FOSSology

Mentors: Student: Saksham Mishra

The current reuse workflow in FOSSology provides a powerful mechanism to copy license clearing decisions from a previously analyzed upload to a newer version. However, this process remains largely mechanical and opaque, offering little insight into what has changed between versions after reuse is applied. As a result, users must manually inspect differences to understand the impact of reuse, which becomes increasingly difficult for large codebases and frequent updates. To address this limitation, this project proposes the development of an Enhanced Reuser Agent that adds an intelligent analysis layer on top of the existing reuse workflow. The agent runs automatically after scanners complete and reuse decisions are applied, comparing the previously cleared upload (v1) with the new upload (v2). It generates insights such as a diff-aware file tree, license comparison, aggregated statistics, and a risk-level summary. In addition, a Smart Reuse (Suggested Matches) mechanism automatically identifies and recommends similar previously-cleared uploads, reducing the manual effort required to search for suitable reuse candidates. By providing a clear and interactive view of structural and license-level changes, along with proactive reuse suggestions, the system enables users to better understand the outcome of reuse and efficiently refine decisions where needed. This transforms reuse into a more transparent and insight-driven process, reducing manual effort and improving overall usability while remaining fully compatible with the existing FOSSology workflow.

Mentors: Student: Tiyasa Kundu

This project aims to complete and productionize the FOSSology UI by finalizing the ongoing migration to Next.js and ensuring full integration with REST API v2. Building on the previous GSoC work, the project focuses on implementing the remaining pages, standardizing the interface using a reusable design system with ShadCN and Tailwind CSS, and improving overall consistency, accessibility, and user experience. The application will be upgraded to the latest Next.js version to leverage advancements in performance, caching, and rendering. Additionally, the project will emphasize modular architecture, component reusability, and comprehensive Storybook documentation. Thorough testing, codebase refactoring, and detailed developer and user documentation will be completed to ensure scalability, maintainability, and ease of future contributions.

Mentors: Student: Krrish Biswas

FOSSology's CycloneDX agent targets spec 1.4 and omits license texts, copyright notices, and scanner evidence. This project upgrades it to 1.7, adds a formal evidence layer separating scanner findings from human clearing decisions, implements CycloneDX import support, and adds full PHPUnit test coverage.

Mentors: Student: rycerzes

This project will enhance FOSSology's license intelligence stack by revamping both Nirjas and Atarashi into a faster, ML-driven, and language-aware pipeline. Nirjas will move from regex-oriented parsing to a Tree-sitter architecture with language packs across 248 languages, improving comment extraction fidelity in real repositories. Atarashi will be upgraded from broad full-file matching toward retrieval-plus-classification that emphasizes high-signal fragments, because keywords matter for license intent (for example, phrases like permission grant, redistribution, warranty disclaimer, copyleft, and patent terms are often decisive). To support both components, I have already upgraded the Minerva dataset pipeline to generate train/validation/test datasets for Nirjas and Atarashi using merged inputs from ScanCode LicenseDB, FOSSology licenseRef.json endpoint, and The Stack Smol comment corpus, followed by augmentation, LLM-generated hard-negative synthesis, and near-dedup. A sample dataset for benchmarking and integration testing is ready, while the full-scale dataset build is in progress. For modeling, Nirjas will use a model2vec classifier distilled from strong sentence-transformer teachers selected using MTEB/RTEB task performance together with our internal evaluations; the same model2vec distillation path is also planned for Atarashi, with a fine-tuned quantized embedding baseline retained as a fallback depending on speed/accuracy trade-offs. The expected outcome is a production-ready path to more accurate, scalable, and maintainable license detection across diverse codebases.

Mentors: Student: Harshit Gandhi

Managing compliance today means juggling dozens of independent reports - SPDX, CycloneDX, CLIXML, ReadmeOSS - generated separately for each component, with no way to merge, track, or evolve them as a whole. This project introduces a Report Aggregator Tool for FOSSology and SW360 that consolidates multiple compliance reports into a single, authoritative document. Users will be able to select reports across formats, merge them through a transparent aggregation UI, inspect and edit the combined data with full traceability, and propagate changes incrementally when source reports are updated. With seamless integration into FOSSology’s agent framework and SW360’s project hierarchy, this tool will transform fragmented compliance artifacts into unified, audit-ready documentation - making FOSSology an even more complete solution for meeting regulation like the EU Cyber Resilience Act