{"total":37,"rows":[{"id":"2306.17193","title":"Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection","created":"2023-06-28","cited_by_count":30,"area":null},{"id":"2106.03353","title":"Understanding Neural Code Intelligence Through Program Simplification","created":"2021-06-07","cited_by_count":22,"area":null},{"id":"2106.08704","title":"Memorization and Generalization in Neural Code Intelligence Models","created":"2021-06-16","cited_by_count":19,"area":null},{"id":"2408.11081","title":"What can Large Language Models Capture about Code Functional Equivalence?","created":"2024-08-20","cited_by_count":14,"area":null},{"id":"2204.10200","title":"An Exploratory Study on Code Attention in BERT","created":"2022-04-05","cited_by_count":12,"area":null},{"id":"2302.03788","title":"Toward a Theory of Causation for Interpreting Neural Code Models","created":"2023-02-07","cited_by_count":12,"area":null},{"id":"2308.03312","title":"Exploiting Code Symmetries for Learning Program Semantics","created":"2023-08-07","cited_by_count":10,"area":null},{"id":"2205.15479","title":"HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations","created":"2022-05-31","cited_by_count":9,"area":null},{"id":"2310.17903","title":"Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey","created":"2023-10-27","cited_by_count":9,"area":null},{"id":"2401.11105","title":"Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Study","created":"2024-01-20","cited_by_count":9,"area":null},{"id":"2204.06643","title":"Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar","created":"2022-04-13","cited_by_count":8,"area":null},{"id":"2311.07553","title":"An Extensive Study on Adversarial Attack against Pre-trained Models of Code","created":"2023-11-13","cited_by_count":8,"area":null},{"id":"2205.14374","title":"Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models","created":"2022-05-28","cited_by_count":7,"area":null},{"id":"2402.11892","title":"Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testing","created":"2024-02-19","cited_by_count":5,"area":null},{"id":"2111.01097","title":"Code2Snapshot: Using Code Snapshots for Learning Representations of Source Code","created":"2021-11-01","cited_by_count":4,"area":null},{"id":"2503.14281","title":"XOXO: Stealthy Cross-Origin Context Poisoning Attacks against AI Coding Assistants","created":"2025-03-18","cited_by_count":4,"area":null},{"id":"2504.00019","title":"ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding","created":"2025-03-27","cited_by_count":4,"area":null},{"id":"2504.04372","title":"Assessing the Impact of Code Changes on the Fault Localizability of Large Language Models","created":"2025-04-06","cited_by_count":4,"area":null},{"id":"2504.19730","title":"Evaluate-and-Purify: Fortifying Code Language Models Against Adversarial Attacks Using LLM-as-a-Judge","created":"2025-04-28","cited_by_count":4,"area":null},{"id":"2109.10476","title":"Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules","created":"2021-09-22","cited_by_count":3,"area":null},{"id":"2402.05939","title":"Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study","created":"2024-01-12","cited_by_count":3,"area":null},{"id":"2303.01739","title":"Study of Distractors in Neural Models of Code","created":"2023-03-03","cited_by_count":2,"area":null},{"id":"2405.02828","title":"Trojans in Large Language Models of Code: A Critical Review through a Trigger-Based Taxonomy","created":"2024-05-05","cited_by_count":2,"area":null},{"id":"2503.23448","title":"Semantic-Preserving Transformations as Mutation Operators: A Study on Their Effectiveness in Defect Detection","created":"2025-03-30","cited_by_count":2,"area":null},{"id":"2603.23443","title":"Evaluating LLM-Based Test Generation Under Software Evolution","created":"2026-03-24","cited_by_count":2,"area":null},{"id":"2102.04625","title":"WheaCha: A Method for Explaining the Predictions of Models of Code","created":"2021-02-09","cited_by_count":1,"area":null},{"id":"2107.04773","title":"Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic Code Search","created":"2021-07-10","cited_by_count":1,"area":null},{"id":"2112.11226","title":"Energy-bounded Learning for Robust Models of Code","created":"2021-12-20","cited_by_count":1,"area":null},{"id":"2305.00875","title":"Redundancy and Concept Analysis for Code-trained Language Models","created":"2023-05-01","cited_by_count":1,"area":null},{"id":"2512.06042","title":"Auto-SPT: Automating Semantic Preserving Transformations for Code","created":"2025-12-05","cited_by_count":1,"area":null},{"id":"2202.06474","title":"Extracting Label-specific Key Input Features for Neural Code Intelligence Models","created":"2022-02-14","cited_by_count":0,"area":null},{"id":"2303.04942","title":"A Study of Variable-Role-based Feature Enrichment in Neural Models of Code","created":"2023-03-08","cited_by_count":0,"area":null},{"id":"2403.01784","title":"CatCode: A Comprehensive Evaluation Framework for LLMs On the Mixture of Code and Text","created":"2024-03-04","cited_by_count":0,"area":null},{"id":"2502.03365","title":"A Match Made in Heaven? AI-driven Matching of Vulnerabilities and Security Unit Tests","created":"2025-02-05","cited_by_count":0,"area":null},{"id":"2505.19059","title":"An Initial Exploration of Fine-tuning Small Language Models for Smart Contract Reentrancy Vulnerability Detection","created":"2025-05-25","cited_by_count":0,"area":null},{"id":"2602.18800","title":"Operational Robustness of LLMs on Code Generation","created":"2026-02-21","cited_by_count":0,"area":null},{"id":"2608.15412","title":"Invariant Pretraining for Robust Code Representations","created":"2026-08-15","cited_by_count":0,"area":null}],"next_cursor":null}