:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	J, Mathav Raj, VM, Kushala, Warrier, Harikrishna, Gupta, Yogesh
Format:	Preprint
Published:	2024
Subjects:	Software Engineering Machine Learning
Online Access:	https://arxiv.org/abs/2404.10779
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Breaking Memorization Barriers in LLM Code Fine-Tuning via Information Bottleneck for Improved Generalization
by: Wang, Changsheng, et al.
Published: (2025)

Benchmarking with MIMIC-IV, an irregular, spare clinical time series dataset
by: Bui, Hung, et al.
Published: (2024)

Step Rejection Fine-Tuning: A Practical Distillation Recipe
by: Slinko, Igor, et al.
Published: (2026)

RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair
by: Silva, André, et al.
Published: (2023)

Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuning
by: Galliamov, Karim, et al.
Published: (2024)

Pimp My LLM: Leveraging Variability Modeling to Tune Inference Hyperparameters
by: Zine, Nada, et al.
Published: (2026)

Large Language Models for Analyzing Enterprise Architecture Debt in Unstructured Documentation
by: Pagels, Christin, et al.
Published: (2026)

GiFT: Gibbs Fine-Tuning for Code Generation
by: Li, Haochen, et al.
Published: (2025)

Code Less, Align More: Efficient LLM Fine-tuning for Code Generation with Data Pruning
by: Tsai, Yun-Da, et al.
Published: (2024)

Grounded AI for Code Review: Resource-Efficient Large-Model Serving in Enterprise Pipelines
by: Mandal, Sayan, et al.
Published: (2025)

Automating the Enterprise with Foundation Models
by: Wornow, Michael, et al.
Published: (2024)

Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approach
by: Chakrabarty, Sayak, et al.
Published: (2024)

Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models
by: Yang, Aidan Z. H., et al.
Published: (2024)

One Model, Many Skills: Parameter-Efficient Fine-Tuning for Multitask Code Analysis
by: Akli, Amal, et al.
Published: (2026)

Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
by: Weyssow, Martin, et al.
Published: (2023)

Beyond Verifiable Rewards: Rubric-Based GRM for Reinforced Fine-Tuning SWE Agents
by: Huang, Jiawei, et al.
Published: (2026)

Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study
by: Storhaug, André, et al.
Published: (2024)

ADReFT: Adaptive Decision Repair for Safe Autonomous Driving via Reinforcement Fine-Tuning
by: Cheng, Mingfei, et al.
Published: (2025)

Keeping Code-Aware LLMs Fresh: Full Refresh, In-Context Deltas, and Incremental Fine-Tuning
by: Sharma, Pradeep Kumar, et al.
Published: (2025)

Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models
by: Chen, Jie, et al.
Published: (2024)

LLM Critics Help Catch LLM Bugs
by: McAleese, Nat, et al.
Published: (2024)

GiveMeLabeledIssues: An Open Source Issue Recommendation System
by: Vargovich, Joseph, et al.
Published: (2023)

DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations
by: Roychowdhury, Sohini, et al.
Published: (2025)

To Err is AI : A Case Study Informing LLM Flaw Reporting Practices
by: McGregor, Sean, et al.
Published: (2024)

BitsAI-Fix: LLM-Driven Approach for Automated Lint Error Resolution in Practice
by: Li, Yuanpeng, et al.
Published: (2025)

Towards MLOps: A DevOps Tools Recommender System for Machine Learning System
by: Shah, Pir Sami Ullah, et al.
Published: (2024)

Automatic Generation of Executable BPMN Models from Medical Guidelines
by: Sekar, Praveen Kumar Menaka, et al.
Published: (2026)

MetaSel: A Test Selection Approach for Fine-tuned DNN Models
by: Abbasishahkoo, Amin, et al.
Published: (2025)

A Catalog of Fairness-Aware Practices in Machine Learning Engineering
by: Voria, Gianmario, et al.
Published: (2024)

Continuous Integration Practices in Machine Learning Projects: The Practitioners` Perspective
by: Bernardo, João Helis, et al.
Published: (2025)

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

The State of Documentation Practices of Third-party Machine Learning Models and Datasets
by: Oreamuno, Ernesto Lang, et al.
Published: (2023)

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

LLM-Based Design Pattern Detection
by: Schindler, Christian, et al.
Published: (2025)

Evaluating Robustness of Large Language Models in Enterprise Applications: Benchmarks for Perturbation Consistency Across Formats and Languages
by: Bogavelli, Tara, et al.
Published: (2026)

LoRA-MME: Multi-Model Ensemble of LoRA-Tuned Encoders for Code Comment Classification
by: Haider, Md Akib, et al.
Published: (2026)

Assumptions to Evidence: Evaluating Security Practices Adoption and Their Impact on Outcomes in the npm Ecosystem
by: Zahan, Nusrat, et al.
Published: (2025)

SkillScope: A Tool to Predict Fine-Grained Skills Needed to Solve Issues on GitHub
by: Carter, Benjamin C., et al.
Published: (2025)

LLM Performance for Code Generation on Noisy Tasks
by: Sendyka, Radzim, 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)