SIMCOPILOT: Evaluating Large Language Models for Copilot-Style Code Generation
This work addresses the need for realistic evaluation frameworks for LLMs in practical coding scenarios, though it is incremental as it builds on existing benchmarking efforts.
The authors tackled the problem of evaluating large language models as interactive coding assistants by introducing SIMCOPILOT, a benchmark for completion and infill tasks in Java and Python, which revealed insights into model strengths and challenges in maintaining logical consistency.
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.