SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

🌈 Abstract

The article discusses the use of language model (LM) agents to automate complicated tasks in digital environments. It introduces the concept of the agent-computer interface (ACI), which is the interface that LM agents use to interact with computers. The authors argue that current human-computer interfaces are not always suitable for LM agents, and that designing LM-centric ACIs can significantly enhance an agent's ability to perform software engineering tasks.

The authors present SWE-agent, a system that provides LMs with an ACI for solving real-world software engineering problems. SWE-agent's ACI includes commands for navigating repositories, viewing and editing files, and executing tests and other programs. The authors evaluate SWE-agent on the SWE-bench and HumanEvalFix benchmarks, achieving state-of-the-art performance.

🙋 Q&A

[01] Introduction

1. What is the key motivation behind the work on SWE-agent? The key motivation is that language model (LM) agents are increasingly being used to automate complicated tasks in digital environments, and the authors posit that LM agents represent a new category of end users with their own needs and abilities. They hypothesize that LM agents would benefit from specially-built interfaces to the software they use, similar to how humans benefit from powerful software applications like integrated development environments for complex tasks like software engineering.

2. What are the limitations of current approaches that motivate the need for an agent-computer interface (ACI)? The authors find that current approaches, where LM agents directly use existing applications like the Linux shell or Python interpreter, struggle with reliable and efficient task completion. For example, LM agents can fail to provide simple commands to edit a small file segment, and do not receive feedback if they make an invalid edit. These deficits substantially hamper performance, motivating the need for an ACI to enhance the LM agent's abilities in computer environments.

[02] The Agent-Computer Interface

1. How do the authors define the agent-computer interface (ACI)? The authors define the ACI as the interface that LM agents use to interact with computers. They argue that the ACI should be designed to complement an LM's limitations and abilities, in contrast to existing interfaces that have been designed with human users in mind.

2. What are the key design principles the authors identify for building effective ACIs? The authors identify four key design principles for building effective ACIs:

1. Actions should be simple and easy to understand for agents.
2. Actions should be compact and efficient.
3. Environment feedback should be informative but concise.
4. Guardrails should be used to mitigate error propagation and hasten recovery.

[03] SWE-agent: Designing an ACI for Software Engineering

1. What are the main components of the SWE-agent ACI? The main components of the SWE-agent ACI are:

• Search and navigation: Commands to search for files and content within files/directories
• File viewer: An interface to view file contents with contextual information
• File editor: Commands to edit files, with linting to prevent syntax errors
• Context management: Prompts, error messages, and history processing to keep agent context concise and informative

2. How does the SWE-agent ACI differ from a standard Linux shell environment? The key differences are:

• Simplified, LM-friendly commands instead of complex shell commands
• Consolidated actions to perform higher-level operations in a single step
• Informative but concise environment feedback, including linting to prevent edit errors
• Guardrails to help agents recover from mistakes

[04] Experimental Setup

1. What are the main datasets used to evaluate SWE-agent? The main datasets used are:

• SWE-bench: A dataset of 2,294 real-world software engineering tasks from 12 different repositories
• SWE-bench Lite: A subset of 300 instances from SWE-bench focused on self-contained functional bug fixes
• HumanEvalFix: A short-form code debugging benchmark

2. What are the key metrics used to evaluate SWE-agent's performance? The key metrics are:

• % Resolved or pass@1: The proportion of instances for which all tests pass successfully after the agent's generated patch is applied
• $ Avg. Cost: The average API inference cost incurred by SWE-agent across successfully resolved instances

[05] Results

1. How does SWE-agent's performance compare to the baselines? SWE-agent w/ GPT-4 Turbo achieves the best performance, solving 12.47% of the full SWE-bench test set and 18.00% of the SWE-bench Lite test set. This significantly outperforms the previous state-of-the-art non-interactive, retrieval-augmented system, which had a 3.8% resolve rate on SWE-bench. SWE-agent also outperforms the Shell-only baseline by 64% on SWE-bench Lite.

2. What insights do the ablation studies provide about the impact of different ACI design choices? The ablation studies show that:

• Compact, efficient file editing is critical to performance. The "No edit" setting leads to a 7.7 percentage point drop in performance.
• Guardrails that prevent edit errors can significantly improve performance. Removing the linting checks leads to a 3 percentage point drop.
• The design of the search interface also impacts performance, with the "Summarized" search outperforming the "Iterative" search.