Can language models replace programmers? Researchers from Princeton and the University of Chicago present SWE-bench: an evaluation framework that tests machine learning models to solve real GitHub problems

Assessing the competency of language models in addressing real-world software engineering challenges is essential to their progress. Enter SWE-bench, an innovative evaluation framework that employs GitHub issues from Python repositories and pull requests to measure the ability of these models to address coding and problem-solving tasks. Surprisingly, the findings reveal that even the most advanced models can only address simple questions. This highlights the pressing need to continue advancing language models to enable practical and intelligent software engineering solutions.

While previous research has introduced evaluation frameworks for language models, they often need more versatility and address the complexity of real-world software engineering tasks. In particular, existing benchmarks for code generation must capture the depth of these challenges. The SWE-bench framework from researchers at Princeton University and the University of Chicago stands out by focusing on real-world software engineering issues, such as patch generation and complex contextual reasoning, and offers a more realistic and comprehensive evaluation to enhance language models with software engineering capabilities. . This is particularly relevant in the field of Machine Learning for Software Engineering.

As language models (LMs) become widely used in commercial applications, the need for robust benchmarks to evaluate their capabilities becomes evident. Existing benchmarks need to be reviewed to challenge LMs with real-world tasks. Software engineering tasks offer a compelling challenge because of their complexity and verifiability through unit testing. SWE-bench leverages GitHub issues and solutions to create a practical benchmark for evaluating LM in a software engineering context, promoting real-world applicability and continuous updates.

Their research includes 2,294 real-world software engineering problems from GitHub. LMs edit code bases to resolve issues between functions, classes, and files. Model inputs include task instructions, problem text, retrieved files, example patch, and a message. The performance of the model is evaluated in two context settings: sparse recovery and oracle recovery.

Evaluation results indicate that even state-of-the-art models such as Claude 2 and GPT-4 struggle to solve real-world software engineering problems, achieving pass rates as low as 4.8% and 1.7%, even with the best context recovery methods. Their models perform worse when addressing questions from longer contexts and showing sensitivity to context variations. Their models tend to generate shorter and more poorly formatted patch files, highlighting the challenges in handling complex code-related tasks.

As LMs advance, the paper highlights the critical need for their comprehensive evaluation in practical real-world scenarios. The evaluation framework, SWE-bench, serves as a challenging and realistic testbed to evaluate the capabilities of next-generation LMs within the context of software engineering. The evaluation results reveal the current limitations of even state-of-the-art LMs in handling complex software engineering challenges. Their contributions emphasize the need to develop more practical, intelligent and autonomous LMs.

The researchers propose several avenues to advance the SWE bank evaluation framework. Their research suggests broadening the benchmark with a broader range of software engineering problems. Exploring advanced retrieval techniques and multimodal learning approaches can improve the performance of language models. Addressing limitations in understanding complex code changes and improving the generation of well-formatted patch files are highlighted as important areas for future exploration. These steps aim to create a more comprehensive and effective evaluation framework for language models in real-world software engineering scenarios.

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