AI Inactive Code Resolution in Multi-Cloud Environments
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Solution Overview
Problem
In cloud computing environments, there is a need for an efficient and automated method to identify and remove inactive, unused, or redundant code, as manual review is time-intensive and impractical with increasing code bases.
Innovation Solution
A system utilizing artificial intelligence (AI) powered by machine learning models and neural networks analyzes cloud data, such as system logs and memory dumps, to identify inactive code, computes a confidence level, and initiates automated remediation processes when the confidence level exceeds a defined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review methods are used to identify and remove inactive code, then code cleanup can be performed with high accuracy, but the process becomes time-intensive and impractical with increasing code bases
Solution Approach 1:
The patent replaces manual mechanical code review processes with an automated AI-based system that uses machine learning models and neural networks to analyze code data, system logs, and execution traces. This substitution enables the system to process large code bases efficiently while maintaining high accuracy in identifying inactive code, directly resolving the contradiction between review accuracy and time consumption.
2Productivity
If automated removal processes are implemented without confidence verification, then processing speed increases, but the risk of removing active code increases
Solution Approach 1:
The patent implements a feedback mechanism where the AI system computes a confidence level for each identified inactive code instance and compares it against a predefined threshold. Only code instances exceeding the threshold are automatically removed, while those below the threshold are flagged for manual review. This feedback loop ensures high-speed automated processing while maintaining reliability by preventing erroneous removal of active code.
3Measurement precision
If comprehensive analysis of all code is performed to ensure accurate identification, then identification accuracy improves, but computational resources are excessively consumed
Solution Approach 1:
The patent applies partial action by analyzing only the most relevant code attributes and execution patterns that are most indicative of inactive code, rather than performing exhaustive analysis of all code characteristics. The AI model focuses on key features such as execution frequency, recent modification timestamps, and dependency analysis, enabling accurate identification while consuming fewer computational resources.
4Productivity
If automated remediation is performed without threshold verification, then resource optimization is maximized, but false positives increase and system stability is compromised
Solution Approach 1:
The patent performs preliminary action by establishing a confidence threshold before automated remediation occurs. The system pre-qualifies code instances by verifying they meet the minimum confidence criteria before any removal action is taken. This preliminary verification step ensures that only high-confidence inactive code is removed, maximizing resource optimization while preventing false positives that could compromise system stability.
Data Source
AI summary
A system is provided for intelligent automatic resolution of inactive computer code in a multi cloud environment. In particular, the system may comprise an artificial intelligence powered apparatus that may use one or more machine learning models and/or neural networks to analyze data from a cloud computing environment, such as system logs, memory dumps, real-time status queries, and/or the like. Based on analyzing the data, the system may identify inactive, unused, and/or redundant code within the cloud computing environment. The system may then compute a confidence level associated with the identification of the inactive code. Upon detecting that the confidence level has exceeded a defined threshold, the system may automatically initiate one or more remediation processes, such as deletion or modification of the inactive code. In this way, the system may provide a way to automatically remove inactive code from the cloud computing environment.