AI Lint Error Resolution Using RAG-Guided Code Recommendations

Solution Overview

Problem

Verification engineers spend significant time fixing Lint errors in verification software, which are unrelated to the intended functionality but can introduce unexpected errors, and there is a need for improved techniques to identify and resolve these errors efficiently.

Innovation Solution

A generative AI-based tool infrastructure that generates software code to correct Lint errors, utilizing a retrieval augmented generation (RAG) framework with a large language model (LLM) and an index to provide high-quality code recommendations, allowing users to review and accept or edit the suggestions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If verification engineers manually fix Lint errors, then code quality can be improved, but development time and cost increase significantly

Engineering Contradiction:

Improvecode qualityVSAvoiddevelopment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service by allowing the AI model to automatically generate correction code for Lint errors. The verification engineer simply needs to review and accept the suggested fixes, transforming a manual task into an automated self-correcting process that maintains code quality while reducing time investment

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

An AI-based intermediary system is introduced between the Lint error detection and the correction process. This intermediary analyzes the error, generates appropriate corrections using retrieval-augmented generation, and presents options to the engineer, thereby bridging the gap between error identification and resolution while reducing manual effort

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If verification engineers spend more time fixing Lint errors, then code quality improves, but productivity decreases

Engineering Contradiction:

Improvecode qualityVSAvoiddevelopment productivity

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system enables self-service by allowing the AI model to automatically generate correction code for Lint errors. The verification engineer simply needs to review and accept the suggested fixes, transforming a manual task into an automated self-correcting process that maintains code quality while reducing time investment

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of reading, understanding, and fixing Lint errors is replaced with an AI-based system that automatically analyzes errors and generates corrections. This substitution eliminates the need for engineers to manually process each error while maintaining or improving code quality standards

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Difficulty of detecting and measuring

If a traditional linter is used to identify Lint errors, then errors can be detected, but the process remains manual and time-consuming

Engineering Contradiction:

Improveerror detectionVSAvoidtime to resolve errors

Core Design Contradiction:

Difficulty of detecting and measuringVSLoss of time

Solution Approach 1:

An AI-based intermediary system is introduced between the Lint error detection and the correction process. This intermediary analyzes the error, generates appropriate corrections using retrieval-augmented generation, and presents options to the engineer, thereby bridging the gap between error identification and resolution while reducing manual effort

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The manual mechanical process of reading, understanding, and fixing Lint errors is replaced with an AI-based system that automatically analyzes errors and generates corrections. This substitution eliminates the need for engineers to manually process each error while maintaining or improving code quality standards

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260072648A1Generative ai-based tool for identification and resolution of lint errors

Publication Date: 2026.03.12 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Embodiments of the present disclosure include a software solution for resolving Lint errors in a verification software application. A RAG index can be queried for a RAG entry that is similar to a given Lint error detected in the source code. The RAG entry returned from the query can in turn be utilized in generating a prompt for a Large Language Model (LLM). The LLM receives the prompt and generates a result that contains replacement software code that is subsequently presented to the user as a possible solution to correct the Lint error in the verification software.