Copertina

DocuMint: Docstring Generation for Python using Small Language Models

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Poudel, Bibek, Cook, Adam, Traore, Sekou, Ameli, Shelah
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Software Engineering Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Towards a Small Language Model Lifecycle Framework
di: Miraghaei, Parsa, et al.
Pubblicazione: (2025)

COSMosFL: Ensemble of Small Language Models for Fault Localisation
di: Cho, Hyunjoon, et al.
Pubblicazione: (2025)

evomap: A Toolbox for Dynamic Mapping in Python
di: Matthe, Maximilian
Pubblicazione: (2025)

Using Small Language Models to Reverse-Engineer Machine Learning Pipelines Structures
di: Lacroix, Nicolas, et al.
Pubblicazione: (2026)

The Constraint Tax: Measuring Validity-Correctness Tradeoffs in Structured Outputs for Small Language Models
di: Ray, Jaideep
Pubblicazione: (2026)

Analysing Python Machine Learning Notebooks with Moose
di: Mignard, Marius, et al.
Pubblicazione: (2025)

PyPulse: A Python Library for Biosignal Imputation
di: Gao, Kevin, et al.
Pubblicazione: (2024)

MLXP: A Framework for Conducting Replicable Experiments in Python
di: Arbel, Michael, et al.
Pubblicazione: (2024)

Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python
di: Adamczyk, Jakub, et al.
Pubblicazione: (2024)

regAL: Python Package for Active Learning of Regression Problems
di: Surzhikova, Elizaveta, et al.
Pubblicazione: (2024)

DomainLab: A modular Python package for domain generalization in deep learning
di: Sun, Xudong, et al.
Pubblicazione: (2024)

Using Large Language Models to Generate JUnit Tests: An Empirical Study
di: Siddiq, Mohammed Latif, et al.
Pubblicazione: (2023)

AssertionBench: A Benchmark to Evaluate Large-Language Models for Assertion Generation
di: Pulavarthi, Vaishnavi, et al.
Pubblicazione: (2024)

Correctness Assessment of Code Generated by Large Language Models Using Internal Representations
di: Bui, Tuan-Dung, et al.
Pubblicazione: (2025)

CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation
di: Yan, Kaiwen, et al.
Pubblicazione: (2025)

Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++
di: Mhatre, Akshay, et al.
Pubblicazione: (2025)

Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researchers
di: Yao, Shunxin
Pubblicazione: (2025)

On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code
di: Weyssow, Martin, et al.
Pubblicazione: (2023)

Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications
di: Huynh, Nam, et al.
Pubblicazione: (2025)

Fault Localization via Fine-tuning Large Language Models with Mutation Generated Stack Traces
di: Jambigi, Neetha, et al.
Pubblicazione: (2025)

scicode-lint: Detecting Methodology Bugs in Scientific Python Code with LLM-Generated Patterns
di: Samsonau, Sergey V.
Pubblicazione: (2026)

PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)
di: Nazzal, Mahmoud, et al.
Pubblicazione: (2024)

Towards a Neural Debugger for Python
di: Beck, Maximilian, et al.
Pubblicazione: (2026)

Fairness Testing in Retrieval-Augmented Generation: How Small Perturbations Reveal Bias in Small Language Models
di: de Oliveira, Matheus Vinicius da Silva, et al.
Pubblicazione: (2025)

Calibration and Correctness of Language Models for Code
di: Spiess, Claudio, et al.
Pubblicazione: (2024)

GLLM: Self-Corrective G-Code Generation using Large Language Models with User Feedback
di: Abdelaal, Mohamed, et al.
Pubblicazione: (2025)

Unlearning Trojans in Large Language Models: A Comparison Between Natural Language and Source Code
di: Kazemi, Mahdi, et al.
Pubblicazione: (2024)

Integrating Large Language Models for Automated Structural Analysis
di: Liang, Haoran, et al.
Pubblicazione: (2025)

LEANCODE: Understanding Models Better for Code Simplification of Pre-trained Large Language Models
di: Wang, Yan, et al.
Pubblicazione: (2025)

Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
di: Aracena, Gabriel, et al.
Pubblicazione: (2025)

Large Language Models for Software Engineering: A Reproducibility Crisis
di: Siddiq, Mohammed Latif, et al.
Pubblicazione: (2025)

Revisiting the Plastic Surgery Hypothesis via Large Language Models
di: Xia, Chunqiu Steven, et al.
Pubblicazione: (2023)

Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)
di: Ahmed, Toufique, et al.
Pubblicazione: (2023)

Fuzz4All: Universal Fuzzing with Large Language Models
di: Xia, Chunqiu Steven, et al.
Pubblicazione: (2023)

Can Code Language Models Learn Clarification-Seeking Behaviors?
di: Wu, Jie JW, et al.
Pubblicazione: (2025)

A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages
di: Joel, Sathvik, et al.
Pubblicazione: (2024)

Generating Streamlining Constraints with Large Language Models
di: Voboril, Florentina, et al.
Pubblicazione: (2024)

Evaluating Language Models for Efficient Code Generation
di: Liu, Jiawei, et al.
Pubblicazione: (2024)

Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection
di: Gonçalves, José, et al.
Pubblicazione: (2025)

CARGO: A Framework for Confidence-Aware Routing of Large Language Models
di: Barrak, Amine, et al.
Pubblicazione: (2025)