Case Study 2: The Open-Source Compliance Challenge

Overview

Organization: GreenLeaf Analytics, a data analytics startup with 40 developers building an open-source-friendly analytics platform.

Product: "Canopy" — a data pipeline and visualization platform. The core engine is proprietary, but the company contributes heavily to open-source projects and releases several of their own tools under the Apache 2.0 license.

Challenge: After adopting AI coding assistants across the engineering team, an external contributor discovered that portions of GreenLeaf's open-source tools contained code strikingly similar to code from a GPL-licensed project. This discovery threatened both their proprietary product and their open-source community standing.

Timeline: Discovery to resolution over 6 weeks

The Discovery

Marcus Chen, a software engineer at an unrelated company, was reviewing GreenLeaf's open-source data connector library — released under the Apache 2.0 license — for potential use in his own project. While reading through the Parquet file parser, he noticed something familiar. The code's structure, variable naming conventions, and even some comments bore a strong resemblance to code he had contributed to an open-source project called DataForge, which was licensed under the GPL v3.

Marcus opened a GitHub issue with a detailed comparison:

Title: Possible GPL-licensed code in Apache 2.0 repository

I've identified significant similarities between the Parquet parser in
this repository (src/connectors/parquet_reader.py) and code from the
DataForge project (github.com/dataforge/core, src/parsers/parquet.py).

DataForge is licensed under GPL v3. This repository is Apache 2.0.
If these similarities constitute a derivative work, there may be a
license compliance issue.

Specific similarities:
- Function decomposition pattern (lines 45-120 here vs lines 200-275 in DataForge)
- Variable naming: chunk_buffer, metadata_cache, row_group_handler
- Error handling structure with identical fallback logic
- Comments on lines 67 and 89 are near-identical to DataForge comments

I'm not claiming intentional copying — this may be an AI tool reproducing
training data patterns. But the similarities are substantial enough to
warrant review.

The issue gained traction quickly. Within 24 hours, it had 47 comments, was trending on developer forums, and had been picked up by a technology news site with the headline "AI Coding Assistant May Have Introduced GPL Code Into Apache Project."

The Assessment

GreenLeaf's engineering lead, Sarah Okonkwo, immediately assembled a response team including their lead developer, a contract attorney specializing in open-source licensing, and their community manager.

Technical Analysis

The team conducted a thorough technical comparison using both automated tools and manual review.

Automated analysis. They ran Scancode Toolkit against their entire codebase, comparing against a database of known open-source code. The results identified three categories of matches:

Manual expert review. The attorney engaged a technical expert to perform a detailed comparison of the four high-similarity files against their apparent GPL sources. The expert's analysis was nuanced:

File 1: parquet_reader.py (the file Marcus identified) - 73% structural similarity to DataForge's parquet.py - Identical variable names in 8 of 12 core functions - Two comments were near-verbatim matches - Assessment: "Substantial similarity that would likely be considered a derivative work"

File 2: csv_streaming.py - 58% structural similarity to a GPL-licensed CSV parsing library - Similar algorithmic approach but different variable names - No identical comments - Assessment: "Moderate similarity — could be independent creation of a common approach, but the specific implementation choices suggest a common source"

File 3: schema_validator.py - 65% structural similarity to a GPL-licensed schema validation tool - Several identical utility functions - Assessment: "The utility functions are likely derived, but the overall structure shows independent design"

File 4: compression_handler.py - 81% structural similarity to a GPL-licensed compression library - Nearly identical error handling patterns and fallback logic - Assessment: "High likelihood of derivation"

Root Cause Investigation

The team investigated how the similar code had entered their codebase. The investigation revealed a pattern:

1. AI tool adoption without compliance processes. GreenLeaf had adopted AI coding assistants six months earlier with enthusiasm but without establishing license compliance workflows. Developers were encouraged to "use AI to move fast."

2. Accelerated development sprint. The four problematic files were all created during a three-week sprint to add new data connectors to the platform. Under time pressure, developers relied heavily on AI-generated code.

3. No license scanning in CI/CD. GreenLeaf's CI/CD pipeline included linting, testing, and security scanning, but no license compliance scanning. AI-generated code was not subject to any additional review.

4. Code review gaps. During the sprint, code reviews focused on functionality and performance, not on whether the code might be derived from licensed sources. Reviewers did not have tools or training to identify license compliance issues.

5. AI tool configuration. The AI tools in use did not have code-matching filters enabled (features that block suggestions closely matching known public code). The developers were unaware these features existed.

Legal Analysis

The attorney's analysis identified several interrelated legal issues:

GPL compliance. If the four high-similarity files were derivative works of GPL-licensed code, GPL v3's copyleft provisions required that the files (and potentially the broader works they were part of) be licensed under GPL v3 or a compatible license. Apache 2.0 is not compatible with GPL v3 in the reverse direction — you cannot take GPL code and relicense it under Apache 2.0.

Impact on proprietary product. Three of the four problematic files also existed in the proprietary Canopy product, where they had been adapted from the open-source connectors library. If the GPL applied, GreenLeaf would need to either: - Remove the affected code from the proprietary product, or - Release the proprietary product (or the affected portions) under the GPL, or - Obtain a separate license from the GPL code's copyright holders

Community trust. Beyond the legal issues, GreenLeaf's reputation as a responsible open-source participant was at stake. Their Apache 2.0 projects were used by other companies and developers who relied on the permissive licensing. Discovering GPL-encumbered code in an Apache project would undermine trust.

Third-party exposure. Companies and developers who had already incorporated GreenLeaf's Apache-2.0-licensed connectors into their own projects could face GPL compliance obligations if those connectors contained GPL-derived code. GreenLeaf could face claims from these third parties.

The Response

GreenLeaf's response unfolded in three parallel tracks: immediate containment, community communication, and long-term remediation.

Track 1: Immediate Containment (Week 1)

Day 1: Acknowledgment. Sarah posted a response to Marcus's GitHub issue within hours:

"Thank you for this thorough analysis, Marcus. We take license compliance seriously and are investigating immediately. We've assembled a team including legal counsel to assess the situation. We'll provide updates as we learn more."

Day 2: Preliminary assessment complete. The technical analysis confirmed the similarity concerns. GreenLeaf added a prominent notice to the affected repository:

"NOTICE: We are investigating potential license compliance issues in this repository. We recommend that users exercise caution when incorporating code from the following files into their projects until this investigation is resolved: [list of files]."

Day 3: Temporary mitigation. GreenLeaf created a branch that replaced the four high-similarity files with stub implementations that raised NotImplementedError. This allowed dependent projects to pin to a clean version while full replacements were developed.

Track 2: Community Communication (Weeks 1-2)

GreenLeaf published a blog post explaining the situation transparently:

What happened: During a recent development sprint, we used AI coding assistants to accelerate the creation of data connectors. Our investigation has confirmed that the AI tools generated code with substantial similarities to GPL-licensed projects. This code was incorporated into our Apache 2.0 repositories without adequate license compliance review.

What we're doing: We are rewriting all affected code from scratch, without AI assistance, to ensure clean provenance. We are implementing license scanning in our CI/CD pipeline. We are reaching out to the copyright holders of the GPL projects to discuss the situation.

What this means for users: If you have incorporated the affected files into your project, we recommend replacing them with the clean versions we will release. We do not believe the moderate- and low-similarity files pose compliance risks, but we are scanning them as a precaution.

The blog post was well-received by the community. Several commenters praised GreenLeaf's transparency and swift response. Marcus Chen himself commented: "This is exactly the right way to handle this. Thank you for taking it seriously."

Track 3: Remediation (Weeks 2-6)

Clean-room reimplementation. GreenLeaf adopted a clean-room process for rewriting the affected code:

1. A developer who had not seen the original GPL code or the AI-generated code wrote a functional specification for each affected module.
2. A different developer implemented the specification from scratch, without using AI tools and without referring to the original AI-generated code or the GPL source.
3. A third developer reviewed the clean implementation to verify it met the specification and did not resemble the GPL source.

This clean-room approach, while time-consuming, provided strong legal protection against infringement claims.

Outreach to GPL copyright holders. GreenLeaf's attorney contacted the maintainers of the DataForge project and the other affected GPL projects. The conversations were constructive:

• DataForge's maintainers appreciated the transparency and accepted GreenLeaf's plan to replace the affected code. They declined to pursue any legal action given GreenLeaf's good-faith response.
• One of the other GPL projects was maintained by a single developer who was actually a GreenLeaf user. He was surprised but understanding, and offered to dual-license the specific functions under both GPL and MIT to resolve the issue.
• The third GPL project's maintainers were less accommodating initially but ultimately accepted GreenLeaf's remediation plan after reviewing the clean-room implementations.

Comprehensive codebase scan. GreenLeaf ran a full license compliance scan across all repositories — both open-source and proprietary. The scan used Scancode Toolkit, supplemented by FOSSA for ongoing monitoring. Results:

• The 4 critical files were being rewritten (on track)
• Of the 12 medium-similarity files, 3 were flagged for precautionary rewriting and 9 were cleared after manual review
• The 38 low-similarity matches were all determined to be common programming patterns, not derived from specific sources
• No additional critical issues were found in the proprietary codebase

Timeline of clean replacements:

Systemic Changes

Beyond the immediate remediation, GreenLeaf implemented systemic changes to prevent recurrence.

New Compliance Infrastructure

License scanning in CI/CD. Every pull request now triggered an automated license compliance scan. Pull requests with potential license matches were automatically flagged and required additional review from a designated license compliance reviewer.

AI code provenance tracking. Developers were required to tag AI-generated code with a standardized comment marker (# AI-GENERATED or # AI-ASSISTED). A Git hook enforced that tagged files had corresponding entries in a provenance log.

Code-matching filters. All AI coding tools were configured to enable code-matching filters that blocked suggestions closely resembling known public code. Developers were trained on what these filters did and why they were important.

Monthly compliance audits. A monthly automated scan compared the full codebase against known open-source code databases, catching any issues that might have slipped through PR-level checks.

Updated Development Practices

AI usage guidelines. GreenLeaf created detailed guidelines for using AI coding tools, including: - Enable code-matching filters on all tools - Never use AI tools for code destined for Apache-licensed projects without running a license scan - When AI generates code that looks suspiciously well-structured or uses unusual variable names, search online for the function signature before using it - For critical modules, prefer human-written code with AI used only for boilerplate and tests

Training program. All developers completed a 2-hour training session covering: - How AI models can reproduce training data - Open-source license types and their requirements - How to use license scanning tools - The clean-room implementation process - GreenLeaf's specific compliance workflows

Contribution guidelines update. GreenLeaf's open-source contribution guidelines were updated to require: - A Developer Certificate of Origin (DCO) sign-off on all commits - Disclosure of AI tool usage in pull request descriptions - License scan results included in pull request reviews

Organizational Accountability

License compliance role. GreenLeaf created a part-time "license compliance champion" role, rotating among senior developers quarterly. This person was responsible for reviewing flagged pull requests, maintaining the license scanning configuration, and staying current with open-source licensing developments.

Incident response playbook. GreenLeaf documented their response to this incident as a playbook for future license compliance issues, including templates for community communications, legal assessment checklists, and clean-room implementation procedures.

Outcomes

Six Months Later

Community Impact

The incident, while embarrassing, ultimately strengthened GreenLeaf's community standing. Their transparent handling became a reference case in discussions about AI coding and open-source compliance. Several other open-source projects adopted similar compliance workflows, citing GreenLeaf's approach.

Marcus Chen, who originally filed the issue, became a regular contributor to GreenLeaf's projects. He also wrote a blog post praising the response: "GreenLeaf showed that doing the right thing and doing it publicly is the best way to build trust in open source."

Lessons Learned

1. AI-generated code is not provenance-free. Code generated by AI may carry license obligations from the training data. Treating AI output as original code is a compliance risk.

2. License scanning is essential, not optional. Automated license compliance scanning should be a standard part of every CI/CD pipeline, especially when AI tools are in use.

3. Transparency builds trust. GreenLeaf's open communication about the issue strengthened their community relationships. Attempting to hide or minimize the problem would have been far more damaging.

4. Clean-room implementation is the gold standard. When you need to replace potentially infringing code, the clean-room process provides the strongest legal defense. It is time-consuming but worth the investment.

5. Prevention is cheaper than remediation. The six-week remediation effort consumed approximately 400 developer-hours. The compliance infrastructure that prevents recurrence required approximately 80 developer-hours to implement. The math strongly favors prevention.

6. AI tool configuration matters. Enabling code-matching filters and other compliance features in AI tools is a simple step that significantly reduces risk. Many developers are unaware these features exist.

7. Open-source license compliance is everyone's responsibility. Creating a compliance champion role and training all developers ensured that license awareness was embedded in the culture, not siloed in a legal team.

Discussion Questions

1. If GreenLeaf had not been alerted by an external contributor, how might they have discovered the license compliance issue? What proactive measures would have caught it earlier?

2. The clean-room reimplementation process is thorough but expensive. Under what circumstances might a less rigorous approach (such as simply rewriting the flagged functions with AI assistance while checking for similarity) be acceptable?

3. One of the GPL project maintainers was initially uncooperative. What legal and practical options would GreenLeaf have had if the maintainer demanded GPL licensing of the entire Canopy platform?

4. How should GreenLeaf handle the third-party companies that may have incorporated the affected Apache-licensed code into their own projects? What obligations, if any, does GreenLeaf have to notify them?

5. Could GreenLeaf pursue any claims against the AI tool provider for generating code that created the compliance issue? What would such a claim look like, and what are the likely obstacles?

Code Reference

See code/case-study-code.py for Python implementations of tools described in this case study, including the clean-room process tracker, the provenance logging system, and the license similarity analyzer.