Multilingual execution-grounded benchmark finds top open code LLM at 23.64% correctness versus 57.2% human baseline, with compile errors dominating 63% of failures.
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A code-and-comment analysis method detects semantic clones in Solidity functions with 59% overall precision (84% for same-name functions) and 97% recall on 300k contracts, plus LLM summaries for uncommented code.
LLMs achieve strong results on syntax parsing tasks but show limited and variable performance on dynamic reasoning, with a clear performance hierarchy across model scales.
An LLM evolutionary framework generates executable heuristics for frame-level QP adaptation in VVenC that improve rate-distortion performance over fixed-QP and Lagrangian baselines.
RSA prompting enables LLMs to automatically create functional exploits for CVEs in Odoo ERP, succeeding on all tested cases in 3-5 rounds and removing the need for manual effort.
Empirical analysis of AI help-seeking trajectories in programming courses finds reactive troubleshooting dominates and links to higher submission counts without score differences.
AI IDEs with structured guidance can produce functional large-scale code but frequently introduce design flaws such as duplication, complexity, and principle violations that risk long-term maintainability.
User study reveals nine LLM failure categories in SE tasks and quantifies abandonment factors from 26 participants.
A survey of user studies on LLM use in programming that identifies interaction behaviors, mixed benefits and weaknesses, and factors influencing human and task performance.
citing papers explorer
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Beyond Pass Rate: A Multilingual, Execution-Grounded Evaluation of Open Code LLMs
Multilingual execution-grounded benchmark finds top open code LLM at 23.64% correctness versus 57.2% human baseline, with compile errors dominating 63% of failures.
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Identifying and Characterizing Semantic Clones of Solidity Functions
A code-and-comment analysis method detects semantic clones in Solidity functions with 59% overall precision (84% for same-name functions) and 97% recall on 300k contracts, plus LLM summaries for uncommented code.
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Exploring Code Analysis: Zero-Shot Insights on Syntax and Semantics with LLMs
LLMs achieve strong results on syntax parsing tasks but show limited and variable performance on dynamic reasoning, with a clear performance hierarchy across model scales.
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LLM-Driven Heuristic Frame-Level Quantization Parameter Adaptation for VVenC
An LLM evolutionary framework generates executable heuristics for frame-level QP adaptation in VVenC that improve rate-distortion performance over fixed-QP and Lagrangian baselines.
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From Rookie to Expert: Manipulating LLMs for Automated Vulnerability Exploitation in Enterprise Software
RSA prompting enables LLMs to automatically create functional exploits for CVEs in Odoo ERP, succeeding on all tested cases in 3-5 rounds and removing the need for manual effort.
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AI-Assisted Help-Seeking Trajectories in Programming Education from an SRL-Informed Perspective
Empirical analysis of AI help-seeking trajectories in programming courses finds reactive troubleshooting dominates and links to higher submission counts without score differences.
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Beyond Functional Correctness: Design Issues in AI IDE-Generated Large-Scale Projects
AI IDEs with structured guidance can produce functional large-scale code but frequently introduce design flaws such as duplication, complexity, and principle violations that risk long-term maintainability.
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"Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineering
User study reveals nine LLM failure categories in SE tasks and quantifies abandonment factors from 26 participants.
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Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks
A survey of user studies on LLM use in programming that identifies interaction behaviors, mixed benefits and weaknesses, and factors influencing human and task performance.