Cover Image

Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Peña, Fabian C., Herbold, Steffen
Format:	Preprint
Published:	2025
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SELU: A Software Engineering Language Understanding Benchmark
by: Peña, Fabian C., et al.
Published: (2025)

Causal Inference for the Effect of Code Coverage on Bug Introduction
by: Schulte, Lukas, et al.
Published: (2026)

Utilizing LLMs for Industrial Process Automation: A Case Study on Modifying RAPID Programs
by: Fares, Salim, et al.
Published: (2025)

CodeCSE: A Simple Multilingual Model for Code and Comment Sentence Embeddings
by: Varkey, Anthony, et al.
Published: (2024)

Neurosymbolic Architectural Reasoning: Towards Formal Analysis through Neural Software Architecture Inference
by: Herbold, Steffen, et al.
Published: (2025)

Augmenting the Generality and Performance of Large Language Models for Software Engineering
by: Peña, Fabian C.
Published: (2025)

An Exploratory Study of Bug-Introducing Changes: Exploring Relationships in Bug-Introducing Changes Towards Causal Understanding
by: Schulte, Lukas, et al.
Published: (2023)

The whos, whats, and whys of issues related to personal data and data protection in open-source projects on GitHub
by: Henning, Anne, et al.
Published: (2023)

Automated Classification of Human Code Review Comments with Large Language Models
by: Çağlar, Semih, et al.
Published: (2026)

Generating Context-Aware Contrastive Explanations in Rule-based Systems
by: Herbold, Lars, et al.
Published: (2024)

Simulink Mutation Testing using CodeBERT
by: Zhang, Jingfan, et al.
Published: (2025)

Optimizing Deep Learning Models to Address Class Imbalance in Code Comment Classification
by: Mock, Moritz, et al.
Published: (2025)

On Evaluating the Efficiency of Source Code Generated by LLMs
by: Niu, Changan, et al.
Published: (2024)

Hold On! Is My Feedback Useful? Evaluating the Usefulness of Code Review Comments
by: Ahmed, Sharif, et al.
Published: (2025)

On the Quality of AI-Generated Source Code Comments: A Comprehensive Evaluation
by: Guelman, Ian, et al.
Published: (2024)

Synthesizing Performance Constraints for Evaluating and Improving Code Efficiency
by: Yang, Jun, et al.
Published: (2025)

Improving Source Code Similarity Detection Through GraphCodeBERT and Integration of Additional Features
by: Martinez-Gil, Jorge
Published: (2024)

Evaluating Efficiency and Novelty of LLM-Generated Code for Graph Analysis
by: Nia, Atieh Barati, et al.
Published: (2025)

TRACE: Evaluating Execution Efficiency of LLM-Based Code Translation
by: Gong, Zhihao, et al.
Published: (2025)

Comment Traps: How Defective Commented-out Code Augment Defects in AI-Assisted Code Generation
by: Huang, Yuan, et al.
Published: (2025)

TRACE: Evaluating Execution Efficiency of LLM-Based Code Translation
by: Gong, Zhihao, et al.
Published: (2026)

Evaluating the Effectiveness and Efficiency of Demonstration Retrievers in RAG for Coding Tasks
by: He, Pengfei, et al.
Published: (2024)

Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?
by: Vitale, Antonio, et al.
Published: (2025)

Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education
by: Mojica-Hanke, Anamaria, et al.
Published: (2024)

DocChecker: Bootstrapping Code Large Language Model for Detecting and Resolving Code-Comment Inconsistencies
by: Dau, Anh T. V., et al.
Published: (2023)

Retrieval-Augmented Code Review Comment Generation
by: Hong, Hyunsun, et al.
Published: (2025)

Understanding Emojis :) in Useful Code Review Comments
by: Ahmed, Sharif, et al.
Published: (2024)

Towards Automated Detection of Inline Code Comment Smells
by: Oztas, Ipek, et al.
Published: (2025)

COFFE: A Code Efficiency Benchmark for Code Generation
by: Peng, Yun, et al.
Published: (2025)

Understanding Practitioners' Expectations on Clear Code Review Comments
by: Chen, Junkai, et al.
Published: (2024)

Investigating the Impact of Code Comment Inconsistency on Bug Introducing
by: Radmanesh, Shiva, et al.
Published: (2024)

Towards Better Static Code Analysis Reports: Sentence Transformer-based Filtering of Non-Actionable Alerts
by: Aladics, Tamás, et al.
Published: (2026)

Evaluating the Energy-Efficiency of the Code Generated by LLMs
by: Islam, Md Arman, et al.
Published: (2025)

On the Compression of Language Models for Code: An Empirical Study on CodeBERT
by: d'Aloisio, Giordano, et al.
Published: (2024)

Measuring Impacts of Poisoning on Model Parameters and Neuron Activations: A Case Study of Poisoning CodeBERT
by: Hussain, Aftab, et al.
Published: (2024)

Issue Retrieval and Verification Enhanced Supplementary Code Comment Generation
by: Zou, Yanzhen, et al.
Published: (2025)

The Influence of Code Comments on the Perceived Helpfulness of Stack Overflow Posts
by: Figl, Kathrin, et al.
Published: (2025)

Improving Retrieval-Augmented Code Comment Generation by Retrieving for Generation
by: Lu, Hanzhen, et al.
Published: (2024)

LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning
by: Dihan, Mahir Labib, et al.
Published: (2026)

Code Comments for Quantum Software Development Kits: An Empirical Study on Qiskit
by: Zhou, Zenghui, et al.
Published: (2025)