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arXiv · 2505.21514

SIMCOPILOT: Evaluating Large Language Models for Copilot-Style Code Generation

Mingchao Jiang·Abhinav Jain·Sophia Zorek·Chris Jermaine
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Abstract

We introduce SIMCOPILOT, a benchmark that simulates the role of large language models (LLMs) as interactive, "copilot"-style coding assistants. Targeting both completion (finishing incomplete methods or code blocks) and infill tasks (filling missing segments within existing code), SIMCOPILOT provides a comprehensive framework for evaluating LLM coding capabilities. The benchmark comprises dedicated sub-benchmarks for Java (SIMCOPILOTJ) and Python (SIMCOPILOTP), covering diverse codebases varying in size and complexity. Our key contributions include: (a) establishing a realistic, detailed evaluation environment to assess LLM utility in practical coding scenarios, and (b) providing fine-grained analyses that address critical factors frequently overlooked by existing benchmarks, such as task-specific performance nuances, contextual understanding across code segments, and sensitivity to variable scope. Evaluations conducted across domains-including algorithms, databases, computer vision, and neural networks-offer insights into model strengths and highlight persistent challenges in maintaining logical consistency within complex dependency structures. Beyond benchmarking, our study sheds light on the current limitations of LLM-driven code generation and underscores the ongoing transition of LLMs from merely syntax-aware generators toward reliable, intelligent software development partners.

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Mingchao Jiang, Abhinav Jain, Sophia Zorek, Chris Jermaine. 2025-05-21. SIMCOPILOT: Evaluating Large Language Models for Copilot-Style Code Generation. https://arxiv.org/abs/2505.21514

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