ML-Based Artifact Naming for Context-Aware Code Generation
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Solution Overview
Problem
Existing software development tools often assign generic placeholder names to newly created code artifacts, such as 'NewMethod', which lack context-specific meaning and hinder developer understanding and productivity.
Innovation Solution
A trained machine learning model generates context-specific and meaningful names for software artifacts based on their content, such as action-object or condition-action formats, filling gaps left by autocompletion mechanisms and providing names that distinguish artifacts from others in the category.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generic placeholder names are assigned to newly created code artifacts, then the naming process is simple and fast, but developer understanding and productivity are hindered
Solution Approach 1:
The system enables artifacts to name themselves automatically by extracting meaningful information from their own content, context, and relationships with other artifacts, eliminating the need for manual naming while generating context-specific names
Solution Approach 2:
The patent replaces the manual mechanical process of developer naming with an automated machine learning-based system that analyzes artifact content, code context, and relationships to generate meaningful names automatically
2Loss of information
If manual naming is required for each artifact, then names can be highly context-specific and meaningful, but the time and effort required increases significantly
Solution Approach 1:
Artifacts automatically generate their own context-specific names by analyzing their content, relationships with other artifacts, and code context, eliminating manual naming time while preserving contextual meaning
Solution Approach 2:
The system uses feedback from artifact content analysis, code context examination, and relationship mapping to generate accurate names, continuously improving naming quality based on the specific context of each artifact
3Speed
If autocompletion mechanisms are used, then common names are provided quickly, but meaningful context-specific names are not generated
Solution Approach 1:
The system generates names specific to each artifact by analyzing its content and context automatically, providing both speed and contextual meaning without relying on pre-defined autocompletion lists
Solution Approach 2:
Each artifact receives a customized name based on its specific content, context, and relationships rather than a generic name, ensuring local quality and relevance while maintaining fast generation through automation
Data Source
AI summary
Some embodiments use specialized machine learning models to generate computing system artifact names which reflect actions, states, conditions, or other aspects of artifact functionality. Artifact creation mechanisms such as method extraction mechanisms, test creation mechanisms, and template extraction mechanisms are enhanced with functionality by which they obtain and suggest meaningful generated names for new artifacts instead of merely prompting users with placeholder names. Specified artifact name formats and name styles are matched during name generation, thereby improving code maintainability and software development efficiency. Generated names are automatically and proactively derived from artifact source code by code summarization, conditional statement location, algorithm category recognition, or name format matching, for example. Generated names are also derived from natural language descriptions in comments and other documentation. Naming gaps left by autocompletion mechanisms are reduced. Inconsistencies between updated artifacts and their names are detected and remedied.