Multi Repository Evolution Analyzer

• Multi Repository Evolution Analyzer is a framework that tracks, visualizes, and quantitatively analyzes software evolution across repositories using static analysis and normalization.
• It employs advanced methodologies such as co-occurrence network analysis, entropy-based metrics, and clone detection to reveal trends in code reuse and modularity.
• Interactive visualizations and real-time monitoring empower researchers and practitioners to uncover architectural shifts and guide strategic planning.

A Multi Repository Evolution Analyzer is an analytical tool or framework designed to assess, track, and visualize the evolution of software projects across multiple repositories. Such analyzers provide empirical, quantitative, and structural insights into the trajectories of code, dependencies, components, and development practices as observed over time and across organizational boundaries. Modern approaches leverage large-scale static analysis, normalized representations of code/artifacts, and configurable analytic pipelines to systematically report ecosystem-scale software evolution—including trends in reuse, modularization, dependency propagation, and architectural shifts.

1. Foundational Data Collection and Normalized Database Construction

At the core of a Multi Repository Evolution Analyzer is an extensive, curated database that captures software evolution events, components, and dependencies at scale. In the case of neural network (NN) software, this construct is exemplified by the Neural Network Bill of Material (NNBOM) database (Ren et al., 24 Sep 2025):

• Repository Gathering and Filtering: An initial corpus (for example, 78,243 PyTorch-related GitHub repositories) is filtered to exclude trivial or low-utility projects using criticality scores, resulting in a high-fidelity dataset (e.g., 55,997 repositories, 93,647 versions).
• Component Extraction: Static analysis is performed to extract:
  • Third-Party Libraries (TPLs): Identified through parsing configuration files and import statements.
  • Pre-trained Models (PTMs): Detected via invocations to model hubs (like Hugging Face) using customized AST analyses.
  • Neural Network Modules: All classes inheriting from core framework base classes (e.g., torch.nn.Module in PyTorch). Incremental parsing and symbol tables ensure efficient processing across versions.
• Module Normalization and Clone Detection: Modules are normalized (removal of comments, variable renaming, literal replacement) before an incremental hash is computed. Type-1 and Type-2 code clones are grouped into clone families, enabling accurate tracking of code reuse.
• Dependency Construction: If modules from the same clone family appear in different repositories with a temporal sequence, a dependency is inferred, supporting cross-repository lineage tracing.

This approach enables the database to act as a foundation for further analytical tasks: trend analysis, dependency graphs, and cross-repository mapping.

2. Analytical Methodologies for Repository Evolution

A Multi Repository Evolution Analyzer applies a range of quantitative and structural methodologies that reveal multi-dimensional evolution patterns:

• Scale and Decomposition Analysis: Macro-level metrics (module/LOC counts per repository and version) are tracked to highlight ecosystem trends, such as the shift towards fine-grained modularity (rise in module count with stable average module size) (Ren et al., 24 Sep 2025).
• Component Co-Occurrence Network Analysis: For each epoch (e.g., annually), co-usage networks are created, where nodes are components and edges indicate joint appearance across repositories. Community detection algorithms (e.g., Louvain) reveal macro-structural shifts in architectural or framework paradigms.
• Entropy-Based Domain Diffusion Metrics: Cross-domain usage of shared components is measured via average entropy:

$$H_{(i)} = -\sum_k p_k \log(p_k), \quad \bar{H} = \frac{1}{N}\sum_{i=1}^{N} H_{(i)}$$

where $p_k$ is the proportion of clone family $i$'s modules in domain $k$, and $\bar{H}$ indexes generalization and diffusion across task domains.

• Overlap Ratio Analysis: The code overlap between domains $A$ and $B$ is computed as:

$P_{AB} = \frac{|A \cap B|}{|A \cup B|}$

providing a normalized metric for the breadth of module reuse.

• Real-Time Repository Monitoring: Newly added repositories are analyzed for their impact on component networks, reporting the number of novel vs. reused TPLs, PTMs, and NN modules.

These methods permit longitudinal, ecosystem-scale evolution mapping, facilitating targeted analyses of architectural reuse, diffusion trends, and the spread of new paradigms (e.g., the rise of Transformer-based models in neural network research).

3. Visualization and Interactive Exploration

Given the combinatorial explosion of multi-repository datasets, scalable, temporally-aware visualization tools such as EvoScat (Serbout et al., 14 Aug 2025) have been developed to support multi repository evolution analysis. Key features include:

• Density Scatterplots: Each artifact’s event trajectory (e.g., commits, metric updates) is rendered as a vertical track of colored dots, enabling side-by-side comparison of thousands of artifacts over decades.
• Configurable Timelines: Temporal axes may be absolute or normalized (relative to start/end/median), allowing for alignment and comparison of artifacts with varying lifespans.
• Artifact Sorting and Filtering: Artifacts can be sorted (e.g., by first/last update), and low-information artifacts filtered out, revealing clusters, development rhythms, and clone/fork occurrences.
• Interactive Color Mapping: Dot colors are mapped to attributes such as commit year, artifact class, or metric changes to visually encode evolutionary properties.
• Preprocessing and Data Scaling: By compressing data and supporting interactive zooming, EvoScat renders millions of events and supports exploration of temporal dynamics, such as punctuated evolution or technical lag between repositories.

This visualization paradigm enables empirical researchers to uncover macro- and micro-level patterns, such as synchronized changes, evolutionary bottlenecks, and outlier behaviors across repository populations.

4. Functionalities and Use Cases

The Multi Repository Evolution Analyzer provides actionable insights for both maintainers and developers:

• Ecosystem Health Monitoring: Real-time tracking of newly introduced components, the emergence or obsolescence of large clone families, and shifts in dominant architectural practices (e.g., mass adoption of Transformers or diffusion of general-purpose modules across CV, NLP, and generative domains) (Ren et al., 24 Sep 2025).
• Dependency Graph Evolution: Through static and temporal analysis, the analyzer elucidates how new projects alter inter-repository dependency networks, identifying components that achieve widespread adoption and their potential propagation effects.
• Component Reuse Detection: By identifying clone families and co-occurrence communities, the analyzer facilitates understanding of best practices and design patterns within the ecosystem, supporting recommendations to developers and maintainers.
• Cross-Domain Influence Discovery: The entropy and overlap ratio metrics reveal the extent to which architectural ideas or code fragments traverse application boundaries—a crucial consideration for framework designers and research on code transferability.
• Guidance for Strategic Planning: The reporting of stable module scales, increasing modularity, and rising cross-domain entropy provides empirical basis for library/framework support decisions and resource allocation in community-driven projects.

5. Design Principles and Limitations

Leading-edge Multi Repository Evolution Analyzers, such as the approach implemented with the NNBOM database, are designed for extensibility, automation, and scalability:

• Static Analysis-Driven: AST parsing and normalization is incrementally performed for large codebases, ensuring timely update and efficient resource use.
• Clone-Family Based Dependency Abstraction: By grouping functionally identical code across repositories, the analyzer moves beyond superficial identifier comparison to structural reuse and propagation tracing.
• Indexing and Searchability: The resulting multi-level indices (by version and by module) permit fine-grained queries, trend analysis, and rapid data retrieval.
• Interpretive Scope: The framework is optimized for identifying high-level modular trends, cross-domain diffusion, and propagation of broadly reused patterns.

A key limitation is imposed by the reliance on static code structure, normalization, and hash-based detection. For code that evolves via deep semantic transformations or for ecosystems with limited code reuse, the efficacy of clone family detection may be lower. Another challenge is that findings are most robust when codebases adhere to clear modular boundaries and consistently declare dependencies.

6. Empirical Insights and Evolutionary Trends

Empirical results underscore several long-term trends observable via multi repository evolution analysis (Ren et al., 24 Sep 2025):

• Shift Toward Fine-Grained Modularity: The count of neural network modules per repository version increases over time, but average module size remains steady (circa 50 LOC), indicating a move towards decomposing complex functionality into smaller, reusable units.
• Increasing Component Reuse: Co-occurrence networks and clone family analysis reveal the formation of large, persistent communities—some dominated by contemporary architectures (e.g., Transformers) replacing legacy patterns.
• Rising Cross-Domain Generality: The average entropy metric increases, reflecting the spread of modules across multiple application areas. Notably, code and design previously confined to NLP proliferate into CV and generative modeling settings.
• Diffusion Waves: The transition from convolution-based to Transformer-based modules illustrates large-scale architectural paradigm shifts, which are detectable using the analyzer’s network and entropy measures.

These trends inform predictions about the ongoing evolution of both open-source and industrial software ecosystems.

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A Multi Repository Evolution Analyzer, as exemplified by the NNBOM-based system, provides a data-driven, configurable infrastructure for systematizing evolutionary software analysis at scale. By integrating rigorous static analysis, cross-repository normalization, advanced metrication (e.g., entropy, overlap), temporal visualization, and real-time monitoring, it delivers a comprehensive perspective on software evolution—enabling empirical research, informed engineering practice, and strategic ecosystem stewardship (Ren et al., 24 Sep 2025).