Software Component Identification Using File-Type Embeddings
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Solution Overview
Problem
Existing software component identification methods struggle in environments lacking comprehensive manifest data, limiting the accuracy and reliability of identifying software components.
Innovation Solution
Employ federated learning with file-type-specific embeddings to generate composite embeddings for software packages, leveraging neural networks and similarity analysis to identify components across diverse file types, even when manifest data is absent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manifest-based identification methods are used, then identification speed is fast, but identification accuracy deteriorates in environments lacking comprehensive manifest data
Solution Approach 1:
The system segments the software package identification problem into multiple file-type-specific embedding tasks. Different neural network models are trained for different file types (source code, binaries, configuration files, documentation), and their embeddings are aggregated to form a composite package embedding. This segmentation allows each model to specialize in specific file characteristics, improving overall identification accuracy without requiring a single overly complex model.
Solution Approach 2:
The system changes the parameter representation from traditional manifest-based metadata to neural network-generated embeddings. By transforming file contents into high-dimensional vector representations that capture semantic and structural characteristics, the system can identify software packages even when manifest data is incomplete or absent, significantly improving identification accuracy in environments lacking comprehensive manifest information.
2Measurement precision
If file-type-specific embeddings are generated for all files, then identification precision improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-training specialized neural network models for different file types before actual software package identification. These pre-trained models can quickly generate embeddings without requiring extensive processing during identification. Additionally, the system pre-computes embeddings for files in the software package and aggregates them, avoiding repeated processing and reducing overall identification time while maintaining high precision.
3Adaptability or versatility
If federated learning is used across distributed systems, then model generalization improves, but communication overhead increases
Solution Approach 1:
The system extracts only the essential embedding parameters and gradient information from distributed training data, rather than transferring entire models or raw data between systems. Each distributed system processes local data independently to generate file-type-specific embeddings, and only the condensed embedding representations and training gradients are communicated during federated learning rounds. This extraction approach maintains model generalization across diverse environments while significantly reducing communication overhead and energy consumption.
Data Source
AI summary
A software package is received which encapsulates a plurality of files which are then extracted. For those files that cannot be identified, an embedding is generated. Different file types can employ different embedding generating techniques. Using this embedding, a set of potential software packages to the file from which the embedding was generated is calculated. This calculation can use, for example, one or more similarity analysis techniques relative to a set of mapped packages and embeddings (i.e., a set of known software packages and embeddings corresponding to known constituent files, etc.). The calculation can be used to determine an identity of the software package. The determined identity can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.