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The term 'Harness' in general refers to a 'Tool' for controlling something. The same rule goes for software testing as well. In Software Testing, Test Harness is a collection of software, test data, test drivers, and tools specially developed to test an application under various environments. Developers then analyze the results of the tests to ensure a satisfactory outcome.How is Test Harness done in software testing?Test Harness can be called a process that does all the testing works, such as executing tests via test libraries and generating reports. For that, developers and testers have to develop specific test scripts to ... Read More
Continuous TestingContinuous testing in DevOps is a kind of software testing that entails testing the program at each phase of the software development cycle. The purpose of continuous testing is to evaluate the software quality at each stage of the Continuous Delivery Process by checking promptly and frequently.In DevOps, the Continuous Testing phase encompasses participants such as Developers, DevOps, QA, and the operational system.This article will teach you −What is Continuous Testing?How is Continuous Testing different?How Is Continuous Testing Different from Test Automation?How to do Continuous TestingContinuous testing toolsBenefits of Continuous testingChallenges of continuous testingHow is Continuous Testing different?The traditional ... Read More
To set different opacity of edge and face color, we can use a color tuple and the 4th index of the tuple could set the opacity value of the colors.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots using subplots() method.Set different values for edge and face color opacity.Add a rectangel patch using add_patch() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, patches plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True figure, ax = plt.subplots() edge_color_opacity = 1 # 0Read More
To draw a parametrized curve using pyplot.plot(), we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for number of samples.Create t, r, x and y data points using numpy.Create a figure and a set of subplots.Use plot() method to plot x and y data points.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 400 t = np.linspace(0, 2 * np.pi, N) r = 0.5 + np.cos(t) x, y = r * ... Read More
Software Testing metrics are quantitative steps taken to evaluate the software testing process's quality, performance, and progress. This helps us to accumulate reliable data about the software testing process and enhance its efficiency. This will allow developers to make proactive and precise decisions for upcoming testing procedures.What is a metric in software testing metrics?A Metric is a degree to which a system or its components retains a given attribute. Testers don't define a metric just for the sake of documentation. It serves greater purposes in software testing. For example, developers can apply a metric to assume the time it takes ... Read More
To plot a spectrogram the same way that pylab's specgram() does, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create t, s1, s2, nse, x, NEFT and Fs data points using numpy.Create a new figure or activate an existing figure using subplots() method with nrows=2.Plot t and x data points using plot() method.Lay out a grid in current line style.Set the X-axis margins.Plot a spectrogram using specgram() method.Lay out a grid in current line style with dotted linestyle and some other properties.To display the figure, use show() method.Exampleimport matplotlib.pyplot as ... Read More
What is Agile Testing?Agile Testing is a method of testing that adheres to the rules and concepts of agile software development. Unlike the Waterfall technique, Agile Testing may commence at the beginning of a project with continuous integration of testing and development. The agile testing approach is not chronological (in the sense that it is only conducted after the coding process), but rather consistent.In this article, we will talk about −Agile Test PlanAgile Testing StrategiesThe Agile Testing QuadrantQA challenges with agile software developmentRisk of Automation in Agile ProcessAgile Test PlanThe kind of test performed in that iteration are included in ... Read More
Software testing is the process of evaluating and verifying a software program to ensure that it functions properly. The goal is to identify flaws and enhance product quality. There are two methods for testing software: Positive Testing and Negative Testing.Positive TestingPositive testing is a sort of testing that is conducted on a software program using legitimate data sets as input. It determines whether or whether the software program acts as predicted when given favorable input. Positive testing is done to ensure that the software program accomplishes precisely what it is supposed to accomplish.Example − In an app, there is a text ... Read More
To save an array as a grayscale image with Matplotlib/numpy, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data with 5☓5 dimension.Set the colormap to "gray".Plot the data using imshow() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True arr = np.random.rand(5, 5) plt.gray() plt.imshow(arr) plt.show()Output
To change the font size of the scale in Matplotlib, we can use labelsize in the tick_params() method.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Plot x data points using plot() method.To change the font size of the scale in matplotlib, we can use labelsize in the ticks_params()method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() x = np.random.rand(10) ax.plot(x) ax.tick_params(axis='x', labelsize=20) plt.show()OutputRead More