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

Summary

Recently, Large Language Models (LLMs) based agents have revealed huge potential in automating tasks. In this paper,

Strengths

This work has presented a powerful AI Agent designed for software engineering, which configured agents with multiple functions like file viewer, file editor and so on. Such a system can significantly improve the capability of agent in programming and doing some software engineering works. Experimental results also demonstrate the capability of SWE-agent in solving tasks in SWE-bench.

Weaknesses

1. This paper is a good engineering work but lack some insights from the perspective of methodology. For example, why we must choose search, navigation, file viewer / editor? Is it possible to add some other operations? It will be better to add some observations or ablation studies to support the motivation of this design, not just some system claims (e.g., Line 85 - 101). 2. Compared with previous works like ReAct, SWE-agent more likes a version that extended with multiple sophisticated functions. 3. Authors claim an agent-computer interface, but actually, it focused on coding or like a software engineering. I don't think that the proposed method can be considered as an agent-computer interface, as computer usually involves more complex functions (e.g., drawing, gaming, web search, and so on). I think such a claim is a little over-claim.

Questions

1. Can you highlight the differences between SWE-agent and MetaGPT? MetaGPT is a multi-agent programming framework designed from software engineering. It also incorporates many unique designs in terms of software engineering. 2. Can the proposed SWE-agent to other benchmarks? It seems like the design of SWE-agent is specialized for SWE-bench.

Rating

6

Confidence

5

Soundness

2

Presentation

3

Contribution

2

Limitations

Please see my comments on weaknesses and questions.

Summary

This paper introduces SWE-agent, a system that enables language models to autonomously perform software engineering tasks by interacting with computers through a specially designed agent-computer interface (ACI). The authors argue that LM agents represent a new category of end users with unique requirements, necessitating interfaces tailored to their strengths and limitations. SWE-agent's ACI is designed to enhance an agent's ability to create and edit code files, navigate repositories, and execute tests and programs. The paper evaluates SWE-agent on two benchmarks: SWE-bench and HumanEvalFix. On SWE-bench, SWE-agent achieves state-of-the-art performance with a pass@1 rate of 12.5%, significantly outperforming previous non-interactive approaches. On HumanEvalFix, it achieves an impressive 87.7% pass@1 rate. The authors provide detailed analyses of how different ACI design choices impact agent behavior and performance.

Strengths

* Strong empirical results: SWE-agent demonstrates significant improvements over previous approaches on challenging benchmarks, particularly on SWE-bench where it more than triples the previous best published performance. * Novel and important concept: The introduction of ACIs as a distinct category from human-computer interfaces is an important contribution that could shape future research in AI agent systems. * Detailed analysis: The paper provides extensive ablation studies and analyses of agent behavior, offering valuable insights into how different interface design choices affect performance.

Weaknesses

* Generalizability: The paper focuses primarily on software engineering tasks. It's unclear how well the ACI design principles would generalize to other domains and how ACI could be implemented more broadly.

Questions

How might the principles of ACI design extend to other domains beyond software engineering? Are there specific challenges you anticipate in adapting this approach to different types of tasks?

Rating

7

Confidence

4

Soundness

4

Presentation

3

Contribution

4

Limitations

Limitations are thoroughly discussed

Summary

The paper introduces SWE-agent, a system designed to enable language model (LM) agents to autonomously perform software engineering tasks through a custom agent-computer interface (ACI). The study posits that LM agents can benefit from interfaces tailored to their specific needs, similar to how human software engineers use integrated development environments (IDEs). The SWE-agent system demonstrates significant improvements in task performance on the SWE-bench and HumanEvalFix benchmarks compared to non-interactive LMs, achieving state-of-the-art results.

Strengths

**Novel Concept:** The introduction of the ACI tailored specifically for LM agents represents a significant innovation, addressing the unique needs of LMs in software engineering tasks. **Empirical Results:** The system achieves impressive performance metrics on established benchmarks, indicating the effectiveness of the ACI design. **Comprehensive Evaluation:** The paper includes a thorough analysis of the system's performance, including ablation studies and comparisons with baseline models. **Open-Source Contribution:** The authors provide anonymized code and data, contributing to the research community and facilitating reproducibility.

Weaknesses

**Complexity and Generalizability:** The system's reliance on specific design choices and configurations might limit its generalizability to other types of LMs or software engineering tasks. **Overhead and Efficiency:** While the ACI improves performance, it introduces additional layers of complexity and potential overhead that are not fully quantified in terms of computational resources and efficiency.

Questions

- What are the specific computational and resource overheads introduced by the ACI, and how do they impact overall system efficiency? - How generalizable are the ACI design principles to other domains beyond software engineering, and what modifications would be necessary? - Can the ACI be adapted for use with other language models with smaller context windows, and what would be the expected impact on performance?

Rating

8

Confidence

4

Soundness

4

Presentation

4

Contribution

3

Limitations

n/a

Summary

The paper presents SWE-agent, a system designed to enhance language model (LM) agents' performance in software engineering tasks through a specialized agent-computer interface (ACI). The ACI allows LMs to efficiently navigate, edit, and execute code within repositories, significantly improving performance over traditional non-interactive LMs. SWE-agent demonstrates good results on benchmarks such as SWE-bench and HumanEvalFix, showcasing a substantial increase in task resolution rates. The authors highlight the importance of designing interfaces tailored specifically for LMs, drawing parallels to human-computer interaction studies. The paper provides detailed experimental results and ablation studies to validate the efficacy of the ACI.

Strengths

The key idea of designing a specialized interface for LMs to interact with software environments is novel and well-executed. By abstracting and simplifying the interaction process, the authors address a significant gap in current LM capabilities. The experimental validation is thorough, with SWE-agent outperforming existing systems on established benchmarks, showing the practical significance of the approach. The writing is clear, making complex ideas accessible, and the data contribution is valuable, with detailed ablation studies shedding light on the importance of various design choices. Additionally, the open-sourcing of SWE-agent and its dataset contributions provide a useful resource for the research community, encouraging further exploration and validation of the proposed approach.

Weaknesses

1. The process of managing context history within the ACI is not clearly explained, leaving ambiguity about how the system maintains essential information while avoiding unnecessary context. 2. The method for optimizing the ACI configuration through manual inspection and grid search lacks transparency and could be elaborated further. 3. The system's error recovery mechanisms, while improved with linting, may still struggle with repeated failed edits, indicating a potential area for further enhancement. 4. The ACI seems to only support a limited number of tools -- how to expand it to other more advanced and useful tools are unclear.

Questions

1. How does the ACI manage context history to maintain essential information while avoiding unnecessary context? Note that the context length would be a bottleneck for many open-source LLMs. 2. How does the system handle scenarios where the LM encounters repeated failed edits despite the linter's intervention? 3. It seems that SWE-Agent is not a SOTA framework for SWE-bench. I am curious what the authors think about the recent SOTA approaches, and what would be the most valuable and promising directions for the next-gen of SWE-agent?

Rating

6

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Limitations

See above.

Response to authors

Thank you for the response and discussion, I think the work is interesting. I will keep the score.

Reviewer BuFF & N4zh

Please respond to the authors at your earliest opportunity. Today is the last day to engage.