AI Coding Assistant for Automated Application Error Fixes
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Solution Overview
Problem
Existing data analytics environments lack an efficient and automated mechanism for addressing errors and exceptions in software development, requiring manual intervention and lengthy development cycles.
Innovation Solution
Implementing an AI-based assistant system that utilizes a first agent to monitor applications and a second agent connected to large language models to automatically detect and generate fixes for errors, streamlining the development process by automating code fixes and deployments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used to address errors and exceptions in software development, then development control and accuracy are maintained, but development cycles become lengthy and productivity decreases
Solution Approach 1:
The system enables self-service error resolution by implementing an automated agent that monitors application logs, detects errors and exceptions, and generates fix code without requiring manual developer intervention. The agent autonomously processes error messages, searches for solutions, and implements corrections, allowing the system to service itself and resolve issues independently.
Solution Approach 2:
The patent replaces the mechanical manual process of error detection and fixing with an automated computational system. The mechanical intervention of developers reviewing logs and writing fixes is substituted by an AI agent that uses machine learning models to automatically analyze errors, generate code fixes, and apply corrections, thereby eliminating the time-consuming manual workflow.
2Productivity
If automated error detection and fix generation is implemented, then productivity and speed are improved, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: an error detection module that monitors application logs, an analysis module that processes error messages, a code generation module that creates fixes using AI models, and an implementation module that applies corrections. This segmentation allows each component to specialize in a specific task, managing overall system complexity through modular design while maintaining high automation capability.
Data Source
AI summary
Embodiments described herein are generally related to data analytics environments, and are particularly directed to systems and methods for use with a data analytics environment to provide an AI-based assistant for use in software development. In accordance with an embodiment, an exemplary method can provide access to a data analytics environment by a computer including one or more processors. The method can provide a first agent operating on the computer, wherein the first agent monitors an application running at an application server. The method can provide a second agent operating on the computer, wherein the second agent comprises a connection to one or more large language models. The method can, upon detection by the first agent, of an error or exception associated with the application running at the application server, utilize, by the second agent, the LLM to generate a fix responsive to the detected error or exception.