How does the token truncation strategy impact LLaMA 3.2's precision on BugsInPy when compared to a sliding win

Abstract

Abstract: Large language models (LLMs) have demonstrated strong performance on a wide range of software engineering tasks, including code generation and analysis. However, most prior work relies on cloud-based models or specialized hardware, limiting practical applicability in privacy-sensitive or resource-constrained environments. In this paper, we present a systematic empirical evaluation of two locally deployed LLMs, LLaMA 3.2 and Mistral, for real-world Python bug detection using the BugsInPy benchmark. We evaluate 349 bugs across 17 projects using a zero-shot prompting approach at the function leve

Research Question

How does the token truncation strategy impact LLaMA 3.2's precision on BugsInPy when compared to a sliding window approach, measured by mean average precision (mAP) across 100 Python repositories?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

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Provenance