AI-ENHANCED SOFTWARE DEBUGGING TOOL

Abstract

The invention provides an AI-enhanced software debugging tool that automatically detects, diagnoses, and suggests corrections for software errors using machine learning and natural language processing techniques. The tool analyzes source code for syntax errors, logical issues, and potential runtime errors by applying machine learning models trained on large datasets of code. It generates error predictions and provides correction suggestions tailored to the detected issues. The tool offers natural language explanations of both the identified errors and the proposed fixes, making the debugging process more transparent and user-friendly. Integrated with existing development environments such as Integrated Development Environments (IDEs) and version control systems, the tool delivers real-time feedback to developers, improving productivity and reducing the time spent on manual debugging. The tool also supports natural language queries, allowing developers to request clarifications or alternative solutions, furthe

Specification

Description:The embodiments of the present invention generally relates to software development and debugging tools, specifically an AI-enhanced software debugging tool that leverages machine learning algorithms and natural language processing (NLP) to automatically detect, diagnose, and suggest corrections for software errors. This invention aims to enhance the efficiency, accuracy, and usability of debugging processes in complex software development environments.
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.

Debugging is a critical and time-consuming phase in the software development lifecycle. As software syste , Claims:1. A method for automated software debugging, comprising:
analyzing source code for syntax errors and code inconsistencies using a static code analyzer;
applying a machine learning model to the analyzed code to predict potential software errors based on error patterns and context;
generating one or more error correction suggestions based on the predicted errors;
providing natural language explanations for the identified errors and suggested corrections; and
displaying the error correction suggestions and explanations to the developer within an integrated development environment (IDE) or version control system in real-time.

2. The method of claim 1, wherein the machine learning model is trained on a dataset of previously debugged code, including error types and corresponding corrections.

3. The method of claim 1, wherein the error correction suggestions include modifications to code syntax, variable names, control flow, or memory management techniques.

4. The method of claim 1, wherein the natural language

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