Description:Technical field of invention:

The present invention relates to provide automated bug detection and repair in software using deep learning.

Background:

Traditionally, bug detection and repair have relied on manual methods, which can be time-consuming, error-prone, and costly. Developers typically rely on testing, code reviews, and debugging techniques to identify and fix bugs. However, these manual approaches may not be able to effectively detect complex or subtle bugs, especially in large-scale software projects.

Deep learning, a subfield of machine learning, has emerged as a promising approach for automated bug detection and repair. Deep learning models, such as neural networks, can learn from large-scale datasets and discover intricate patterns and relationships in software code. This ability makes them well-suited for identifying and understanding complex bugs that may be challenging to detect using traditional methods.

Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

The recitation of ranges of values herein is merely intended to serve as a shorth and method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context.

The use of any and all examples, or exemplary language (e.g., “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.

The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art.

Objective of the invention

The primary object of the present invention is to provide automated bug detection and repair in software using deep learning.

Summary of the invention:

The present invention relates to provide automated bug detection and repair in software using deep learning.

Further, the system consists of a Deep Learning Bug Detection unit, Real-time Bug Monitoring unit, Automated Bug Localization unit, and Intelligent Bug Repair Suggestions unit.

Further, by analyzing software code, the system identifies bugs, provides real-time feedback, locates bug locations, and offers intelligent suggestions for repair.

Furthermore, its advantages include continuous learning, seamless integration with development environments, and the ability to detect security vulnerabilities. This cutting-edge system revolutionizes bug identification and resolution, enhancing efficiency, accuracy, and software quality.

Detailed description of invention:

The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

The present invention relates to provide automated bug detection and repair in software using deep learning.

Further, the system provides an innovative software solution that leverages deep learning techniques for automated bug detection and repair. This cutting-edge system revolutionizes the way software bugs are identified and resolved, saving developers valuable time and resources while ensuring higher software quality.

The system comprises of Deep Learning Bug Detection unit, Real-time Bug Monitoring unit, Automated Bug Localization unit, Intelligent Bug Repair Suggestions unit.

Deep Learning Bug Detection unit: the system is utilized advanced deep learning algorithms to analyze software code and identify potential bugs automatically. The system is trained on vast datasets of code examples, allowing it to recognize patterns and anomalies associated with common bug types. It helps to improve the efficiency and accuracy of bug detection.

Real-time Bug Monitoring unit: The system continuously monitors software code, providing real-time feedback during the development process. It flags potential bugs as soon as they are detected, allowing developers to address them promptly and minimize the impact on the overall project timeline.

Automated Bug Localization: the system works bug detection by automatically localizing the precise location of the identified bugs within the codebase. It highlights the specific lines or sections of code that require attention, simplifying the debugging process for developers and reducing the time needed to pinpoint and understand the issue.

Intelligent Bug Repair Suggestions: the system provides intelligent suggestions for bug repair. Based on its extensive knowledge base, the system provides recommendations and proposes fixes for common bugs, helping developers resolve issues quickly and efficiently.

Advantages:

Learning and Adaptation: the system continuously learns from user feedback and new bug data, improving its bug detection and repair capabilities over time.

Integration with Development Environments: the system seamlessly integrates into popular software development environments, such as integrated development environments (IDEs) or code repositories.

Security Vulnerability Detection: the system also includes features for identifying security vulnerabilities in code. By analyzing code patterns and known security risks, the system can flag potential vulnerabilities and provide suggestions for strengthening security measures, ensuring more robust and secure software.
, Claims:1. Automated bug detection and repair in software using deep learning.

2. Automated bug detection and repair in software using deep learning claimed in claim 1, the system provides an innovative software solution that leverages deep learning techniques for automated bug detection and repair.