Neural Network Bill of Material

• NNBOM is a structured inventory that catalogues neural network components, including third-party libraries, pre-trained models, and custom modules.
• It systematically extracts components from versioned repositories, enabling quantitative analysis of scale, modularity, and reuse trends.
• Insights from NNBOM drive best practices in neural network software engineering by highlighting cross-domain adaptability and evolving architectural patterns.

The Neural Network Bill of Material (NNBOM) defines a structured, comprehensive inventory tailored to the unique modular and evolutionary properties of neural network (NN) software. Differing from traditional Software Bill of Materials (SBOMs) and conceptual AI Bill of Materials (AIBOMs), an NNBOM catalogs all relevant components—third-party libraries, pre-trained models, and custom modules—comprising NN projects, thus enabling large-scale quantitative analysis of their development, reuse patterns, and cross-domain integration. By systematically extracting and organizing these components across repository versions, the NNBOM framework supports analyses of software scale, modularity, and architectural evolution, directly informing maintainers, developers, and researchers about trends and best practices in neural network software engineering (Ren et al., 24 Sep 2025).

1. Concept and Distinction

The NNBOM is distinguished from generic SBOMs by its explicit focus on NN-specific artifacts:

• Component Coverage: NNBOM enumerates third-party libraries (TPLs), pre-trained models (PTMs), and neural network modules (i.e., Python classes derived from torch.nn.Module), whereas SBOMs focus solely on traditional software dependencies.
• Modularity: NNBOM captures the modular organization characteristic of NN software, including the prevalence of model sharing and intricate re-use of modules, which are insufficiently represented in SBOMs or high-level AIBOM discussions.
• Analytical Utility: It supports empirical and quantitative analysis of the evolution of NN software, including trends in adoption of architectural innovations (e.g., the transition to Transformer-based modules), which is infeasible with component-agnostic SBOM frameworks (Ren et al., 24 Sep 2025).

2. Database Construction: Large-scale Dataset Extraction

Construction of a comprehensive NNBOM database entails several systematic steps:

• Repository Collection: Begin with open-source repositories (e.g., 78,243 PyTorch-tagged repositories from GitHub). Curate by filtering for relevance (excluding tutorials, demos, example-only projects) and module presence.
• Quality Control: Discard repositories with no detectable TPL, PTM, or custom NN modules; apply criticality scoring to exclude bottom-tier repositories (bottom 5% by activity/maintenance).
• Versioning: For each repository, distinct versions are obtained via Git tags or branch snapshots.
• Component Extraction:
  • TPLs: Aggregate dependencies via configuration files (e.g., setup.py, requirements.txt) and import analysis.
  • PTMs: Identify usage through custom parsers for model hubs (e.g., Hugging Face, PyTorch Hub) and deployment framework conventions.
  • NN Modules: Extract all class definitions deriving from torch.nn.Module using AST symbol table analysis.
• Scale: The resulting dataset comprises 55,997 repositories and 93,647 repository versions, each associated with a full NNBOM listing (Ren et al., 24 Sep 2025).

3. Empirical Findings: Evolution and Structural Trends

Systematic analysis of the NNBOM database elucidates major trends in neural network software:

Software Scale Evolution

• The absolute number of TPLs, PTMs, and NN modules per repository increases steadily over time.
• While early rapid growth in TPL usage plateaus, PTM invocation (present in ∼7.6% of repositories) and custom module creation continue to accelerate.
• The mean module size stabilizes at ∼50 lines of code, but the number of modules per version rises, evidencing increasing software decomposition.

Component Reuse Evolution

• Annual co-occurrence networks (nodes: unique NNBOM components; edges: frequent co-usage) reveal a progression from small, isolated functional modules (e.g., activation functions) toward larger, densely interconnected communities anchored by advanced architectures (e.g., ResNet, Inception, Transformers).
• Reuse patterns shift from simple functional units (2017), to CNNs (2018–2020), and to Transformer-based modules (2021–2024), underlining an architectural migration toward scalability and generality.

Inter-Domain Dependency

• Average module reuse entropy ($\bar{H}$) rises from 0.157 (2017) to 0.485 (2024), indicating modules are reused across a broader spectrum of domains (NLP, vision, generative modeling).
• Modules demonstrating high cross-domain adaptability exhibit extended lifespans, suggesting that such entropy is a proxy for long-term viability of component designs.

Summary Table: Key NNBOM Analytical Dimensions

Dimension	Example Metric / Method	Main Finding
Software Scale	Module count, mean module size	Growth in modules per version; stable module size
Component Reuse	Co-occurrence networks	Shift from simple to complex (CNN, Transformer) modules
Inter-domain Entropy	$\bar{H} = (1/N) \sum_i H_i$	Increasing cross-domain reuse and module lifespan

4. Prototype Tools for Practical Application

The NNBOM framework supports two application classes:

• Multi-repository Evolution Analyzer: Tracks the aggregate evolution of TPLs, PTMs, and modules across repositories, visualizing component proliferation and dependency expansion to inform ecosystem-level trend identification and policy.
• Single-repository Component Assessor and Recommender: Assesses the modular composition of a given repository, distinguishing newly introduced vs. reused elements and recommending components or similar repositories based on co-usage patterns; for example, analyzing the test-time-training/ttt-video-dit repository exposes the distribution of novel and inherited modules, while suggesting alternative architectures from the NNBOM database (Ren et al., 24 Sep 2025).

5. Implications for Software Engineering and Research

The NNBOM establishes an empirical foundation for understanding and engineering NN software:

• Process Transparency: By offering quantitative snapshots of component structure and evolution, NNBOM aids maintainers in effective component selection, reuse, and modularization.
• Best Practices: Analysis of growing modularity and entropy guides best-practice recommendations, such as designing for cross-domain adaptability to enhance software longevity.
• Trend Anticipation: Identification of rising architectures (e.g., Transformers) and their spread across application areas enables developers to anticipate and adopt emerging paradigms.
• Workflow Integration: Prototype tooling demonstrates how NNBOM insights can be incorporated into daily development workflows for repository maintenance, code review, and dependency management (Ren et al., 24 Sep 2025).

6. Methodological Rigor and Limitations

The extraction and analysis pipeline underpins the reliability of the NNBOM:

• AST-based Module Extraction: Ensures comprehensive capture of all NN module subclasses, supporting longitudinal and cross-sectional studies of architectural developments.
• Repository Curation: Quantitative criticality-based filtering enhances dataset quality by omitting outdated or trivial repositories.
• Metric Validity: Empirical metrics (e.g., entropy, line counts, reuse network density) are directly computed from versioned source code data, enabling reproducible, large-scale analysis.

A plausible implication is that such methodology can be generalized to other domains with highly modular or plugin-based architectures; however, the current focus on PyTorch-based repositories suggests broader applicability would require adaptation for alternative NN frameworks and ecosystems.

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In sum, the Neural Network Bill of Material provides an empirically driven, systematically organized compendium of NN software artifacts, enabling unprecedented analysis of their modular composition, reuse patterns, and evolutionary dynamics across the open-source landscape. This structured approach informs research, engineering practice, and future-proofing efforts in the rapidly evolving field of neural network software (Ren et al., 24 Sep 2025).