Class-level Code Generation
3 papers with code • 1 benchmarks • 0 datasets
a task to generate the class-level code based on input description
Benchmarks
These leaderboards are used to track progress in Class-level Code Generation
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Most implemented papers
pix2code: Generating Code from a Graphical User Interface Screenshot
Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications.
ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation
Third, we find that generating the entire class all at once (i. e. holistic generation strategy) is the best generation strategy only for GPT-4 and GPT-3. 5, while method-by-method generation (i. e. incremental and compositional) is better strategies for the other models with limited ability of understanding long instructions and utilizing the middle information.
AutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}
We introduce AutoCoder, the first Large Language Model to surpass GPT-4 Turbo (April 2024) and GPT-4o in pass@1 on the Human Eval benchmark test ($\mathbf{90. 9\%}$ vs. $\mathbf{90. 2\%}$).
