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Articles on Trending Technologies
Technical articles with clear explanations and examples
What is Continuous Testing in DevOps(Definition, Benefits, Tools)?
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 MoreHow to set different opacity of edgecolor and facecolor of a patch in Matplotlib?
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 # 0
Read MoreDraw a parametrized curve using pyplot.plot() in Matplotlib
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 MoreWhat is Software Testing Metrics with Types & Example?
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 MoreHow do I plot a spectrogram the same way that pylab's specgram() does? (Matplotlib)
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 MoreWhat is Agile Testing? (Process, Strategy, Test Plan, Life Cycle Example)
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 MoreHow to save an array as a grayscale image with Matplotlib/Numpy?
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
Read MoreHow to plot categorical variables in Matplotlib?
To plot categorical variables in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a dictionary with some details.Extract the keys and values from the dictionary (Step 2).Create a figure and a set of subplots.Plot bar, scatter and plot with names and values data.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = {'apple': 10, 'orange': 15, 'lemon': 5} names = list(data.keys()) values = list(data.values()) fig, axs = plt.subplots(1, 3) axs[0].bar(names, values) axs[1].scatter(names, values) axs[2].plot(names, values) ...
Read MoreDesign Verification & Validation Process in Software testing
You will learn the following in this article −What is Design Verification?Difference between Design Verification and ValidationDesign Verification ProcessDesign Validation ProcessAdvantages of Design Validation and VerificationDesign ValidationDesign Validation is the process of testing a software product to ensure that it meets the specific needs of the customer or partners. The goal of design validation is to check the software product after it has been developed to confirm that it fits the criteria for implementations in the user's environment.Validation is focused on establishing the design's accuracy and reliability in relation to the user's demands. This is the step in which you ...
Read MorePlot curves in fivethirtyeight stylesheet in Matplotlib
To use fivethirtyeight stylesheet, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.To use fivethirtyeight, we can use plt.style.use() method.Create x data points using numpy.Create a figure and a set of subplots using subplots() method.Plot three curves using plot() method.Set the title of the plot.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.style.use('fivethirtyeight') x = np.linspace(0, 10) fig, ax = plt.subplots() ax.plot(x, np.sin(x) + x + np.random.randn(50)) ax.plot(x, np.sin(x) + 0.5 * x + ...
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