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Computer Science > Machine Learning

arXiv:2502.12115 (cs)
[Submitted on 17 Feb 2025]

Title:SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World
Freelance Software Engineering?

Authors:Samuel Miserendino, Michele Wang, Tejal Patwardhan, Johannes
Heidecke
View a PDF of the paper titled SWE-Lancer: Can Frontier LLMs Earn $1
Million from Real-World Freelance Software Engineering?, by Samuel
Miserendino and 3 other authors
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    Abstract:We introduce SWE-Lancer, a benchmark of over 1,400
    freelance software engineering tasks from Upwork, valued at \$1
    million USD total in real-world payouts. SWE-Lancer encompasses
    both independent engineering tasks--ranging from \$50 bug fixes
    to \$32,000 feature implementations--and managerial tasks, where
    models choose between technical implementation proposals.
    Independent tasks are graded with end-to-end tests
    triple-verified by experienced software engineers, while
    managerial decisions are assessed against the choices of the
    original hired engineering managers. We evaluate model
    performance and find that frontier models are still unable to
    solve the majority of tasks. To facilitate future research, we
    open-source a unified Docker image and a public evaluation split,
    SWE-Lancer Diamond (this https URL). By mapping model performance
    to monetary value, we hope SWE-Lancer enables greater research
    into the economic impact of AI model development.

Comments: 9 pages, 24 pages appendix
Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2502.12115 [cs.LG]
          (or arXiv:2502.12115v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2502.12115
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Michele Wang [view email]
[v1] Mon, 17 Feb 2025 18:41:16 UTC (3,335 KB)
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    View a PDF of the paper titled SWE-Lancer: Can Frontier LLMs Earn
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    Samuel Miserendino and 3 other authors
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