Cover Image

DynamicsLLM: a Dynamic Analysis-based Tool for Generating Intelligent Execution Traces Using LLMs to Detect Android Behavioural Code Smells

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cherief, Houcine Abdelkader, Avellaneda, Florent, Moha, Naouel
Format:	Preprint
Published:	2026
Subjects:	Software Engineering Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ML Code Smells: From Specification to Detection
by: Mahmoudi, Brahim, et al.
Published: (2025)

Specification and Detection of LLM Code Smells
by: Mahmoudi, Brahim, et al.
Published: (2025)

GLiSE: A Prompt-Driven and ML-Powered Tool for Automated Grey Literature Extraction in Software Engineering
by: Cherief, Houcine Abdelkader, et al.
Published: (2025)

Comparison of Code Quality and Best Practices in IoT and non-IoT Software
by: Khezemi, Nour, et al.
Published: (2024)

An Event-Driven Tool for Context-Aware Code Smell Detection Using SmellDSL
by: Viegas, Matheus dos Santos, et al.
Published: (2026)

MLmisFinder: A Specification and Detection Approach of Machine Learning Service Misuses
by: Amor, Hadil Ben, et al.
Published: (2026)

A Systematic Literature Review of Machine Learning Approaches for Migrating Monolithic Systems to Microservices
by: Trabelsi, Imen, et al.
Published: (2025)

Investigating The Smells of LLM Generated Code
by: Paul, Debalina Ghosh, et al.
Published: (2025)

Assessing the Capability of Android Dynamic Analysis Tools to Combat Anti-Runtime Analysis Techniques
by: Suo, Dewen, et al.
Published: (2025)

TraceRAG: A LLM-Based Framework for Explainable Android Malware Detection and Behavior Analysis
by: Zhang, Guangyu, et al.
Published: (2025)

HyClone: Bridging LLM Understanding and Dynamic Execution for Semantic Code Clone Detection
by: Liang, Yunhao, et al.
Published: (2025)

A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code
by: Velasco, Alejandro, et al.
Published: (2025)

AI-Generated Smells: An Analysis of Code and Architecture in LLM and Agent-Driven Development
by: Zhu, Yuecai, et al.
Published: (2026)

XTrace: A Non-Invasive Dynamic Tracing Framework for Android Applications in Production
by: Hu, Qi, et al.
Published: (2025)

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

An Empirical Evaluation of Code Smell Detection in Angular Applications
by: Nunes, Maykon, et al.
Published: (2026)

An Empirical Study of Interaction Smells in Multi-Turn Human-LLM Collaborative Code Generation
by: Zhang, Binquan, et al.
Published: (2026)

Demystifying Errors in LLM Reasoning Traces: An Empirical Study of Code Execution Simulation
by: Abdollahi, Mohammad, et al.
Published: (2025)

Automating Android Build Repair: Bridging the Reasoning-Execution Gap in LLM Agents with Domain-Specific Tools
by: Son, Ha Min, et al.
Published: (2025)

PyExamine A Comprehensive, UnOpinionated Smell Detection Tool for Python
by: Shivashankar, Karthik, et al.
Published: (2025)

Clean Code, Better Models: Enhancing LLM Performance with Smell-Cleaned Dataset
by: Xue, Zhipeng, et al.
Published: (2025)

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study
by: Santana Jr, E. G., et al.
Published: (2025)

Towards Effectively Leveraging Execution Traces for Program Repair with Code LLMs
by: Haque, Mirazul, et al.
Published: (2025)

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

A Tool for In-depth Analysis of Code Execution Reasoning of Large Language Models
by: Liu, Changshu, et al.
Published: (2025)

AndroLog: Android Instrumentation and Code Coverage Analysis
by: Samhi, Jordan, et al.
Published: (2024)

A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Code Smell Detection
by: Zhang, Beiqi, et al.
Published: (2024)

Prompt Learning for Multi-Label Code Smell Detection: A Promising Approach
by: Liu, Haiyang, et al.
Published: (2024)

A Study of Using Multimodal LLMs for Non-Crash Functional Bug Detection in Android Apps
by: Ju, Bangyan, et al.
Published: (2024)

From UI to Code: Mobile Ads Detection via LLM-Unified Static-Dynamic Analysis
by: Ma, Shang, et al.
Published: (2026)

SACS: A Code Smell Dataset using Semi-automatic Generation Approach
by: Zhang, Hanyu, et al.
Published: (2026)

Comparing ML-Specific and General Python Code Smells Across Project Characteristics
by: Agh, Halimeh, et al.
Published: (2026)

Beyond Strict Rules: Assessing the Effectiveness of Large Language Models for Code Smell Detection
by: Souza, Saymon, et al.
Published: (2026)

Implementing and Executing Static Analysis Using LLVM and CodeChecker
by: Horvath, Gabor, et al.
Published: (2024)

Empirical Characterization of Logging Smells in Machine Learning Code
by: Foalem, Patrick Loic, et al.
Published: (2026)

Empirical Characterization of Logging Smells in Machine Learning Code
by: Foalem, Patrick Loic, et al.
Published: (2026)

The Influence of Code Smells in Efferent Neighbors on Class Stability
by: Zhang, Zushuai, et al.
Published: (2026)

LDMDroid: Leveraging LLMs for Detecting Data Manipulation Errors in Android Apps
by: Xiao, Xiangyang, et al.
Published: (2026)

The Code Whisperer: LLM and Graph-Based AI for Smell and Vulnerability Resolution
by: Baqar, Mohammad, et al.
Published: (2026)

Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation
by: Wang, Chong, et al.
Published: (2024)