Add GSoC 2026 report for ScanCode required phrases

docs/source/archive/gsoc-toc.rst

@@ -15,6 +15,7 @@ GSoC 2026
    :maxdepth: 2
 
    gsoc/reports/2026/vulnerablecode_sampurna
+   gsoc/reports/2026/scancode_required_phrases_kaushik
 
 GSoC 2025
 ---------

docs/source/archive/gsoc/reports/2026/scancode_required_phrases_kaushik.rst

@@ -0,0 +1,279 @@
+=============================================================================
+ScanCode-Toolkit: Mark required phrases for rules automatically using NLP/AI
+=============================================================================
+
+| Mentee: **Kaushik Kumar R**
+| GitHub: `Kaushik-Kumar-CEG <https://github.com/Kaushik-Kumar-CEG>`_
+| LinkedIn: `Profile <https://www.linkedin.com/in/kaushik-kumar-ceg/>`_
+| Repository: `ScanCode Required Phrases
+  <https://github.com/aboutcode-org/scancode-required-phrases>`_
+| Official GSoC project page: `Project Link
+  <https://summerofcode.withgoogle.com/programs/2026/projects/K6CQtnjP>`_
+| GSoC Proposal: `Proposal Link
+  <https://docs.google.com/document/d/1qXYTrIoF4Tn5q-dPMQicCFZ0NKbLj9jYN18ipyPyqZI/edit?usp=sharing>`_
+| Model: `Hugging Face
+  <https://huggingface.co/Kaushik-Kumar-CEG/scancode-required-phrases-deberta-bioes-crf-hardened>`_
+
+Overview
+--------
+
+Required phrases help ScanCode avoid weak or partial license matches. They are
+marked with ``{{...}}`` inside a license rule. If the phrase is missing from the
+scanned text, that rule does not match.
+
+Many rules do not have these markers yet. Existing ScanCode commands can copy
+known phrases to similar rules, but they need a phrase to start with. The work
+in `scancode-toolkit#3924
+<https://github.com/aboutcode-org/scancode-toolkit/pull/3924>`_ provides the
+rule APIs used here. This project adds an NLP model that can suggest a phrase
+directly from rule text.
+
+A wrong phrase can hide a valid license match, so model output cannot be written
+to rules without checks and review. The work includes dataset generation, model
+training, prediction validation, a maintainer review command and a small
+ScanCode Toolkit wrapper.
+
+The two main flows are:
+
+.. code-block:: text
+
+    marked ScanCode rules -> dataset -> training -> public model
+    unmarked rule -> prediction -> ScanCode checks -> review -> rule update
+
+Work Completed
+--------------
+
+Dataset and composite rules
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The dataset command reads eligible ScanCode ``.RULE`` files and converts
+existing ``{{...}}`` markers into BIOES token labels. Text outside the markers
+is labelled ``O``.
+
+The split is deterministic. Rules with rarer license expressions stay together
+in one split. Rules from common expressions are assigned by a hash of their
+identifiers to keep the splits balanced. After tokenizer alignment checks, the
+training run used 8,265 training rules, 1,044 validation rules and 1,004 test
+rules.
+
+A separate command handles composite expressions. It uses phrases already known
+for the individual license keys and only updates a composite rule when every
+key has a safe, non-overlapping match.
+
+Model training and inference
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The selected model is based on DeBERTa v3 large with BIOES labels and a constrained
+conditional random field (CRF) decoder. BIOES marks the start, inside, end and
+single-token cases for each phrase. The CRF keeps the decoded label sequence
+valid.
+
+Training and test evaluation are kept separate. Test evaluation is opt-in and
+was run only for the selected model. The training command also reloads and
+checks the saved model before treating the output as complete.
+
+The model is public on Hugging Face. It is pinned to revision
+``11215925b0f9b64cfcfbbb5492b52d6aeb5a572b`` for the default command.
+
+.. list-table:: Model results
+   :widths: 40 30 30
+   :width: 100%
+   :header-rows: 1
+
+   * - Metric
+     - Validation
+     - Test
+   * - Precision
+     - 0.8981
+     - 0.8748
+   * - Recall
+     - 0.9035
+     - 0.8784
+   * - F1
+     - 0.9008
+     - 0.8766
+   * - Exact match
+     - 0.8563
+     - 0.8197
+
+Precision, recall and F1 are strict span metrics. The validation and test runs
+had no invalid BIOES paths.
+
+Review and rule updates
+^^^^^^^^^^^^^^^^^^^^^^^
+
+The main maintainer command is ``add-model-required-phrases``. It can review one
+rule, a directory of rules or eligible rules installed with ScanCode Toolkit.
+It shows the prediction, score, validation result, nearby text and the exact
+rule diff before asking for a decision.
+
+A review can approve, reject, edit or defer a phrase. Sessions are saved as
+JSONL and can be resumed without loading the model again. Predict-only mode can
+write machine-readable JSON without creating a review session or changing any
+rule.
+
+Batch mode requires explicit score thresholds. It does not write unless
+``--yes`` is supplied. ``--dry-run`` always prevents rule-file changes. After
+installed rules are changed, the command tells the maintainer to rebuild the
+ScanCode license index.
+
+Safety checks
+^^^^^^^^^^^^^
+
+Every prediction is treated as a candidate. ScanCode's own rule APIs decide
+whether the phrase can be inserted. The command blocks phrases that overlap
+protected content such as URLs and referenced filenames. It also rejects
+ambiguous repeated text, conflicting spans and predictions cut by model input
+limits.
+
+Before applying a reviewed session, the command checks every path and file hash
+again. It prepares the complete update first, then writes each changed rule once
+using atomic replacement. File permissions and line endings are kept. A rule
+with a pending phrase is left unchanged.
+
+Toolkit integration
+^^^^^^^^^^^^^^^^^^^
+
+The model code and its optional dependencies live in the
+``scancode-required-phrases`` package. ScanCode Toolkit only gets a small source
+checkout wrapper. This keeps normal ScanCode installation and license scanning
+free from ML dependencies. The wrapper imports the package command and passes
+its arguments through.
+
+A second Toolkit pull request contains ten manually reviewed rule changes from
+a fixed sample of 30 rules. The sample was selected before prediction and was
+checked against the final train, validation and test data.
+
+The model returned 37 candidate phrases across 22 of the 30 rules. Twenty-one
+passed the insertion checks and 16 were blocked. Ten clear license-specific
+phrases were kept. Each updated rule still matched its original full text, while
+text missing the required phrase did not match that rule.
+
+Using the Command
+-----------------
+
+Install the inference dependencies from a package checkout:
+
+.. code-block:: console
+
+    python -m pip install ".[inference]"
+
+Review one rule:
+
+.. code-block:: console
+
+    add-model-required-phrases --rule path/to/example.RULE
+
+Run read-only prediction and save JSON:
+
+.. code-block:: console
+
+    add-model-required-phrases --rule path/to/example.RULE \
+        --predict-only --json predictions.json
+
+Preview a batch without writing rule files:
+
+.. code-block:: console
+
+    add-model-required-phrases --all --batch \
+        --auto-score 0.90 --review-score 0.70 --dry-run
+
+Linked Pull Requests
+--------------------
+
+.. list-table::
+   :widths: 8 62 30
+   :width: 100%
+   :header-rows: 1
+
+   * - No.
+     - Work
+     - Link
+   * - 1
+     - Dataset and composite required phrase commands
+     - `scancode-required-phrases#1
+       <https://github.com/aboutcode-org/scancode-required-phrases/pull/1>`_
+   * - 2
+     - Model training, export and read-only inference
+     - `scancode-required-phrases#2
+       <https://github.com/aboutcode-org/scancode-required-phrases/pull/2>`_
+   * - 3
+     - Model prediction and rule integration
+     - `scancode-required-phrases#3
+       <https://github.com/aboutcode-org/scancode-required-phrases/pull/3>`_
+   * - 4
+     - Human review workflow for model predictions
+     - `scancode-required-phrases#4
+       <https://github.com/aboutcode-org/scancode-required-phrases/pull/4>`_
+   * - 5
+     - ScanCode Toolkit command wrapper
+     - `scancode-toolkit#5267
+       <https://github.com/aboutcode-org/scancode-toolkit/pull/5267>`_
+   * - 6
+     - Reviewed model predictions for ten sample rules
+     - `scancode-toolkit#5330
+       <https://github.com/aboutcode-org/scancode-toolkit/pull/5330>`_
+
+Testing
+-------
+
+The package tests cover dataset generation, model loading, prediction decoding,
+rule validation, review sessions, stale files, dry runs, batch thresholds,
+atomic writes and command output. The complete package suite passed 322 tests.
+Documentation, style, source distributions and wheels were also checked.
+
+The Toolkit wrapper passed its focused tests with a fake package and with the
+real public model. The ten evidence rules passed ScanCode rule validation and
+the focused required-phrase tests.
+
+Earlier Work
+------------
+
+Before GSoC, I worked on a few ScanCode license detection cases. These helped me
+understand rule data and how small rule changes affect matching.
+
+* `Add a rule for CKSource Holding copyright
+  <https://github.com/aboutcode-org/scancode-toolkit/pull/4649>`_
+* `Fix incorrect license detection in dragon4.c
+  <https://github.com/aboutcode-org/scancode-toolkit/pull/4654>`_
+* `Fix JCalendar LGPL detected as GPL
+  <https://github.com/aboutcode-org/scancode-toolkit/pull/4880>`_
+
+Current Limits and Next Steps
+-----------------------------
+
+The model can miss a phrase or suggest a boundary that is not useful. Scores are
+not enough to approve a change, which is why review and ScanCode validation stay
+part of the workflow. Long rules may also be truncated by the model and are not
+approved automatically.
+
+The package pull requests and both Toolkit pull requests are still open. After
+they are reviewed and merged, I plan to prepare version ``0.1.0`` and help with
+the PyPI release. I will keep maintaining the dataset tools, training pipeline,
+model and review command. Maintainer feedback and reviewed rules can be used for
+future training runs.
+
+One later area to study is `scancode-toolkit#3945
+<https://github.com/aboutcode-org/scancode-toolkit/issues/3945>`_. It considers
+ranking matches with required phrases instead of always filtering matches that
+miss them. The phrases produced by this project can still be used if that policy
+changes.
+
+Closing Thoughts
+----------------
+
+GSoC taught me much more than how to train an NLP model. I learned how ScanCode's
+license rules and matching code work, how to turn an experiment into a package
+and why review matters for even a small rule change.
+
+The weekly community calls were one of my favourite parts of the project. I
+could show my progress, hear where an idea was weak and fix it before going too
+far. Thanks to `Ayan Sinha Mahapatra
+<https://github.com/AyanSinhaMahapatra>`_, `Philippe Ombredanne
+<https://github.com/pombredanne>`_ and `Dennis Clark
+<https://github.com/DennisClark>`_ for their time, feedback and patience.
+
+I am very glad I got to work with the `AboutCode community
+<https://github.com/aboutcode-org>`_. I learned a lot from the reviews and the
+technical discussions. I plan to keep contributing to AboutCode and continue
+maintaining this work after GSoC.