SWE-Lancer
S
SWE Lancer
Overview :
SWE-Lancer, launched by OpenAI, is a benchmark designed to assess the performance of cutting-edge language models in real-world freelance software engineering tasks. This benchmark encompasses a range of independent engineering tasks, from $50 bug fixes to $32,000 feature implementations, as well as managerial tasks such as selecting between technical implementation options. By mapping model performance to monetary value, SWE-Lancer offers a new perspective on researching the economic impact of AI model development and promoting the advancement of related research.
Target Users :
SWE-Lancer is designed for researchers, developers, and enterprises to evaluate and study the practical application capabilities and economic value of AI models in software engineering. It provides insights into model performance in solving real-world software engineering tasks, driving technological advancements and innovation. Furthermore, SWE-Lancer serves as a powerful tool for exploring the economic impact of AI within the software development industry.
Total Visits: 505.0M
Top Region: US(17.26%)
Website Views : 49.7K
Use Cases
Researchers can use SWE-Lancer to evaluate the performance differences of various AI models in solving software engineering tasks, providing a basis for model optimization and improvement.
Developers can use this benchmark to understand the performance of AI models in actual software development tasks, exploring how to better integrate AI technology into the development process.
Enterprises can use SWE-Lancer to evaluate the economic value of AI models in software engineering tasks, determining whether it is suitable to introduce AI technology to improve development efficiency and reduce costs.
Features
Offers over 1400 real-world freelance software engineering tasks, covering a wide range of difficulty and value.
Includes independent engineering tasks and management decision tasks for comprehensive model capability assessment.
Independent tasks are scored through end-to-end testing triple-verified by experienced software engineers.
Management decision tasks are evaluated by comparing against the choices of original hired engineering managers.
Provides open-source unified Docker images and public evaluation splits for future research.
Presents the economic potential of AI models intuitively by mapping model performance to task value.
Supports quantitative analysis of the performance of cutting-edge models in actual software engineering tasks.
Offers researchers a standardized testing environment and dataset, promoting technological development.
How to Use
Access the SWE-Lancer open-source repository to obtain the relevant Docker images and test datasets.
Set up your local development environment, ensuring the Docker environment is functioning correctly.
Integrate the AI model to be evaluated into the SWE-Lancer testing framework.
Run the test tasks; the model will sequentially process various software engineering tasks.
Review the test results, including task completion status, scores, and mapping to real-world value.
Analyze the model's strengths and weaknesses based on the test results, providing a reference for further research and development.
AIbase
Empowering the Future, Your AI Solution Knowledge Base
© 2025AIbase