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

evergreen - Agents

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

• Thesis: Agent-Computer Interfaces (ACIs) can significantly enhance the ability of language model agents to autonomously perform software engineering tasks. The paper introduces SWE-agent, a system that leverages these interfaces to enable language models, such as GPT-4 or other large language models, to interact with computers effectively. This interaction allows the agent to autonomously address tasks like bug fixing, competitive coding, and even offensive cybersecurity challenges
• Contribution:
  • Introduce ACI and its importance to improve LM agent’s performance
  • SWE-agent
• Takeaways: Spending time on building well-crafted ACI will yield the best performance boost for LM agents. The current state of UI has only human end users in my which is completely different than LM agent than has different needs and abilities. ACI in a major step forward to enhance LM agents to search and execute commands and integrate environment feedback into their state
• Notes:
  • LM agent represents a new category of end users. Carefully designing the agent-computer interface (ACI) helps improve the LM performance and ability to solve complex tasks
  • SWE-agent is a system that solves SWE tasks by creating ACI that allows the agent to create/edit files, navigate repositories, and execute tests/programs
  • ACI is just an abstraction that provides the LM agent with an interface to simple few commands such as viewing and editing files as well as searching and navigating repositories. It also provides well-formatted/structured feedback for the agent’s actions. Finally, it has some guardrails to prevent common mistakes
    • The current UI are built with only human end users in mind. However, LM agent has different abilities and needs. For example, LM agent suffers (as of now) to grasp the GUI and not get destracted with all unnecessary details that would also harm its performance
    • ACI provides actions that the LM agent can take as well as how the environment feedback is represented to the agent. It should specifies the previous changes, manage history in terms of what to ignore and what to keep an eye on, and actions that it can take efficiently and reliably
  • For a good ACI:
    • Actions should be simple with concise documentation
    • Environment feedback should be informative and concise
    • Consolidate actions especially the common ones
    • Have guardrails to prevent common mistakes
  • The agent follows ReAct framework by generating a thought and action at each step. It then incorporate the feedback from the executed commands
  • The agent has access to common Linux commands
  • Search & Navigation:Agent has access to search for files and strings that are mentioned in the issues. Commands are find_file, search_file, search_directory. Results returned are at most 50 results for each query
  • File viewer: open a file in a window using open command and restrict it to 100 lines. The agent can use scroll_down and scroll_up to move the window as well as goto to jump to specific line. Code localization is provided by presenting the full path of the file and number of lines before/after the current line as well as the total number of lines in the file
  • File editor. edit command lets the agent to edit range of lines while viewing the file. Code linter is used to provide feedback to the agent in terms of syntax errors in the form of file contents before/after error was introduced. If there is an error, the agent is asked to retry the edit
  • Context management. The agent receives prompts, instructions, documentation, and demonstration of the bash and ACI commands. If the agent fail to generate a response that strictly follow the thought/action paradigm, it triggers an error until one is produced

#nlp #llm #agents