3260 papers • 126 benchmarks • 313 datasets
a task to generate the class-level code based on input description
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This work makes the first attempt to evaluate LLMs in a more challenging code generation scenario, i.e. class-level code generation, and finds the limited model ability of generating method-dependent code and discusses the frequent error types in generated classes.
It is shown that deep learning methods can be leveraged to train a model end-to-end to automatically reverse engineer user interfaces and generate code from a single input image with over 77% of accuracy for three different platforms.
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