Code Analysis Tool Using Vector Embeddings for Defect Detection
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Solution Overview
Problem
Automated tools struggle to detect subtle coding defects such as resource leaks, synchronization issues, and insufficient input validation, which are often missed or misunderstood by developers, and there is a need for efficient methods to identify non-defective code similar to defective code.
Innovation Solution
A hierarchical index system is used to organize and retrieve relevant code samples, allowing for the detection of similar non-defective code by mapping non-defective samples with metadata tokens and utilizing similarity comparison functions to find closest matches, enabling detectors to identify and highlight better coding practices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If automated tools are used to detect coding defects, then detection coverage is improved, but detection precision deteriorates for subtle defects
Solution Approach 1:
The patent introduces an intermediary system that uses language model embeddings to bridge the gap between automated defect detection and precision. The system embeds both the defective code and candidate corrective code into a shared vector space, allowing for semantic similarity comparison that goes beyond traditional pattern matching. This intermediary embedding layer enables the system to detect subtle defects with higher precision by comparing the semantic meaning of code rather than just syntactic patterns.
Solution Approach 2:
The patent creates copies of the defective code in the form of language model embeddings (vector representations). Instead of directly analyzing the original code for defects, the system creates semantic copies through embedding and compares these copies to identify similar code patterns and potential defects. This copying approach allows the system to detect subtle defects by comparing semantic representations rather than raw code text.
2Productivity
If code samples are organized for retrieval, then retrieval efficiency is improved, but system complexity worsens
Solution Approach 1:
The patent replaces traditional mechanical indexing and search systems with a language model-based embedding system. Instead of organizing code samples using manual categorization, keywords, or hierarchical indexes, the system automatically transforms code into vector embeddings and uses similarity search in the vector space. This substitution of mechanical organization with semantic representation dramatically improves retrieval efficiency while keeping system complexity manageable through the use of pre-trained language models.
3Measurement precision
If similarity comparison is performed on code, then detection accuracy is improved, but computational cost worsens
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing language model embeddings for code samples before they are needed for defect detection. The embeddings are calculated in advance and stored in a database, so when defect detection is needed, the system only needs to perform lightweight similarity comparisons in the vector space rather than computing embeddings from scratch. This preliminary computation significantly reduces the computational cost during actual detection while maintaining high accuracy.
Data Source
AI summary
Techniques for analyzing code are described. In some instances, a code analysis service is to perform a series of comparisons, one or more per path segment of an index structure of non-defective code samples, using a token derived from a defective code segment of the stored code, to determine one or more paths in the index, wherein each path is to point to code that is similar to the defective code segment; and provide, in response to the series of comparisons, at least one of: a location of the code determined to be similar to the defective code segment and the code determined to be similar.