Code Vector Representations for Semantic Similarity
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Solution Overview
Problem
Deep learning architectures applied to source code analysis fail to accurately capture the semantics or meaning of the code, only capturing syntax, which hinders the identification of semantically similar code snippets written in different ways.
Innovation Solution
A method involving a machine learning model that collects training sets of code snippets for semantic similarity and dissimilarity, generates vector representations using algorithms like Doc2vec or Word2vec, and trains the model to minimize loss values based on vector distances to capture semantic similarity, enabling the identification of semantically similar code snippets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If deep learning architectures are applied to source code analysis, then the analysis capability is improved, but the ability to capture semantic meaning deteriorates
Solution Approach 1:
The patent segments code analysis into two distinct processing streams: one for syntax analysis using deep learning architectures and another for semantic analysis using vector representations. This segmentation allows each stream to optimize for its specific purpose while avoiding the limitation where syntax-based approaches fail to capture semantic meaning.
Solution Approach 2:
The patent introduces vector representations as an intermediary between code syntax and semantic meaning. These vectors serve as a bridge that translates syntactic structures into semantic representations, enabling the system to capture both the structural and meaningful aspects of code simultaneously.
2Measurement precision
If vector representations are generated to capture semantic similarity, then the identification accuracy of semantically similar code is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-training the machine learning model on large corpora of code to learn semantic relationships before actual code similarity analysis. This pre-training phase creates the vector representation capability that can be efficiently applied to subsequent code comparisons without requiring complex real-time computation.
Solution Approach 2:
The patent changes the parameter space by transforming code from discrete syntactic tokens into continuous vector representations. This parameter transformation enables semantic similarity to be measured through standard distance metrics in vector space, simplifying the computational approach while maintaining high identification accuracy.
Data Source
AI summary
A method, system and computer program product for obtaining vector representations of code snippets capturing semantic similarity. A first and second training set of code snippets are collected, where the first training set of code snippets implements the same function representing semantic similarity and the second training set of code snippets implements a different function representing semantic dissimilarity. A vector representation of a first and second code snippet from either the first or second training set of code snippets is generated using a machine learning model. A loss value is generated utilizing a loss function that is proportional or inverse to the distance between the first and second vectors in response to receiving the first and second code snippets from the first or second training set of code snippets, respectively. The machine learning model is trained to capture the semantic similarity in the code snippets by minimizing the loss value.