Language models (LMs) have gained ground as aids in software engineering, where users act as intermediaries between LMs and computers, refining LM-generated code based on feedback from the computer. Recent advances show that LMs operate autonomously in computing environments, potentially accelerating software development. However, the practical application of this autonomous approach still needs to be explored.
Code generation benchmarks serve as crucial metrics for evaluating LM performance, and evolve to include various tasks, such as translating problems into different programming languages and incorporating third-party libraries. While traditional benchmarks may become saturated due to the rapid development of LM, recent efforts explore the more complex landscape of software engineering (SE). This shift led to the emergence of SE benchmarks such as SWE-bench, which reflect real-world SE challenges and show the potential of LMs in practical settings. Furthermore, the rise of linguistic agents means a paradigm shift towards interactive LM environments, with applications spanning web browsing, computer control, and code generation tasks.
Researchers from Princeton Language and Intelligence (PLI), Princeton University present SWE agent, a LM-based autonomous system that addresses real-world software engineering challenges from SWE-bench. It works by issuing thoughts and commands and then receiving feedback from the execution of commands using the ReAct environment. The central idea lies in designing an agent-computer interface (ACI) adapted to LMs, which surpasses traditional interfaces such as the Linux shell. The insufficiency of the Linux shell for LM interaction drives the creation of an effective ACI for the SWE agent, significantly improving performance with commands for file manipulation and informational feedback.
SWE-agent revolutionizes LM interaction in software engineering by providing a custom ACI to navigate, edit, and execute code commands. Unlike traditional interfaces designed for human users, SWE-agent's ACI addresses the specific needs and limitations of LM, significantly improving performance. ACI comprises search/navigation, file viewing, file editing, and context management components, ensuring efficient navigation and editing of the codebase while minimizing distractions and errors. The integration of a code linter by the SWE agent alerts the model to errors during file editing, ensuring code quality. Context management includes concise prompts, error messages, and history processors to maintain agent informational context and improve interaction clarity.
The SWE agent, together with GPT-4 Turbo, achieves superior performance, solving 12.47% and 18.00% of the full test suite of the SWE bench and Lite division, respectively. Iterative search interfaces, which resemble traditional user interfaces such as Vim or VSCode, provide search results sequentially through the file viewer. However, an exhaustive search can hamper efficiency. SWE-agent's file editor allows efficient multi-line edits with immediate feedback, in contrast to the restrictive options in the Shell-only configuration. Error recovery guardrails mitigate repetitive editing due to syntax errors, improving overall performance.
In conclusion, this research presents SWE-agent, a language agent designed for software engineering tasks, which shows state-of-the-art performance on SWE-bench. This approach highlights the importance of designing ACIs specific to agent needs, as demonstrated by its methodology, empirical findings, and analysis. The researchers provided their code, hints, and generations, along with a flexible codebase for future extensions. SWE-agent aims to inspire advancements in agent versatility and capability for future projects.
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Asjad is an internal consultant at Marktechpost. He is pursuing B.tech in Mechanical Engineering at Indian Institute of technology, Kharagpur. Asjad is a machine learning and deep learning enthusiast who is always researching applications of machine learning in healthcare.
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