Sliding-Window Vulnerability Reduction for Incomplete Code
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Solution Overview
Problem
Traditional vulnerability detection systems fail to effectively identify code vulnerabilities in syntactically incomplete or missing dependency code during development, requiring complete code with all dependencies for accurate detection, leading to inefficiencies and delayed vulnerability identification.
Innovation Solution
Utilizing an AI-based code vulnerability detection tool with a neural architecture that applies a sliding window to extract input code portions, enabling real-time detection and correction of vulnerabilities and malicious logic, even in incomplete code, without relying on complete dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional vulnerability detection systems are used, then detection accuracy is maintained for complete code, but detection capability is lost for syntactically incomplete code
Solution Approach 1:
The code is divided into smaller segments using a sliding window approach, allowing the detection system to process syntactically incomplete code portions independently. Each window contains a subset of code tokens that can be analyzed without requiring the entire codebase to be syntactically complete, thus enabling detection in incremental development scenarios.
Solution Approach 2:
A neural architecture serves as an intermediary between the input code and traditional vulnerability detection methods. This neural model preprocesses and contextualizes code segments, enabling accurate vulnerability detection even when the code is syntactically incomplete by learning patterns from training data that includes incomplete code examples.
2Measurement precision
If complete code with all dependencies is required for detection, then detection accuracy is improved, but development efficiency is reduced due to delayed detection
Solution Approach 1:
Vulnerability detection is performed preliminarily on code segments as they are being written, rather than waiting for complete code with all dependencies. The sliding window approach enables detection to occur on partial code portions during development, allowing developers to identify and fix vulnerabilities early in the coding process before the entire codebase is complete.
Solution Approach 2:
The system performs partial vulnerability detection by analyzing only the code segments contained within each sliding window, rather than requiring analysis of the entire codebase. This partial action approach enables detection to proceed with incomplete code, trading some comprehensiveness for the ability to detect vulnerabilities incrementally during development.
3Adaptability or versatility
If sliding window approach is used to process incomplete code, then detection capability is improved, but computational complexity increases
Solution Approach 1:
The codebase is segmented into smaller windows, reducing the computational burden on each individual detection operation. Instead of analyzing the entire codebase at once, the neural architecture processes smaller subsets of tokens, which decreases the complexity of each inference step while maintaining overall detection coverage through multiple overlapping windows.
Solution Approach 2:
Each sliding window performs partial analysis of only the code segments it contains, rather than attempting to analyze the entire codebase. This partial action reduces the computational complexity of each detection operation, allowing the system to process incomplete code efficiently by focusing computational resources on local code segments.
Data Source
AI summary
Example solutions performing software code vulnerability reduction. An input code portion is extracted from input software code. The input code portion may be syntactically incomplete and/or syntactically incorrect. A code vulnerability is detected in the input code portion. A correction of the code vulnerability is made, and an output code portion is generated including the correction. In some examples, a code vulnerability detection tool takes, as input, the output from a code completion tool. The output is thus annotated or corrected in real-time, as a user is developing the code.