AUTOMATED CODE SYNTHESIS USING DEEP LEARNING TECHNIQUES

ORDINARY APPLICATION

Published

date

Filed on 17 November 2024

Abstract

The present invention relates to an automated code synthesis system that utilizes deep learning techniques to generate source code from high-level specifications. The system receives input in the form of natural language descriptions, pseudo-code, or visual programming models, processes the input through a preprocessing module, and employs a deep learning model to generate corresponding source code. A validation module ensures the generated code is syntactically correct and semantically valid, while an optimization module refines the code for performance, memory usage, and scalability. The system provides an intuitive output interface for users, allowing them to receive optimized, error-free code ready for integration into development projects. The invention significantly accelerates the software development process, reducing manual coding efforts and improving code quality.

Patent Information

Application ID202441088833
Invention FieldCOMPUTER SCIENCE
Date of Application17/11/2024
Publication Number47/2024

Inventors

NameAddressCountryNationality
Mrs.A.SrilathaAssistant Professor, Department of Computer Science and Engineering (CSE), Anurag Engineering College, Ananthagiri, Kodad, Suryapet, Telangana – 508206IndiaIndia
Mr.Vummadi AchireddyAssistant Professor, Department of Computer Science and Engineering (CSE), Anurag Engineering College, Ananthagiri, Kodad, Suryapet, Telangana – 508206IndiaIndia
K V MaruthishSoftware / IT Professional, Research Enthusiast, Bangalore 560037IndiaIndia
Dr.T SrikanthAssociate Professor, Department of Computer Science and Engineering, Malla Reddy Engineering College for Women (Autonomous), Telangana 500100IndiaIndia
Dr.Vinodpuri Rampuri GosaviAssociate Professor, Department of Electronics and Telecommunications Engineering, Sandip Foundation's Sandip Institute of Technology and Research Center (SITRC), Nashik, Maharashtra, IndiaIndiaIndia
M EzhilvendanAssistant Professor, Department of Artificial Intelligence and Machine Learning, Panimalar Engineering College, Bangalore Trunk Road, Varadharajapuram, Poonamallee, Chennai 600123IndiaIndia
Mr. Vallem Ranadheer ReddyAssistant Professor, Malla Reddy Engineering College for Women Maisammaguda, Kompally, Medchal, Telangana, India 500100IndiaIndia

Applicants

NameAddressCountryNationality
Anurag Engineering CollegeAnurag Engineering College, Ananthagiri(V), Kodad, Suryapet (Dist), Telangana-508206IndiaIndia

Specification

Description:The embodiments of the present invention generally relates to the field of software development, specifically to an automated system for code synthesis using deep learning techniques. More particularly, it concerns methods and systems that leverage machine learning algorithms to automatically generate source code from high-level specifications such as natural language descriptions, pseudo-code, or visual programming models, thereby facilitating faster, more efficient software development.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

In traditional software development, coding is often a time-consumin , Claims:1. An automated code synthesis system, comprising:
an input interface configured to receive a high-level specification from a user, wherein the specification is one of natural language descriptions, pseudo-code, or visual programming models;
a preprocessing module configured to process the received specification and convert it into a format suitable for a deep learning model;
a deep learning model configured to generate source code based on the processed specification;
a validation module configured to check the generated source code for syntax errors and semantic correctness;
an optimization module configured to optimize the generated source code for performance, memory usage, and scalability;
an output module configured to provide the generated and optimized source code to the user.

2. The system of claim 1, wherein the high-level specification is a natural language description, and the preprocessing module includes a natural language processing (NLP) unit that extracts key concepts and relationships from

Documents

NameDate
202441088833-COMPLETE SPECIFICATION [17-11-2024(online)].pdf17/11/2024
202441088833-DECLARATION OF INVENTORSHIP (FORM 5) [17-11-2024(online)].pdf17/11/2024
202441088833-DRAWINGS [17-11-2024(online)].pdf17/11/2024
202441088833-FORM 1 [17-11-2024(online)].pdf17/11/2024
202441088833-FORM-9 [17-11-2024(online)].pdf17/11/2024
202441088833-REQUEST FOR EARLY PUBLICATION(FORM-9) [17-11-2024(online)].pdf17/11/2024

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