SWE-Bench: Evaluating Language Models on Real-World Software Engineering Tasks

Core Concepts

Language models struggle to resolve real-world software engineering issues, highlighting the need for more challenging and realistic benchmarks to drive their future development.

Abstract

The paper introduces SWE-bench, a benchmark for evaluating language models on real-world software engineering tasks. SWE-bench consists of 2,294 software engineering problems drawn from GitHub issues and corresponding pull requests across 12 popular Python repositories. The key highlights and insights are: SWE-bench tasks require models to understand and coordinate changes across multiple functions, classes, and files, going beyond traditional code generation tasks. This calls for models to interact with execution environments, process long contexts, and perform complex reasoning. Evaluations show that state-of-the-art language models, including proprietary models and the fine-tuned SWE-Llama, can only resolve the simplest issues. The best-performing model, Claude 2, is able to solve a mere 1.96% of the issues. SWE-bench offers several advantages over existing programming benchmarks, including a realistic setting, diverse inputs, robust execution-based evaluation, and the ability to continuously update with new instances. The authors release SWE-bench-train, a dataset of 19,000 non-testing task instances, and two fine-tuned models, SWE-Llama 7b and 13b, which show some competitiveness with Claude 2 in certain settings. The paper provides a qualitative analysis of model generations, highlighting that models tend to generate shorter, simpler edits compared to human-written solutions, and struggle to leverage the full codebase context.

Stats

The average issue text length is 195.1 words, with a maximum of 4,477 words. The average codebase has 3,010 non-test files and 438,000 non-test lines. The average gold patch makes 32.8 line edits across 1.7 files and 3.0 functions. The average number of fail-to-pass tests is 9.1, with a maximum of 1,633. The average total number of tests is 120.8, with a maximum of 9,459.

Quotes

"Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities." "Resolving issues in SWE-bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes far beyond traditional code generation tasks." "Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous."

Key Insights Distilled From

SWE-bench

by Carlos E. Ji... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2310.06770.pdf

Deeper Inquiries

How can language models be augmented with software engineering tools and practices to improve their performance on SWE-bench tasks

To enhance the performance of language models on SWE-bench tasks, integrating software engineering tools and practices is crucial. One approach is to incorporate tools for code analysis, such as static code analyzers, linters, and code formatters, into the training process. By exposing the models to these tools, they can learn to generate code that adheres to best practices and coding standards. Additionally, integrating version control systems like Git can help models understand the context of code changes and improve their ability to generate accurate patches. Furthermore, leveraging software engineering practices like code reviews and testing can be beneficial. Models can be trained on datasets that include code reviews and feedback, enabling them to generate code that is not only correct but also maintainable and well-structured. Incorporating testing frameworks into the evaluation process can ensure that the generated code not only solves the issue but also passes relevant tests, reflecting real-world software development scenarios. By augmenting language models with these software engineering tools and practices, they can develop a deeper understanding of code quality, maintainability, and functionality, leading to improved performance on SWE-bench tasks.

What are the potential biases and limitations in the SWE-bench dataset, and how can they be addressed to ensure fair and comprehensive evaluation of language models

The SWE-bench dataset may have potential biases and limitations that need to be addressed for a fair and comprehensive evaluation of language models. One bias could be the overrepresentation of certain types of issues or repositories, leading to a skewed evaluation of model performance. To address this, a more diverse set of repositories and issue types could be included in the dataset to ensure a balanced evaluation. Another limitation could be the lack of consideration for different programming languages. Since SWE-bench currently focuses on Python repositories, expanding the dataset to include other languages like Java, C++, or JavaScript would provide a more comprehensive evaluation of language models across different programming paradigms. To ensure fairness in evaluation, it is essential to carefully curate the dataset, balance the distribution of tasks, and consider factors like code complexity, issue types, and context length. Additionally, conducting thorough analyses of model performance across different subsets of the dataset can help identify and mitigate any biases or limitations present in the evaluation process.

Given the complexity of real-world software development, what other aspects of the software engineering process could be incorporated into future benchmarks to further challenge and advance language models

Incorporating additional aspects of the software engineering process into future benchmarks can further challenge and advance language models. One aspect to consider is software architecture design, where models are tasked with generating high-level architectural diagrams, design patterns, or system components based on given requirements. This would test the models' ability to understand and translate abstract concepts into concrete software designs. Another aspect could be software maintenance and refactoring tasks, where models are required to identify and refactor code smells, improve code readability, or optimize performance. This would assess the models' capability to enhance existing codebases and adhere to software engineering principles for maintainability and scalability. Furthermore, integrating tasks related to software deployment, continuous integration, and DevOps practices can test the models' understanding of the software development lifecycle and their ability to automate deployment processes, manage pipelines, and ensure software quality throughout the development cycle. By incorporating these additional aspects, future benchmarks can provide a more holistic evaluation of language models in real-world software engineering scenarios.