UCRBench: Benchmarking LLMs on Use Case Recovery

TL;DR

This paper introduces UCRBench, a new benchmark for evaluating large language models on use case recovery from source code, highlighting their capabilities and limitations in real-world software systems.

Contribution

The paper presents a manually validated, code-aligned use case benchmark across nine projects and a hierarchical evaluation protocol for LLMs in use case generation.

Findings

LLMs can partially reconstruct system functionality.

Performance varies significantly across projects.

High omission rates and difficulty maintaining abstraction.

Abstract

Use cases are widely employed to specify functional requirements, yet existing benchmarks are scarce and face the risk of being misaligned with actual system behavior, similarly limiting the rigorous evaluation of large language models (LLMs) in generating use cases from source code. We address this gap by introducing code-aligned use case benchmarks, constructed through manual validation of both user-goal and subfunction use cases across nine real-world software projects. Using this benchmark, we conduct the first systematic study of LLMs and propose a hierarchical evaluation protocol that assesses actor correctness, name accuracy, path fidelity, and behavioral coverage. The results show that while LLMs can partially reconstruct system functionality, their performance varies significantly across projects, with particularly noticeable shortcomings in domain-specific and multi-module systems. The models also exhibit high omission rates and struggle to maintain consistent abstraction when aggregating subfunctions into user-goal use cases, highlighting both the potential and current limitations of LLM-based use case reverse engineering.