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Showing 2026 · cs.SEShow all
3 papers · 2 filters
cs.SE2026
Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering
Daniel Rodriguez-Cardenas, Xiaochang Li, Marcos Macedo +5
Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical ga…
cs.SE2026★ 1 cited
Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis
Dipin Khati, Daniel Rodriguez-Cardenas, Paul Pantzer +1
Large Language Models (LLMs) for code generation boost productivity but frequently introduce Knowledge Conflicting Hallucinations (KCHs), subtle, semantic errors, such as non-exist…
cs.SE2026
Tricky: Towards a Benchmark for Evaluating Human and LLM Error Interactions
Cole Granger, Dipin Khati, Daniel Rodriguez-Cardenas +1
Large language models (LLMs) are increasingly integrated into software development workflows, yet they often introduce subtle logic or data-misuse errors that differ from human bug…