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AUTOMATED CODE SYNTHESIS USING DEEP LEARNING TECHNIQUES
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Abstract
Information
Inventors
Applicants
Specification
Documents
ORDINARY APPLICATION
Published
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 ID | 202441088833 |
Invention Field | COMPUTER SCIENCE |
Date of Application | 17/11/2024 |
Publication Number | 47/2024 |
Inventors
Name | Address | Country | Nationality |
---|---|---|---|
Mrs.A.Srilatha | Assistant Professor, Department of Computer Science and Engineering (CSE), Anurag Engineering College, Ananthagiri, Kodad, Suryapet, Telangana – 508206 | India | India |
Mr.Vummadi Achireddy | Assistant Professor, Department of Computer Science and Engineering (CSE), Anurag Engineering College, Ananthagiri, Kodad, Suryapet, Telangana – 508206 | India | India |
K V Maruthish | Software / IT Professional, Research Enthusiast, Bangalore 560037 | India | India |
Dr.T Srikanth | Associate Professor, Department of Computer Science and Engineering, Malla Reddy Engineering College for Women (Autonomous), Telangana 500100 | India | India |
Dr.Vinodpuri Rampuri Gosavi | Associate Professor, Department of Electronics and Telecommunications Engineering, Sandip Foundation's Sandip Institute of Technology and Research Center (SITRC), Nashik, Maharashtra, India | India | India |
M Ezhilvendan | Assistant Professor, Department of Artificial Intelligence and Machine Learning, Panimalar Engineering College, Bangalore Trunk Road, Varadharajapuram, Poonamallee, Chennai 600123 | India | India |
Mr. Vallem Ranadheer Reddy | Assistant Professor, Malla Reddy Engineering College for Women Maisammaguda, Kompally, Medchal, Telangana, India 500100 | India | India |
Applicants
Name | Address | Country | Nationality |
---|---|---|---|
Anurag Engineering College | Anurag Engineering College, Ananthagiri(V), Kodad, Suryapet (Dist), Telangana-508206 | India | India |
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
Name | Date |
---|---|
202441088833-COMPLETE SPECIFICATION [17-11-2024(online)].pdf | 17/11/2024 |
202441088833-DECLARATION OF INVENTORSHIP (FORM 5) [17-11-2024(online)].pdf | 17/11/2024 |
202441088833-DRAWINGS [17-11-2024(online)].pdf | 17/11/2024 |
202441088833-FORM 1 [17-11-2024(online)].pdf | 17/11/2024 |
202441088833-FORM-9 [17-11-2024(online)].pdf | 17/11/2024 |
202441088833-REQUEST FOR EARLY PUBLICATION(FORM-9) [17-11-2024(online)].pdf | 17/11/2024 |
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