{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"bigcodebench","formal_name":"BigCodeBench","introduction":"BigCodeBench poses 1,140 function-level tasks drawn across 139 libraries. It tests whether a model can compose several real APIs correctly rather than write one self-contained function.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://bigcode-bench.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"6f90de17-3879-567f-bd75-104991833985","task_key":"default--v0~2e1~2e0~5fhf--6f90de17-3879-567f-bd75-104991833985","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 6f90de17-3879-567f-bd75-104991833985","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy import stats\\ndef task_func(mean=123456.908, std_dev=1.2, save_plots=False):\\n\",\"complete_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy import stats\\n\\n\\ndef task_func(mean=123456.908, std_dev=1.2, save_plots=False):\\n    \\\"\\\"\\\"\\n    Generate a random sample from a normal distribution, analyze its skewness and kurtosis,\\n    and create a histogram and a QQ plot to visualize the distribution.\\n\\n    Parameters:\\n    - mean (float, optional): Mean of the normal distribution. Defaults to 123456.908.\\n    - std_dev (float, optional): Standard deviation of the normal distribution. Defaults to 1.2.\\n    - save_plots (bool, optional): If True, saves the plots to files. Defaults to False.\\n\\n    Returns:\\n    - float: Skewness of the sample.\\n    - float: Kurtosis of the sample.\\n    - list: Paths to the saved plot files, empty if save_plots is False.\\n\\n    Requirements:\\n    - numpy\\n    - matplotlib.pyplot\\n    - scipy.stats\\n\\n    Example:\\n    >>> np.random.seed(0)\\n    >>> skewness, kurtosis, plot_paths = task_func(123456.908, 1.2, True)\\n    >>> print(f'Skewness: {skewness}, Kurtosis: {kurtosis}, Plots: {plot_paths}')\\n    Skewness: 0.03385895323538189, Kurtosis: -0.04676632447765128, Plots: ['histogram_plot.png', 'qq_plot.png']\\n\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate a random sample from a normal distribution, analyze its skewness and kurtosis, and create a histogram and a QQ plot to visualize the distribution.\\nThe function should output with:\\n    float: Skewness of the sample.\\n    float: Kurtosis of the sample.\\n    list: Paths to the saved plot files, empty if save_plots is False.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy import stats\\ndef task_func(mean=123456.908, std_dev=1.2, save_plots=False):\\n```\",\"libs\":\"['numpy', 'matplotlib', 'scipy']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}