seai2526-uniba-TheClouds/code-comment-classification-locust

Overview

CodeCommentClassification is an end-to-end pipeline to classify comment sentences into language-specific categories and to aggregate results at file/PR level so reviewers can focus on rationale, usage notes, deprecations, examples, and other high-value signals.

The project targets and aims to surpass the NLBSE’26 baselines, providing reproducible training, evaluation, and inference.

The full documentation is available here: https://se4ai2526-uniba.github.io/TheClouds/

Locust

To verify the robustness of the API (hosted here: https://huggingface.co/spaces/seai2526-uniba-TheClouds/Code-Comment-Classification-Api) torwards several concurrent connection, the Locust library was used. This provides a high traffic simulation that tests the API stability under load.

Endpoints covered by the simulation

The simulated user interacts with several endpoints to mirror a typical client lifecycle:

• —Root (/): Basic connectivity and welcome message, useful as a quick sanity check under load.
• —Health (/status): Periodic health checks mimicking liveness/readiness probes, ensuring the monitoring surface stays responsive even during heavy inference traffic.
• —Models (/models): A model discovery call representing clients querying available model types or metadata before calling /predict.
• —Privacy (/privacy): A lightweight informational endpoint that users or UIs may hit to retrieve privacy policy information.
• —Predict (/predict?model_type=transformer): The main, high-cost operation that receives randomly sampled comments and triggers full Transformer inference, responsible for the majority of CPU/RAM usage during the test.

Test data

This Space bundles a small dataset under data/raw/ so Locust always has samples available.

Expected files:

• —data/raw/java_test.csv
• —data/raw/python_test.csv
• —data/raw/pharo_test.csv

Each CSV must contain either a comment_sentence column (used by default) or a combo column.