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Microsoft, Penn U & UC San Diego’s TiCoder Framework Generates Code With 90.4% Consistency to User Intent
Aug. 18, 2022, 8:31 p.m. | Synced
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In the new paper Interactive Code Generation via Test-Driven User-Intent Formalization, a team from Microsoft Research, the University of Pennsylvania, and the University of California, San Diego proposes a workflow for test-driven user-intent formalization that leverages user feedback to generate code that is 90.40 percent consistent with user intent.
The post Microsoft, Penn U & UC San Diego’s TiCoder Framework Generates Code With 90.4% Consistency to User Intent first appeared on Synced.
ai artificial intelligence code code generation deep-neural-networks framework machine learning machine learning & data science microsoft ml pretrained language model research technology uc san diego
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