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

TL;DR

SIMCOPILOT is a comprehensive benchmark for evaluating large language models' effectiveness in copilot-style code generation tasks across multiple programming languages and domains, highlighting current strengths and limitations.

Contribution

It introduces a detailed, realistic evaluation framework for LLMs in coding, including nuanced performance analysis and domain-specific assessments.

Findings

LLMs show strengths in certain coding tasks but struggle with complex dependencies.

Performance varies significantly across domains like algorithms and neural networks.

The benchmark reveals persistent challenges in logical consistency and contextual understanding.

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.