AI Server for Automated Code Style Correction
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Solution Overview
Problem
When multiple developers work on a program, differences in coding styles and error check tools lead to unnecessary work in modifying code styles, decreasing productivity due to human error and inconsistencies.
Innovation Solution
A server with a processor and AI model that identifies and modifies code style errors based on predetermined rules, using code information from user terminal devices to input and correct style inconsistencies, thereby reducing human intervention and improving code uniformity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple developers work on a program with different coding styles and error check tools, then diverse perspectives and skills are utilized, but unnecessary work is required to modify code styles and productivity decreases
Solution Approach 1:
An AI model is introduced as an intermediary between developers with different coding styles. The model learns from code samples and error information to automatically modify code styles, serving as a mediator that translates between different developer conventions without requiring manual intervention from each developer.
Solution Approach 2:
The code style modification system performs self-service by automatically detecting code style errors and correcting them without human intervention. The AI model learns from training data and autonomously applies corrections, eliminating the need for developers to manually modify code styles.
2Stability of the object's composition
If manual code style modification is performed by developers, then code style consistency can be achieved, but human error increases and time is wasted
Solution Approach 1:
The manual mechanical process of code style modification by developers is replaced with an automated AI-based system. The AI model processes code style corrections algorithmically, eliminating human error and ensuring consistent application of coding standards without relying on human judgment or attention.
3Measurement precision
If different error check tools are used by developers, then comprehensive error detection is possible, but code style uniformity becomes difficult to maintain
Solution Approach 1:
The AI model serves multiple functions: it detects code style errors, learns from different coding conventions, and applies unified corrections. This multi-functional approach allows the system to handle diverse error detection requirements while maintaining code style uniformity across the entire codebase.
Solution Approach 2:
Multiple error check tools and coding style guidelines are merged into a single AI model that learns from all inputs. The model integrates various error detection capabilities and coding standards into one unified system that applies consistent corrections across all code.
4Stability of the object's composition
If code style rules are strictly enforced, then code uniformity is improved, but developers perform unnecessary work to modify styles
Solution Approach 1:
The AI model performs preliminary learning from code samples and error information before actual code modification is needed. By pre-learning coding styles and error patterns, the system is prepared to automatically correct issues without requiring developers to spend time on style modifications.
Data Source
AI summary
A control method of a server is provided. The method includes acquiring code information about a program, identifying at least one error with respect to a code style included in the code information based on a predetermined code style rule, acquiring at least one error information with respect to the identified code style, and modifying the code style by inputting the code information and the error information to an artificial intelligence model in which the code style rule is trained.