RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code

Recent advances in language model (LM) agents and function calling have enabled autonomous, feedback-driven systems to solve problems across various digital domains. To better understand the unique limitations of LM agents, we introduce RefactorBench, a benchmark consisting of 100 large handcrafted multi-file refactoring tasks in popular open-source repositories. Solving tasks within RefactorBench requires thorough exploration of dependencies across multiple files and strong adherence to relevant instructions. Every task is defined by 3 natural language instructions of varying specificity and is mutually exclusive, allowing for the creation of longer combined tasks on the same repository. Baselines on RefactorBench reveal that current LM agents struggle with simple compositional tasks, solving only 22% of tasks with base instructions, in contrast to a human developer with short time constraints solving 87%. Through trajectory analysis, we identify various unique failure modes of LM agents, and further explore the failure mode of tracking past actions. By adapting a baseline agent to condition on representations of state, we achieve a 43.9% improvement in solving RefactorBench tasks. We further extend our state-aware approach to encompass entire digital environments and outline potential directions for future research. RefactorBench aims to support the study of LM agents by providing a set of real-world, multi-hop tasks within the realm of code. "Repetition is the root of all software evil" -- Martin Fowler Large language models (LLMs) have been quickly acquiring new capabilities (Bubeck et al., 2023), leading towards adoption of AI-powered systems in various formats and domains. The increasing usage of LLM powered tools like Github Copilot have greatly improved the capability of developers in software development tasks (Peng et al., 2023). More recently, an emphasis on multi-step execution through LLM feedback loops has unlocked the ability to solve harder problems within a variety of fields (Reed et al., 2022; Sumers et al., 2024; Yao & Narasimhan, 2023), including parts of software engineering. This new paradigm of solving larger software tasks has led to the construction of a variety of new automated software engineering (ASE) systems, most being structured as LM agents (Wang et al., 2024c; Cognition.ai, Evaluations for such systems are currently largely comprised from real world data on Github (Jimenez et al., 2024; LaBash et al., 2024). While being the strongest open-source signal for software engineering tasks at scale, Github is inherently noisy through its snapshot nature, also requiring strong filtration and validation testing for reliable evaluations (Chowdhury et al., 2024; Bowman & Dahl, 2021).