# STACC: Code Comment Classification using SentenceTransformers

arXiv: https://arxiv.org/abs/2302.13149 · HTML: https://mlsanity.com/paper/2302.13149/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences](https://mlsanity.com/paper/2202.06689/) (2022)
- [Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries](https://mlsanity.com/paper/2301.01701/) (2023)
- [How to Identify Class Comment Types? A Multi-language Approach for Class Comment Classification](https://mlsanity.com/paper/2107.04521/) (2021)

## Built on by (most cited first, 5 of 5)

- [Enriching Source Code with Contextual Data for Code Completion Models: An Empirical Study](https://mlsanity.com/paper/2304.12269/) (2023)
- [Can LLMs Handle WebShell Detection? Overcoming Detection Challenges with Behavioral Function-Aware Framework](https://mlsanity.com/paper/2504.13811/) (2025)
- [A Multi-solution Study on GDPR AI-enabled Completeness Checking of DPAs](https://mlsanity.com/paper/2311.13881/) (2023)
- [Dopamin: Transformer-based Comment Classifiers through Domain Post-Training and Multi-level Layer Aggregation](https://mlsanity.com/paper/2408.04663/) (2024)
- [LoRA-MME: Multi-Model Ensemble of LoRA-Tuned Encoders for Code Comment Classification](https://mlsanity.com/paper/2603.03959/) (2026)

## Abstract (arXiv)

Code comments are a key resource for information about software artefacts. Depending on the use case, only some types of comments are useful. Thus, automatic approaches to classify these comments have been proposed. In this work, we address this need by proposing, STACC, a set of SentenceTransformers-based binary classifiers. These lightweight classifiers are trained and tested on the NLBSE Code Comment Classification tool competition dataset, and surpass the baseline by a significant margin, achieving an average F1 score of 0.74 against the baseline of 0.31, which is an improvement of 139%. A replication package, as well as the models themselves, are publicly available.