LV-Compat: License Compatibility Analysis

• LV-Compat is an automated pipeline that detects license incompatibilities in software dependency networks, addressing both standard and modified open-source licenses.
• It leverages diff-based analysis and LLM-powered parsing to perform precise, term-level comparisons between canonical licenses and their variants.
• LV-Compat significantly improves detection rates and efficiency, integrating with CI/CD workflows to mitigate legal risks in large-scale ecosystems such as PyPI.

LV-Compat refers to an automated, scalable pipeline for detecting license incompatibilities within software dependency networks, with particular emphasis on handling both standard open-source licenses and their textually and substantively modified variants. Developed in the context of empirical analysis of the Python Package Index (PyPI) ecosystem, LV-Compat addresses the unique compliance challenges posed by license variants—modified standard licenses and custom alternatives—by systematically analyzing package dependencies at ecosystem scale (Xu et al., 19 Jul 2025).

1. Background and Motivation

Open-source software derivative works inherit legal conditions from their own licenses as well as from all dependencies. In modern ecosystems such as PyPI, package maintainers often adopt standard licenses (SPDX license texts) but may introduce deliberate or accidental textual modifications, resulting in what the paper terms “license variants.” While only 2% of variants involve substantive changes, these can have a disproportionate compliance impact, with 10.7% of downstream dependencies found license-incompatible. Historically, standard license compliance tools do not account for these non-canonical texts, limiting their effectiveness and potentially introducing significant legal risk when integrating or redistributing software that inherits such variants.

LV-Compat was created to address the prevalence and impact of license variants by enabling fine-grained, automated compatibility analysis across entire dependency graphs, significantly improving upon prior tools that relied upon simplistic or canonical-only license checks.

2. Architecture and Technical Approach

LV-Compat is constructed as a multi-stage pipeline with deep integration with LV-Parser—an upstream module that parses license texts at a granular (term) level using diff-based analysis and LLMs. The workflow comprises:

• Dependency Resolution:

Collection of direct and transitive dependencies for every analyzed package, scaling up to millions of releases (over 6 million in PyPI).

• License Extraction:

Automated harvesting and segmentation of license files, including disambiguation of complex or composite (multi-license) texts with LLM assistance. In cases lacking explicit license files, LLMs fill gaps using metadata.

• Fine-Grained License Parsing:

LV-Parser compares candidate licenses against a curated set of 63 canonical licenses using a combination of dense retrieval (LLM embedding-based) and textual diffing. A high similarity score (threshold 0.9) triggers further examination, where only non-matching “diff” clauses are retained for term-level analysis.

• Legal Term Mapping:

Each clause is mapped to legal concepts (e.g., copyright, copyleft, permitted use, exceptions) from a knowledge base, with the most suitable term value assigned via LLM-powered multi-label classification and similarity matching against annotated exemplars.

• Compatibility Analysis:
  • Secondary Compatibility: Whether the downstream package’s license allows relicensing all components under a common downstream license.
  • Combinative Compatibility: Whether original licensing conditions of each component can be maintained when combined.
  • Specialized routines are applied for exceptions, such as per-clause analysis for copyleft exceptions. If neither axis permits compatibility, the package is flagged as incompatible.

A representative scoring component for similarity, as used in LV-Parser, is expressed by:

$S = \frac{c(A \cap B)}{c(A \cup B)}$

where $S$ is the match score, $A$ the set of clause signatures from the candidate license, $B$ from the canonical SPDX license, and $c(\cdot)$ counts unique signatures.

3. Evaluation and Empirical Performance

LV-Compat and LV-Parser were quantitatively evaluated on a dataset of 74 annotated licenses—20 near-canonical and 54 substantively variant. Key results include:

• Parsing accuracy: LV-Parser achieved 0.936 average accuracy.
• Computational efficiency: Utilizing diff-based targeted analysis and clause-level dispatch, LV-Parser reduced LLM query costs per license by 30% compared to a full-text baseline.
• Compatibility detection: Testing on 75 downstream releases, LV-Compat identified 52 packages with incompatible dependencies (vs. 10 by a prior tool, SILENSE), and flagged 116 incompatible dependency pairs (vs. 30 by SILENSE). Random validation confirmed 51 of 52 cases, yielding a precision of 0.98.

LV-Compat, therefore, found 5.2 times more incompatible packages compared to existing methods with equivalent or higher accuracy.

4. Practical Implications and Integration

LV-Compat’s robust compatibility assessment enables several advancements in software compliance practices:

• Enhanced Risk Mitigation:

By systematically identifying even subtle, legally significant license divergences at scale, it reduces the risk of inadvertent noncompliance during code integration or redistribution.

• Improved Coverage:

The ability to resolve and assess complete dependency graphs—including third-party, dual-license, or license-variant components—offers significantly more comprehensive legal due diligence than canonical-only approaches.

• CI/CD Pipeline Integration:

The pipeline can be incorporated into continuous integration and deployment workflows, automating monitoring and enforcement of license policies across evolving codebases and dependencies.

• Resource Efficiency:

Diff-based pre-filtering avoids redundant full-license analyses and LLM overuse, enabling practical operation even over massive ecosystems.

5. Limitations and Challenges

Recognized limitations of LV-Compat include:

• Ecosystem Focus:

The empirical paper and evaluation are centered on popular PyPI packages; findings may not generalize fully to less popular packages or other ecosystems such as Maven or npm.

• Recall and Ambiguity:

While precision is high, recall is difficult to certify due to inherent ambiguity in legal clauses and open-ended or poorly specified licensing conditions, especially with “usage limitation” terms and complex exceptions.

• Ground Truth Establishment:

Manual annotation dependency for training and validation introduces subjectivity; differing interpretations by legal experts can affect reference data quality.

• Edge Terms and Subtle Variants:

Certain non-canonical and open-list license dimensions remain difficult to annotate or classify reliably, challenging both automated and manual methods.

6. Future Directions

Areas identified for further development include:

• Ecosystem Expansion:

Extending methods and pipelines to other package ecosystems to validate adaptability to different licensing cultures and variant forms.

• Improved Recall:

Incorporating deeper interpretive models, enriched legal ontologies, and perhaps expert-in-the-loop validation to capture edge cases and ambiguous terms.

• Source-level Analysis:

Enhancing detection of licenses embedded directly within source code or comments, not just in distribution files.

• Refinement of Legal Concept Models:

Continued development of more nuanced knowledge bases for emerging and hybrid licensing regimes, especially around combinative copyleft mechanisms and exceptions.

7. Summary Table: LV-Compat Core Features and Results

Feature	Description/Result	Performance/Impact
License variant handling	Diff-based, LLM-aided parsing & mapping	0.936 parsing accuracy (LV-Parser)
Compatibility analysis	Term-level legal reasoning, combination and exception logic	5.2× improved detection rate; 0.98 precision
Computational efficiency	Clause-diff prefiltering, LLM query reduction	30% fewer LLM queries per license
Integration	Ecosystem-scale, CI/CD compatibility, modular	Scales to >6M PyPI releases
Ecosystem	PyPI (Python), with intention to generalize	Practical compliance risk reduction

LV-Compat establishes a new benchmark for automated, accurate, and efficient license compatibility analysis in software supply chains where license variants are common. By leveraging advanced parsing and legal concept modeling, it demonstrably improves both coverage and precision, although the interpretability of more exotic or ambiguous license modifications remains a subject for future research (Xu et al., 19 Jul 2025).