Minxiao Li

Peiding Wang, Li Zhang, Fang Liu et al.

Large Language Models (LLMs) have demonstrated exceptional performance in code generation tasks and have become indispensable programming assistants for developers. However, existing code generation benchmarks primarily assess the functional correctness of code generated by LLMs in single-turn interactions. They offer limited insight into LLMs' abilities to generate code that strictly follows users' instructions in multi-turn interaction scenarios. In this paper, we introduce CodeIF-Bench, a benchmark for evaluating the instruction-following capabilities of LLMs in interactive code generation. Specifically, CodeIF-Bench incorporates nine types of verifiable instructions aligned with the real-world software development requirements, which can be independently and objectively validated through specified test cases, facilitating the evaluation of instruction-following capability in multi-turn interactions. In both \textit{Static Conversation} and \textit{Dynamic Conversation} settings, we evaluate the performance of 7 state-of-the-art LLMs and summarize the important factors influencing the instruction-following ability of LLMs in multi-turn interactions, as well as potential directions for improvement.

Minxiao Li, Shuying Yan, Li Zhang et al.

Recently, Large Language Models (LLMs) have demonstrated significant potential in automating software engineering tasks. Generating software architecture designs from requirement documents is a crucial step in software development. However, there is currently a lack of functional datasets tailored for this task. To bridge this gap, we introduce R2ABench (Requirement-To-Architecture Benchmark), a novel benchmark comprising diverse real-world software projects paired with comprehensive Product Requirements Documents (PRDs) and expert-curated PlantUML reference diagrams. Furthermore, we propose a multi-dimensional, hybrid evaluation framework that assesses generated diagrams across three complementary layers: Structural Graph Metrics, Multi-dimensional Scoring, and Architecture Anti-pattern Detection. Using this framework, we conducted a comprehensive empirical study evaluating state-of-the-art models and agentic workflows. Our study shows that LLMs show strong syntactic validity and robust entity extraction but fundamentally struggle with relational reasoning, leading to structurally fragmented architectures. Code-specialized models partially alleviate this limitation, while agent frameworks introduce significant instability rather than consistent improvements. R2ABench provides a robust and standardized foundation for advancing LLM-driven software architecture generation.