Copertina

Which Alert Removals are Beneficial?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Amit, Idan
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Software Engineering Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Follow Your Nose -- Which Code Smells are Worth Chasing?
di: Amit, Idan, et al.
Pubblicazione: (2021)

A Large Scale Survey of Motivation in Software Development and Analysis of its Validity
di: Amit, Idan, et al.
Pubblicazione: (2024)

DEM: A Method for Certifying Deep Neural Network Classifier Outputs in Aerospace
di: Katz, Guy, et al.
Pubblicazione: (2024)

Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach
di: Kuang, Jinxi, et al.
Pubblicazione: (2024)

Which Programming Language and Model Work Best With LLM-as-a-Judge For Code Retrieval?
di: Roberts, Lucas, et al.
Pubblicazione: (2025)

I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution
di: Bisztray, Tamas, et al.
Pubblicazione: (2025)

Models That Prove Their Own Correctness
di: Amit, Noga, et al.
Pubblicazione: (2024)

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation
di: Diggs, Colin, et al.
Pubblicazione: (2024)

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

Which PPML Would a User Choose? A Structured Decision Support Framework for Developers to Rank PPML Techniques Based on User Acceptance Criteria
di: Löbner, Sascha, et al.
Pubblicazione: (2024)

RM -RF: Reward Model for Run-Free Unit Test Evaluation
di: Bruches, Elena, et al.
Pubblicazione: (2026)

HyperNet-Adaptation for Diffusion-Based Test Case Generation
di: Weißl, Oliver, et al.
Pubblicazione: (2026)

Composer 2 Technical Report
di: Research, Cursor, et al.
Pubblicazione: (2026)

Think Anywhere in Code Generation
di: Jiang, Xue, et al.
Pubblicazione: (2026)

PLMGH: What Matters in PLM-GNN Hybrids for Code Classification and Vulnerability Detection
di: Idrissi, Mohamed Taoufik Kaouthar El, et al.
Pubblicazione: (2026)

Carbon-Taxed Transformers: A Green Compression Pipeline for Overgrown Language Models
di: Alam, Ajmain Inqiad, et al.
Pubblicazione: (2026)

Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap
di: Kim, Naryeong, et al.
Pubblicazione: (2026)

Shapley-Guided Neural Repair Approach via Derivative-Free Optimization
di: Sun, Xinyu, et al.
Pubblicazione: (2026)

Praxium: Diagnosing Cloud Anomalies with AI-based Telemetry and Dependency Analysis
di: Kumar, Rohan, et al.
Pubblicazione: (2026)

ReCodeAgent: A Multi-Agent Workflow for Language-agnostic Translation and Validation of Large-scale Repositories
di: Ibrahimzada, Ali Reza, et al.
Pubblicazione: (2026)

SkVM: Revisiting Language VM for Skills across Heterogenous LLMs and Harnesses
di: Chen, Le, et al.
Pubblicazione: (2026)

CA2: Code-Aware Agent for Automated Game Testing
di: Adaikkappan, Valliappan Chidambaram, et al.
Pubblicazione: (2026)

From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing
di: Liang, Linfeng, et al.
Pubblicazione: (2026)

Automating Formal Verification with Reinforcement Learning and Recursive Inference
di: Tan, Max
Pubblicazione: (2026)

ARFT-Transformer: Modeling Metric Dependencies for Cross-Project Aging-Related Bug Prediction
di: Ge, Shuning, et al.
Pubblicazione: (2026)

Optimising for Energy Efficiency and Performance in Machine Learning
di: Ferreira, Emile Dos Santos, et al.
Pubblicazione: (2026)

Whitespaces Don't Lie: Feature-Driven and Embedding-Based Approaches for Detecting Machine-Generated Code
di: Nirob, Syed Mehedi Hasan, et al.
Pubblicazione: (2026)

Bug Severity Prediction in Software Projects Using Supervised Machine Learning Models
di: Nice, Nafisha Tamanna
Pubblicazione: (2026)

Graph-Free Root Cause Analysis
di: Pham, Luan
Pubblicazione: (2026)

FunPRM: Function-as-Step Process Reward Model with Meta Reward Correction for Code Generation
di: Zhang, Ruiyi, et al.
Pubblicazione: (2026)

AICD Bench: A Challenging Benchmark for AI-Generated Code Detection
di: Orel, Daniil, et al.
Pubblicazione: (2026)

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

Comparative Separation: Evaluating Separation on Comparative Judgment Test Data
di: Xi, Xiaoyin, et al.
Pubblicazione: (2026)

Leveraging Generative AI for Enhancing Domain-Driven Software Design
di: Wiegand, Götz-Henrik, et al.
Pubblicazione: (2026)

Harmonica: A Self-Adaptation Exemplar for Sustainable MLOps
di: Halgatti, Ananya, et al.
Pubblicazione: (2026)

SmartOracle -- An Agentic Approach to Mitigate Noise in Differential Oracles
di: Srinivasan, Srinath, et al.
Pubblicazione: (2026)

CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models
di: Li, Mengran, et al.
Pubblicazione: (2026)

LLM-Guided Runtime Parameter Optimization for Energy-Efficient Model Inference
di: Crumpacker, Katelyn, et al.
Pubblicazione: (2026)

Evaluating Assurance Cases as Text-Attributed Graphs for Structure and Provenance Analysis
di: Ikhwantri, Fariz, et al.
Pubblicazione: (2026)

Domain-Adaptable Reinforcement Learning for Code Generation with Dense Rewards
di: Jolfaei, Erfan Aghadavoodi, et al.
Pubblicazione: (2026)