ML Chatbot Code Error Detection and Correction
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Solution Overview
Problem
Conventional error checking methods for code, such as those used in insurance applications, require significant manual intervention and human expertise, leading to inefficiencies and ineffectiveness in detecting and correcting code errors.
Innovation Solution
A system and method utilizing a machine learning or artificial intelligence chatbot to automatically detect and fix code errors by sending target code for analysis, determining the presence of errors, and implementing solutions through interaction with the chatbot, with the ability to analyze and implement solutions to correct the code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional error checking methods are used, then code errors can be detected, but significant manual intervention and human expertise are required
Solution Approach 1:
The system enables code to be checked and errors to be detected automatically without requiring human operators. The chatbot autonomously analyzes code, identifies errors, and even generates fix suggestions, making the error checking process self-service and eliminating manual intervention while maintaining high detection accuracy
Solution Approach 2:
The patent replaces manual human expertise with an automated chatbot system that uses machine learning and natural language processing to perform code analysis. This substitution transforms the mechanical process of manual code review into an automated intelligent system that can detect errors without human involvement
2Reliability
If conventional error checking tools are used, then code can be analyzed, but the process is time-consuming and inefficient
Solution Approach 1:
The chatbot system provides continuous automated error checking capability that can operate without interruption. Once initiated, the system continuously analyzes code, detects errors, and provides fixes in an uninterrupted workflow, eliminating the intermittent and batch-processing nature of conventional tools, thereby significantly improving productivity
Solution Approach 2:
The system performs preliminary error detection and analysis automatically before code deployment or execution. By proactively identifying and flagging errors in advance through automated chatbot interaction, the system prevents errors from reaching production environments, improving both reliability and efficiency by catching issues early in the development cycle
3Extent of automation
If automated error checking is implemented, then manual intervention is reduced, but complexity of the system increases
Solution Approach 1:
The chatbot serves as an intermediary layer between the user and the complex underlying machine learning models and code analysis engines. This intermediary provides a simple, familiar conversational interface that masks the complexity of the automated error checking system, allowing high automation to be achieved without exposing users to system complexity
Solution Approach 2:
The chatbot system is designed as a universal platform that can handle multiple programming languages, code types, and error scenarios through a single interface. This multi-functional design consolidates what would otherwise require multiple specialized tools into one system, reducing overall system complexity while maintaining high automation across diverse code bases
Data Source
AI summary
Apparatuses, systems and methods are provided for checking code for errors. The apparatuses, systems and methods may send a target code and a prompt for code checking to a machine learning (ML) chatbot to cause the ML chatbot to check the target code for errors. The apparatuses, systems and methods may determine whether there is an error in the target code based at least partially on a response from the ML chatbot. The apparatuses, systems and methods may, responsive to determining that there is an error in the target code, determine, via an interaction with the ML chatbot, a solution to fix the error. The apparatuses, systems and methods may analyze the solution to determine a number of at least one of (i) a set of steps or (ii) a set of interactions required by the solution. The apparatuses, systems and methods may, responsive to determining that the number exceeds a predetermined threshold, fix the error by implementing the solution with respect to the target code.