This is a Plain English Papers summary of a research paper called SWE-bench: Can Language Models Resolve Real-World GitHub Issues?. If you like these kinds of analysis, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter.
Overview
Researchers find real-world software engineering tasks to be a useful testbed for evaluating the capabilities of large language models (LLMs)
They introduce SWE-bench, an evaluation framework with 2,294 software engineering problems from GitHub issues and pull requests across 12 Python repositories
The goal is for LLMs to edit code to resolve the issues, which requires understanding and coordinating changes across multiple functions, classes, and files
Evaluations show that even state-of-the-art models can only resolve the simplest issues, with the best-performing model solving just 1.96% of the problems
Plain English Explanation
As language models like GPT-3 have become increasingly capable, it’s become harder to effectively evaluate their full range of abilities. Researchers believe that real-world software engineering tasks could provide a rich, challenging testbed for assessing the next generation of these models.
To explore this, the researchers created SWE-bench, a collection of over 2,000 software engineering problems drawn from actual GitHub issues and pull requests. These issues often require coordinating changes across multiple parts of the codebase, understanding the execution environment, and performing complex reasoning – going well beyond typical code generation.
The researchers then tested both state-of-the-art commercial models and a fine-tuned model called SWE-Llama on these software engineering challenges. The results were sobering – even the best-performing model could only solve about 2% of the problems. This shows that current language models still have significant limitations when it comes to practical, real-world tasks that involve in-depth reasoning and interaction with complex software systems.
Improving performance on benchmarks like SWE-bench would be an important step towards developing language models that are more practical, intelligent, and autonomous. The insights from this research could help guide the development of the next generation of AI that can truly assist with software engineering and other demanding, real-world applications.
Technical Explanation
The researchers introduce SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and pull requests across 12 popular Python repositories. Given a codebase and a description of an issue to be resolved, language models are tasked with editing the codebase to address the problem.
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.
The researchers evaluated both state-of-the-art proprietary models and their own fine-tuned model, SWE-Llama, on the SWE-bench challenges. Their results showed that even the best-performing model, Claude 2, could only solve a mere 1.96% of the issues.
These findings highlight the significant limitations of current language models when it comes to practical, real-world tasks that involve in-depth reasoning and interaction with complex software systems. Improving performance on benchmarks like SWE-bench and S3-Eval would be an important step towards developing more practical, intelligent, and autonomous language models that can assist with software engineering and other demanding, real-world applications.
Critical Analysis
The researchers acknowledge several limitations of their study. First, the SWE-bench dataset may not fully capture the breadth of software engineering challenges that language models would face in the real world. The problems were drawn from a limited set of 12 Python repositories, and there may be important types of issues or codebases that are not represented.
Additionally, the researchers only evaluated models on their ability to edit code, rather than other important software engineering skills like understanding requirements, designing architecture, or debugging complex systems. A more comprehensive evaluation framework would be needed to fully assess the capabilities of language models in this domain.
It’s also worth noting that the state-of-the-art models tested, while impressive in many ways, are not necessarily representative of the full potential of large language models. As the field continues to advance, more capable and specialized models may emerge that are better suited for software engineering tasks.
Nevertheless, the findings of this study are a sobering reminder that current language models still have significant limitations when it comes to practical, real-world applications. Continued research and benchmarking in this area will be crucial for driving progress towards more intelligent and autonomous AI systems that can truly assist with software development and other complex, knowledge-intensive domains.
Conclusion
This research introduces SWE-bench, a new evaluation framework for assessing the capabilities of large language models on real-world software engineering tasks. The results show that even state-of-the-art models struggle to resolve the majority of the issues, highlighting the significant limitations of current language technology when it comes to practical, intelligent interaction with complex software systems.
While more work is needed to fully capture the breadth of software engineering challenges, the insights from this study represent an important step towards developing the next generation of language models that are more practical, autonomous, and capable of assisting with demanding, real-world applications. Continued research and benchmarking in this area will be crucial for driving progress in this direction.
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