Cover Image

On Interpreting the Effectiveness of Unsupervised Software Traceability with Information Theory

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Palacio, David N., Rodriguez-Cardenas, Daniel, Poshyvanyk, Denys, Moran, Kevin
Format:	Preprint
Published:	2024
Subjects:	Software Engineering Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations
by: Palacio, David N., et al.
Published: (2024)

Toward a Theory of Causation for Interpreting Neural Code Models
by: Palacio, David N., et al.
Published: (2023)

Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives
by: Khati, Dipin, et al.
Published: (2025)

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study
by: Velasco, Alejandro, et al.
Published: (2024)

Towards Enabling An Artificial Self-Construction Software Life-cycle via Autopoietic Architectures
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2026)

Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis
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)

SnipGen: A Mining Repository Framework for Evaluating LLMs for Code
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2025)

Toward Neurosymbolic Program Comprehension
by: Velasco, Alejandro, et al.
Published: (2025)

Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2026)

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering
by: Palacio, David N.
Published: (2025)

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

"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)

Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code?
by: Velasco, Alejandro, et al.
Published: (2024)

Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development
by: Stalnaker, Trevor, et al.
Published: (2024)

Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation
by: Khati, Dipin, et al.
Published: (2025)

Toward Explaining Large Language Models in Software Engineering Tasks
by: Vitale, Antonio, et al.
Published: (2025)

Rethinking Software Empirical Studies with Structural Causal Models
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2026)

A Path Less Traveled: Reimagining Software Engineering Automation via a Neurosymbolic Paradigm
by: Mastropaolo, Antonio, et al.
Published: (2025)

Validating Network Protocol Parsers with Traceable RFC Document Interpretation
by: Zheng, Mingwei, et al.
Published: (2025)

Testing Practices, Challenges, and Developer Perspectives in Open-Source IoT Platforms
by: Rodriguez-Cardenas, Daniel, et al.
Published: (2025)

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

Understanding Privacy Risks in Code Models Through Training Dynamics: A Causal Approach
by: Yang, Hua, et al.
Published: (2025)

Natural Language-Programming Language Software Traceability Link Recovery Needs More than Textual Similarity
by: Zou, Zhiyuan, 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)

How Do Semantically Equivalent Code Transformations Impact Membership Inference on LLMs for Code?
by: Yang, Hua, et al.
Published: (2025)

Challenges and Practices in Quantum Software Testing and Debugging: Insights from Practitioners
by: Zappin, Jake, et al.
Published: (2025)

Bridging the Quantum Divide: Aligning Academic and Industry Goals in Software Engineering
by: Zappin, Jake, et al.
Published: (2025)

Prompting in Practice: Investigating Software Practitioners' Use of Generative AI Tools
by: Otten, Daniel, et al.
Published: (2025)

Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education
by: Mojica-Hanke, Anamaria, et al.
Published: (2024)

More Is Different: Toward a Theory of Emergence in AI-Native Software Ecosystems
by: Russo, Daniel
Published: (2026)

Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate Video-based Bug Reports
by: Yan, Yanfu, et al.
Published: (2024)

Traceability and Accountability in Role-Specialized Multi-Agent LLM Pipelines
by: Barrak, Amine
Published: (2025)

Enhancing Interpretability in Software Change Management with Chain-of-Thought Reasoning
by: Sun, Yongqian, et al.
Published: (2025)

Large Language Models for Software Testing: A Research Roadmap
by: Augusto, Cristian, et al.
Published: (2025)

Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study
by: Ceka, Ira, et al.
Published: (2025)

Reproducible, Explainable, and Effective Evaluations of Agentic AI for Software Engineering
by: Li, Jingyue, et al.
Published: (2026)

Mutation-based Evaluation of Cryptographic API Misuse Detectors
by: Ami, Amit Seal, et al.
Published: (2021)

SpecMap: Hierarchical LLM Agent for Datasheet-to-Code Traceability Link Recovery in Systems Engineering
by: Nipane, Vedant, et al.
Published: (2026)

Theory of Code Space: Do Code Agents Understand Software Architecture?
by: Sapunov, Grigory
Published: (2026)