Graph-Based Semi-Supervised Dockerfile Generation
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Solution Overview
Problem
The construction of Dockerfiles for containerization is manual, lacks standards, and security aspects, and requires specific files for each application, with existing solutions failing to provide direct methods for automatic generation and addressing syntactic/semantic errors.
Innovation Solution
A graph-based semi-supervised file generation method that filters high-quality repositories, splits data into subsets, generates feature vectors, constructs level graphs, and uses semi-supervised learning to automatically generate Dockerfiles, reducing manual effort and improving security by leveraging a small set of labeled files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual construction of Dockerfiles is used, then security and standards can be maintained, but productivity is reduced and time is lost
Solution Approach 1:
The system enables self-service by automatically generating Dockerfiles through machine learning models that learn from existing high-quality files, eliminating the need for manual construction while maintaining security standards through supervised learning on curated datasets
Solution Approach 2:
Manual mechanical construction of Dockerfiles is replaced with an automated machine learning system that uses neural networks and graph-based representations to generate files automatically, substituting human effort with intelligent automation
2Productivity
If existing automatic generation solutions are used, then productivity is improved, but they fail to provide direct methods for generation and address syntactic/semantic errors
Solution Approach 1:
The system performs preliminary action by curating and filtering high-quality Dockerfiles before training the model, pre-processing the training data to ensure only accurate, standards-compliant files are used, which prevents propagation of errors in generated files
Solution Approach 2:
The system incorporates feedback mechanisms by using semi-supervised learning where the model learns from both labeled high-quality data and unlabeled data, continuously improving generation accuracy through feedback from the curated dataset and error correction
3Quantity of substance
If large amounts of existing files are used for training, then data availability is improved, but noise increases and file quality decreases
Solution Approach 1:
The system extracts only the high-quality subset of Dockerfiles from the larger dataset through filtering and curation processes, removing noisy or low-quality files while retaining sufficient training data volume to train effective machine learning models
Solution Approach 2:
The system applies local quality by creating a curated subset of high-quality Dockerfiles specifically for training purposes, ensuring that the training data has uniformly high quality standards rather than relying on the variable quality of all available files
Data Source
AI summary
A processor may collect a set of repositories. The processor may filter the set of repositories based on one or more predefined rules. The processor may obtain a high-quality subset from the set of repositories. The high-quality subset may include one or more datum. The processor may split the one or more datum into a high-quality dataset and an uncertain dataset.