Toward a Theory of Causation for Interpreting Neural Code Models
Fuente:
arXiv
Saved in:
| Main Authors: | Palacio, David N., Velasco, Alejandro, Cooper, Nathan, Rodriguez, Alvaro, Moran, Kevin, Poshyvanyk, Denys |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On Interpreting the Effectiveness of Unsupervised Software Traceability with Information Theory
by: Palacio, David N., et al.
Published: (2024)
by: Palacio, David N., et al.
Published: (2024)
Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations
by: Palacio, David N., et al.
Published: (2024)
by: Palacio, David N., et al.
Published: (2024)
How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study
by: Velasco, Alejandro, et al.
Published: (2024)
by: Velasco, Alejandro, et al.
Published: (2024)
Toward Neurosymbolic Program Comprehension
by: Velasco, Alejandro, et al.
Published: (2025)
by: Velasco, Alejandro, et al.
Published: (2025)
SnipGen: A Mining Repository Framework for Evaluating LLMs for Code
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2025)
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2025)
Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code?
by: Velasco, Alejandro, et al.
Published: (2024)
by: Velasco, Alejandro, et al.
Published: (2024)
Understanding Privacy Risks in Code Models Through Training Dynamics: A Causal Approach
by: Yang, Hua, et al.
Published: (2025)
by: Yang, Hua, et al.
Published: (2025)
Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives
by: Khati, Dipin, et al.
Published: (2025)
by: Khati, Dipin, et al.
Published: (2025)
Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis
by: Khati, Dipin, et al.
Published: (2026)
by: Khati, Dipin, et al.
Published: (2026)
Tricky$^2$: Towards a Benchmark for Evaluating Human and LLM Error Interactions
by: Granger, Cole, et al.
Published: (2026)
by: Granger, Cole, et al.
Published: (2026)
How Do Semantically Equivalent Code Transformations Impact Membership Inference on LLMs for Code?
by: Yang, Hua, et al.
Published: (2025)
by: Yang, Hua, et al.
Published: (2025)
Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation
by: Khati, Dipin, et al.
Published: (2025)
by: Khati, Dipin, et al.
Published: (2025)
On the Generalizability of Transformer Models to Code Completions of Different Lengths
by: Cooper, Nathan, et al.
Published: (2025)
by: Cooper, Nathan, et al.
Published: (2025)
Towards a Science of Causal Interpretability in Deep Learning for Software Engineering
by: Palacio, David N.
Published: (2025)
by: Palacio, David N.
Published: (2025)
Towards Enabling An Artificial Self-Construction Software Life-cycle via Autopoietic Architectures
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2026)
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2026)
A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code
by: Velasco, Alejandro, et al.
Published: (2025)
by: Velasco, Alejandro, et al.
Published: (2025)
"Don't Be Afraid, Just Learn": Insights from Industry Practitioners to Prepare Software Engineers in the Age of Generative AI
by: Otten, Daniel, et al.
Published: (2026)
by: Otten, Daniel, et al.
Published: (2026)
Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate Video-based Bug Reports
by: Yan, Yanfu, et al.
Published: (2024)
by: Yan, Yanfu, et al.
Published: (2024)
Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?
by: O'Brien, Conor, et al.
Published: (2024)
by: O'Brien, Conor, et al.
Published: (2024)
Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2026)
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2026)
Toward Explaining Large Language Models in Software Engineering Tasks
by: Vitale, Antonio, et al.
Published: (2025)
by: Vitale, Antonio, et al.
Published: (2025)
Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development
by: Stalnaker, Trevor, et al.
Published: (2024)
by: Stalnaker, Trevor, et al.
Published: (2024)
Towards More Trustworthy Deep Code Models by Enabling Out-of-Distribution Detection
by: Yan, Yanfu, et al.
Published: (2025)
by: Yan, Yanfu, et al.
Published: (2025)
On the Effectiveness of LLM-as-a-judge for Code Generation and Summarization
by: Crupi, Giuseppe, et al.
Published: (2025)
by: Crupi, Giuseppe, et al.
Published: (2025)
A Path Less Traveled: Reimagining Software Engineering Automation via a Neurosymbolic Paradigm
by: Mastropaolo, Antonio, et al.
Published: (2025)
by: Mastropaolo, Antonio, et al.
Published: (2025)
"False negative -- that one is going to kill you": Understanding Industry Perspectives of Static Analysis based Security Testing
by: Ami, Amit Seal, et al.
Published: (2023)
by: Ami, Amit Seal, et al.
Published: (2023)
CodeSSM: Towards State Space Models for Code Understanding
by: Verma, Shweta, et al.
Published: (2025)
by: Verma, Shweta, et al.
Published: (2025)
Testing Practices, Challenges, and Developer Perspectives in Open-Source IoT Platforms
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2025)
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2025)
Using AI/ML to Find and Remediate Enterprise Secrets in Code & Document Sharing Platforms
by: Kerr, Gregor, et al.
Published: (2024)
by: Kerr, Gregor, et al.
Published: (2024)
PerfCoder: Large Language Models for Interpretable Code Performance Optimization
by: Yang, Jiuding, et al.
Published: (2025)
by: Yang, Jiuding, et al.
Published: (2025)
WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models
by: Lei, Xinping, et al.
Published: (2026)
by: Lei, Xinping, et al.
Published: (2026)
Unveiling Project-Specific Bias in Neural Code Models
by: Li, Zhiming, et al.
Published: (2022)
by: Li, Zhiming, et al.
Published: (2022)
Mutation-based Evaluation of Cryptographic API Misuse Detectors
by: Ami, Amit Seal, et al.
Published: (2021)
by: Ami, Amit Seal, et al.
Published: (2021)
Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems
by: Sun, Zhensu, et al.
Published: (2022)
by: Sun, Zhensu, et al.
Published: (2022)
Towards Leveraging Large Language Model Summaries for Topic Modeling in Source Code
by: Carissimi, Michele, et al.
Published: (2025)
by: Carissimi, Michele, et al.
Published: (2025)
Theory of Code Space: Do Code Agents Understand Software Architecture?
by: Sapunov, Grigory
Published: (2026)
by: Sapunov, Grigory
Published: (2026)
Neuron-Guided Interpretation of Code LLMs: Where, Why, and How?
by: Yin, Zhe, et al.
Published: (2025)
by: Yin, Zhe, et al.
Published: (2025)
Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach
by: Wan, Yao, et al.
Published: (2024)
by: Wan, Yao, et al.
Published: (2024)
Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization
by: Li, Jiliang, et al.
Published: (2024)
by: Li, Jiliang, et al.
Published: (2024)
Towards Better Code Understanding in Decoder-Only Models with Contrastive Learning
by: Lin, Jiayi, et al.
Published: (2024)
by: Lin, Jiayi, et al.
Published: (2024)
Similar Items
-
On Interpreting the Effectiveness of Unsupervised Software Traceability with Information Theory
by: Palacio, David N., et al.
Published: (2024) -
Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations
by: Palacio, David N., et al.
Published: (2024) -
How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study
by: Velasco, Alejandro, et al.
Published: (2024) -
Toward Neurosymbolic Program Comprehension
by: Velasco, Alejandro, et al.
Published: (2025) -
SnipGen: A Mining Repository Framework for Evaluating LLMs for Code
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2025)