mlsanityData to 7 Oct 2026

Robustness, Security, Privacy, Explainability, Efficiency, and Usability of Large Language Models for Code

arXiv 2403.07506 ↗First posted 12 Mar 2024cs.SEcited by 26

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Abstract (arXiv)

Large language models for code (LLM4Code), which demonstrate strong performance (e.g., high accuracy) in processing source code, have significantly transformed software engineering. Many studies separately investigate the non-functional properties of LM4Code, but there is no systematic review of how these properties are evaluated and enhanced. This paper fills this gap by thoroughly examining 146 relevant studies, thereby presenting the first systematic literature review to identify seven important properties beyond accuracy, including robustness, security, privacy, explainability, efficiency, and usability. We discuss the current state-of-the-art methods and trends, identify gaps in existing research, and present promising directions for future study.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 11; 2025: 11; 2026: 4.

26 papers.

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  1. 2024A Survey on Large Language Models for Code Generationcited by 646
  2. 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Futurecited by 85
  3. 2024CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferencescited by 56
  4. 2025Can LLMs Reason About Program Semantics? A Comprehensive Evaluation of LLMs on Formal Specification Inferencecited by 14
  5. 2024LiCoEval: Evaluating LLMs on License Compliance in Code Generationcited by 13
  6. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 13
  7. 2024Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directionscited by 10
  8. 2025Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safetycited by 10
  9. 2024AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generationcited by 8
  10. 2024Towards Agentic Runtime Healingcited by 7
  11. 2025Evaluating the Generalization Capabilities of Large Language Models on Code Reasoningcited by 7
  12. 2025Towards Advancing Code Generation with Large Language Models: A Research Roadmapcited by 5
14 more
  1. 2024Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorizationcited by 4
  2. 2025Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models?cited by 4
  3. 2025"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistantscited by 4
  4. 2024CodeCipher: Learning to Obfuscate Source Code Against LLMscited by 3
  5. 2025Smaller = Weaker? Benchmarking Robustness of Quantized LLMs in Code Generationcited by 3
  6. 2024The Rise and Fall(?) of Software Engineeringcited by 1
  7. 2025Mitigating Sensitive Information Leakage in LLMs4Code through Machine Unlearningcited by 1
  8. 2025EVALOOOP: A Self-Consistency-Centered Framework for Assessing Large Language Model Robustness in Programmingcited by 1
  9. 2025Deconstructing Obfuscation: A four-dimensional framework for evaluating Large Language Models assembly code deobfuscation capabilitiescited by 1
  10. 2024CodePurify: Defend Backdoor Attacks on Neural Code Models via Entropy-based Purificationcited by 0
  11. 2026AST-PAC: AST-guided Membership Inference for Codecited by 0
  12. 2026Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generationcited by 0
  13. 2026Noisy Test-Time Reinforcement Learning for Code LLMscited by 0
  14. 2026Runnable Commit Untangling for Coding Agentscited by 0