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Showing posts with the label Defects in Software development

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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

2 Root Causes for Defects in Software Development

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Miscommunication Miscommunication is a common factor, which can be defined as inaccurate statements or information missing that is required for the action to be done successfully. This miscommunication ends up in the documentation or verbal communication that occurs. Instead of spending time to make sure everything is accurate, statements are made that are untrue or unclear. When this occurs at the beginning of the change process the bad information continues down through the process. Decisions and design are made based on it.  At some point it gets realized that the information is bad and a defect is created. In the common project process that could be classified as linear, most defects are not found until in the later phase of development and unit testing has started. Process Defects This would be similar to a defect a machine makes in manufacturing. Even though the input is accurate, the process itself causes a defect to occur.  The original process was prone to defects no matt