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

TL;DR

SIMCOPILOT tackles the challenge of evaluating LLMs as interactive copilots by introducing two language-specific benchmarks (SIMCOPILOTJ for Java and SIMCOPILOTP for Python) that test both completion and infill within realistic, multi-file codebases. The authors detail an end-to-end pipeline—annotation, pre-processing, and post-processing—across 1,163 curated tasks derived from private Java and Python repositories, enabling fine-grained analysis of contextual understanding, variable scope, and cross-reference dependencies. Their results reveal substantial model gaps that are muted in traditional benchmarks, with infill generally easier than completion for large models and proximity to comments significantly boosting performance; post-processing further amplifies accuracy, especially for smaller models and Java. The work emphasizes practical implications for LLM-based coding partners and provides an open-source framework intended for ongoing community contributions to improve real-world software development with AI assistance.

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.

Figures (8)

• Figure 1: Workflow for each of the $1,163$ programming tasks in SimCopilot.
• Figure 2: Pass rate group by Python Construct.
• Figure 3: Pass rate grouped by Java Construct.
• Figure 4: Pass rate grouped by reference object distance.
• Figure 5: Pass rate grouped by distance to the nearest comment.