Cover Image

Evaluating the Capability of LLMs in Identifying Compilation Errors in Configurable Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Albuquerque, Lucas, Gheyi, Rohit, Ribeiro, Márcio
Format:	Preprint
Published:	2024
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Variability-Aware Detection and Repair of Compilation Errors Using Foundation Models in Configurable Systems
by: Gheyi, Rohit, et al.
Published: (2026)

Evaluating the Effectiveness of Small Language Models in Detecting Refactoring Bugs
by: Gheyi, Rohit, et al.
Published: (2025)

Code Generation with Small Language Models: A Codeforces-Based Study
by: Souza, Débora, et al.
Published: (2025)

Evaluating Large Language Models in Detecting Test Smells
by: Lucas, Keila, et al.
Published: (2024)

Bugs in the Shadows: Static Detection of Faulty Python Refactorings
by: Oliveira, Jonhnanthan, et al.
Published: (2025)

Investigating the Performance of Small Language Models in Detecting Test Smells in Manual Test Cases
by: Lucas, Keila, et al.
Published: (2025)

Foundation Models as Oracles for Refactoring Correctness Detection
by: Gheyi, Rohit, et al.
Published: (2026)

Assessing Python Style Guides: An Eye-Tracking Study with Novice Developers
by: Roberto, Pablo, et al.
Published: (2024)

RefModel: Detecting Refactorings using Foundation Models
by: Simões, Pedro, et al.
Published: (2025)

Agentic LMs: Hunting Down Test Smells
by: Melo, Rian, et al.
Published: (2025)

Adoption of Large Language Models in Scrum Management: Insights from Brazilian Practitioners
by: Perkusich, Mirko, et al.
Published: (2026)

LLMAID: Identifying AI Capabilities in Android Apps with LLMs
by: Liu, Pei, et al.
Published: (2025)

Isolating Compiler Faults via Multiple Pairs of Adversarial Compilation Configurations
by: Li, Qingyang, et al.
Published: (2025)

Refactoring for Novices in Java: An Eye Tracking Study on the Extract vs. Inline Methods
by: da Costa, José Aldo Silva, et al.
Published: (2026)

A Catalog of Transformations to Remove Smells From Natural Language Tests
by: Aranda, Manoel, et al.
Published: (2024)

Compiling Code LLMs into Lightweight Executables
by: Shi, Jieke, et al.
Published: (2026)

IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injection
by: Nekrasov, Roman, et al.
Published: (2025)

PhantomRun: Auto Repair of Compilation Errors in Embedded Open Source Software
by: Fu, Han, et al.
Published: (2026)

From LLMs to Agents in Programming: The Impact of Providing an LLM with a Compiler
by: Kjellberg, Viktor, et al.
Published: (2026)

Bridging Solidity Evolution Gaps: An LLM-Enhanced Approach for Smart Contract Compilation Error Resolution
by: Ye, Likai, et al.
Published: (2025)

ConfLogger: Enhance Systems' Configuration Diagnosability through Configuration Logging
by: Shan, Shiwen, et al.
Published: (2025)

Exploring the Capabilities of LLMs for Code Change Related Tasks
by: Fan, Lishui, et al.
Published: (2024)

An Empirical Study on the Capability of LLMs in Decomposing Bug Reports
by: Chen, Zhiyuan, et al.
Published: (2025)

Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents
by: Wang, Zehao, et al.
Published: (2024)

CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System
by: Hu, Li, et al.
Published: (2025)

LLMs are Bug Replicators: An Empirical Study on LLMs' Capability in Completing Bug-prone Code
by: Guo, Liwei, et al.
Published: (2025)

ClozeMaster: Fuzzing Rust Compiler by Harnessing LLMs for Infilling Masked Real Programs
by: Gao, Hongyan, et al.
Published: (2026)

Assessing the Capability of LLMs in Solving POSCOMP Questions
by: Viegas, Cayo, et al.
Published: (2025)

Beyond Code, We Are People: A Systematic Mapping of 25 Years of Literature on Soft Skills in Agile Development Teams
by: Lima, Israely, et al.
Published: (2026)

Mutation-based Consistency Testing for Evaluating the Code Understanding Capability of LLMs
by: Li, Ziyu, et al.
Published: (2024)

Isolating Compiler Bugs through Compilation Steps Analysis
by: Liu, Yujie, et al.
Published: (2025)

AP2O-Coder: Adaptively Progressive Preference Optimization for Reducing Compilation and Runtime Errors in LLM-Generated Code
by: Zhang, Jianqing, et al.
Published: (2025)

Studying How Configurations Impact Code Generation in LLMs: the Case of ChatGPT
by: Donato, Benedetta, et al.
Published: (2025)

CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios
by: Huang, Shiting, et al.
Published: (2025)

Empirical evaluation of LLMs in predicting fixes of Configuration bugs in Smart Home System
by: Monisha, Sheikh Moonwara Anjum, et al.
Published: (2025)

Still Manual? Automated Linter Configuration via DSL-Based LLM Compilation of Coding Standards
by: Zhang, Zejun, et al.
Published: (2026)

Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?
by: O'Brien, Conor, et al.
Published: (2024)

Unmasking the Genuine Type Inference Capabilities of LLMs for Java Code Snippets
by: Dong, Yiwen, et al.
Published: (2025)

WARP -- Web-Augmented Real-time Program Repairer: A Real-Time Compilation Error Resolution using LLMs and Web-Augmented Synthesis
by: Luiz, Anderson de Lima
Published: (2025)

The New Compiler Stack: A Survey on the Synergy of LLMs and Compilers
by: Zhang, Shuoming, et al.
Published: (2026)