Enregistré dans:
| Auteurs principaux: | Velasco, Alejandro, Palacio, David N., Rodriguez-Cardenas, Daniel, Poshyvanyk, Denys |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2401.01512 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?
par: O'Brien, Conor, et autres
Publié: (2024)
par: O'Brien, Conor, et autres
Publié: (2024)
How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study
par: Velasco, Alejandro, et autres
Publié: (2024)
par: Velasco, Alejandro, et autres
Publié: (2024)
SnipGen: A Mining Repository Framework for Evaluating LLMs for Code
par: Rodriguez-Cardenas, Daniel, et autres
Publié: (2025)
par: Rodriguez-Cardenas, Daniel, et autres
Publié: (2025)
A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code
par: Velasco, Alejandro, et autres
Publié: (2025)
par: Velasco, Alejandro, et autres
Publié: (2025)
Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation
par: Khati, Dipin, et autres
Publié: (2025)
par: Khati, Dipin, et autres
Publié: (2025)
Towards Enabling An Artificial Self-Construction Software Life-cycle via Autopoietic Architectures
par: Rodriguez-Cardenas, Daniel, et autres
Publié: (2026)
par: Rodriguez-Cardenas, Daniel, et autres
Publié: (2026)
Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations
par: Palacio, David N., et autres
Publié: (2024)
par: Palacio, David N., et autres
Publié: (2024)
Toward a Theory of Causation for Interpreting Neural Code Models
par: Palacio, David N., et autres
Publié: (2023)
par: Palacio, David N., et autres
Publié: (2023)
On Interpreting the Effectiveness of Unsupervised Software Traceability with Information Theory
par: Palacio, David N., et autres
Publié: (2024)
par: Palacio, David N., et autres
Publié: (2024)
Toward Neurosymbolic Program Comprehension
par: Velasco, Alejandro, et autres
Publié: (2025)
par: Velasco, Alejandro, et autres
Publié: (2025)
Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis
par: Khati, Dipin, et autres
Publié: (2026)
par: Khati, Dipin, et autres
Publié: (2026)
Rethinking Software Empirical Studies with Structural Causal Models
par: Rodriguez-Cardenas, Daniel, et autres
Publié: (2026)
par: Rodriguez-Cardenas, Daniel, et autres
Publié: (2026)
On the Effectiveness of LLM-as-a-judge for Code Generation and Summarization
par: Crupi, Giuseppe, et autres
Publié: (2025)
par: Crupi, Giuseppe, et autres
Publié: (2025)
Understanding Privacy Risks in Code Models Through Training Dynamics: A Causal Approach
par: Yang, Hua, et autres
Publié: (2025)
par: Yang, Hua, et autres
Publié: (2025)
Tricky$^2$: Towards a Benchmark for Evaluating Human and LLM Error Interactions
par: Granger, Cole, et autres
Publié: (2026)
par: Granger, Cole, et autres
Publié: (2026)
On the Generalizability of Transformer Models to Code Completions of Different Lengths
par: Cooper, Nathan, et autres
Publié: (2025)
par: Cooper, Nathan, et autres
Publié: (2025)
Towards More Trustworthy Deep Code Models by Enabling Out-of-Distribution Detection
par: Yan, Yanfu, et autres
Publié: (2025)
par: Yan, Yanfu, et autres
Publié: (2025)
A Path Less Traveled: Reimagining Software Engineering Automation via a Neurosymbolic Paradigm
par: Mastropaolo, Antonio, et autres
Publié: (2025)
par: Mastropaolo, Antonio, et autres
Publié: (2025)
Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives
par: Khati, Dipin, et autres
Publié: (2025)
par: Khati, Dipin, et autres
Publié: (2025)
How Do Semantically Equivalent Code Transformations Impact Membership Inference on LLMs for Code?
par: Yang, Hua, et autres
Publié: (2025)
par: Yang, Hua, et autres
Publié: (2025)
Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education
par: Mojica-Hanke, Anamaria, et autres
Publié: (2024)
par: Mojica-Hanke, Anamaria, et autres
Publié: (2024)
Testing Practices, Challenges, and Developer Perspectives in Open-Source IoT Platforms
par: Rodriguez-Cardenas, Daniel, et autres
Publié: (2025)
par: Rodriguez-Cardenas, Daniel, et autres
Publié: (2025)
When Quantum Meets Classical: Characterizing Hybrid Quantum-Classical Issues Discussed in Developer Forums
par: Zappin, Jake, et autres
Publié: (2024)
par: Zappin, Jake, et autres
Publié: (2024)
Challenges and Practices in Quantum Software Testing and Debugging: Insights from Practitioners
par: Zappin, Jake, et autres
Publié: (2025)
par: Zappin, Jake, et autres
Publié: (2025)
Bridging the Quantum Divide: Aligning Academic and Industry Goals in Software Engineering
par: Zappin, Jake, et autres
Publié: (2025)
par: Zappin, Jake, et autres
Publié: (2025)
Prompting in Practice: Investigating Software Practitioners' Use of Generative AI Tools
par: Otten, Daniel, et autres
Publié: (2025)
par: Otten, Daniel, et autres
Publié: (2025)
Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering
par: Rodriguez-Cardenas, Daniel, et autres
Publié: (2026)
par: Rodriguez-Cardenas, Daniel, et autres
Publié: (2026)
"Don't Be Afraid, Just Learn": Insights from Industry Practitioners to Prepare Software Engineers in the Age of Generative AI
par: Otten, Daniel, et autres
Publié: (2026)
par: Otten, Daniel, et autres
Publié: (2026)
Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate Video-based Bug Reports
par: Yan, Yanfu, et autres
Publié: (2024)
par: Yan, Yanfu, et autres
Publié: (2024)
"The Law Doesn't Work Like a Computer": Exploring Software Licensing Issues Faced by Legal Practitioners
par: Wintersgill, Nathan, et autres
Publié: (2024)
par: Wintersgill, Nathan, et autres
Publié: (2024)
"False negative -- that one is going to kill you": Understanding Industry Perspectives of Static Analysis based Security Testing
par: Ami, Amit Seal, et autres
Publié: (2023)
par: Ami, Amit Seal, et autres
Publié: (2023)
BOMs Away! Inside the Minds of Stakeholders: A Comprehensive Study of Bills of Materials for Software Systems
par: Stalnaker, Trevor, et autres
Publié: (2023)
par: Stalnaker, Trevor, et autres
Publié: (2023)
Toward Explaining Large Language Models in Software Engineering Tasks
par: Vitale, Antonio, et autres
Publié: (2025)
par: Vitale, Antonio, et autres
Publié: (2025)
An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face
par: Stalnaker, Trevor, et autres
Publié: (2025)
par: Stalnaker, Trevor, et autres
Publié: (2025)
Mutation-based Evaluation of Cryptographic API Misuse Detectors
par: Ami, Amit Seal, et autres
Publié: (2021)
par: Ami, Amit Seal, et autres
Publié: (2021)
Developers' Perspectives on Software Licensing: Current Practices, Challenges, and Tools
par: Wintersgill, Nathan, et autres
Publié: (2025)
par: Wintersgill, Nathan, et autres
Publié: (2025)
GUIPilot: A Consistency-based Mobile GUI Testing Approach for Detecting Application-specific Bugs
par: Liu, Ruofan, et autres
Publié: (2025)
par: Liu, Ruofan, et autres
Publié: (2025)
Hierarchical Evaluation of Software Design Capabilities of Large Language Models of Code
par: Saad, Mootez, et autres
Publié: (2025)
par: Saad, Mootez, et autres
Publié: (2025)
Towards a Science of Causal Interpretability in Deep Learning for Software Engineering
par: Palacio, David N.
Publié: (2025)
par: Palacio, David N.
Publié: (2025)
Data-Driven Evidence-Based Syntactic Sugar Design
par: OBrien, David, et autres
Publié: (2024)
par: OBrien, David, et autres
Publié: (2024)
Documents similaires
-
Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?
par: O'Brien, Conor, et autres
Publié: (2024) -
How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study
par: Velasco, Alejandro, et autres
Publié: (2024) -
SnipGen: A Mining Repository Framework for Evaluating LLMs for Code
par: Rodriguez-Cardenas, Daniel, et autres
Publié: (2025) -
A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code
par: Velasco, Alejandro, et autres
Publié: (2025) -
Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation
par: Khati, Dipin, et autres
Publié: (2025)