Deep Learning Code Review Automation
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Solution Overview
Problem
Manual code review processes are time-consuming and inefficient, as they rely on human effort to identify errors and vulnerabilities in source code, often missing issues like race conditions, malware, memory leaks, and buffer overflows.
Innovation Solution
An automatic code review process using a deep learning model that learns from historical code reviews to identify relevant patterns in source code snippets, generating probability scores to highlight areas requiring attention, thereby reducing manual effort and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual code review is performed, then code quality and vulnerability detection are improved, but time consumption and reviewer effort increase
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated deep learning model that analyzes code snippets. The model uses neural networks to process code syntax, semantics, and historical review data, automatically generating relevance scores without human intervention, thus eliminating the trade-off between manual effort and review quality
Solution Approach 2:
The system creates a digital copy of the code review process by training a deep learning model on historical code review data. The model learns from past reviewer comments and decisions, replicating expert review capabilities in an automated system that can process code without consuming human time while maintaining review quality
2Measurement precision
If deep learning model is trained on historical code review data, then review accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the code review task into distinct components: code snippet extraction, syntax analysis, semantic understanding, and relevance scoring. Each component is handled by specialized neural network modules that process specific aspects of code analysis independently, making the complex system more manageable and maintainable while improving accuracy through focused specialization
Solution Approach 2:
The system introduces an intermediary training phase where historical code review data is processed to create a trained deep learning model. This intermediary step separates the complexity of learning from the complexity of execution, allowing the model to absorb complex patterns during training while presenting a simplified interface during actual code review operations
3Productivity
If automatic code review is implemented, then productivity is improved, but ability to detect complex vulnerabilities decreases
Solution Approach 1:
The system performs preliminary action by pre-training the deep learning model on extensive historical code review data before deployment. This preliminary training enables the model to learn complex vulnerability patterns and reviewer expertise in advance, so that during actual code review operations, it can detect complex vulnerabilities with the same efficiency as simple issues without requiring human reviewers
Data Source
AI summary
A code review process utilizes a deep learning model trained on historical code reviews to automatically perform peer or code review of a source code file. The deep learning model is able to predict the code reviews relevant to a source code snippet by learning from historical code reviews. The deep learning model is trained on pairs of code snippets and code reviews that are relevant to each other and pairs of code snippets and code reviews that have no relation to each other. The deep learning model is data driven thereby not relying on pre-configured rules which makes the model adaptable to different review environments.