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Selenium Automation Testing Framework
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Here are the key components
1. Continuous Integration (CI) Servers Utilize Jenkins, TeamCity, Bamboo, GitHub, Chef, and CruiseControl for building, running, and initiating tests, ensuring seamless integration and continuous testing.
2. Selenium Automation Framework
Config File Centralized configuration management.
Page Objects Simplify test scripts by modeling UI components.
Utility Libraries Reusable code libraries for common tasks.
Application Specific Libraries Custom libraries tailored to specific application needs.
3. Automated Test Suite Incorporate various scripts and test data, covering business functions, generic functions, and extended functions for comprehensive testing.
4. Input Test Data and Object Repository Manage test data and object repositories efficiently, ensuring accurate and reusable test artifacts.
5. Application Under Test (AUT) Execute tests across multiple browsers (Chrome, Firefox, IE, Opera) with parallel test execution capabilities.
6. Test Management Utilize tools like Jira, HP, QTest, QAComplete, TestLink, and Confluence to manage test suites, test casesuser stories, and test execution processes.
Data visualization is incredibly important in machine learning for a number of reasons: 1️⃣ Understanding Data: - Identifying patterns and trends: Visualizations like scatter plots, histograms, and heatmaps can reveal relationships, trends, and anomalies in your data that might be hidden in raw numbers. This helps you understand your data better and make informed decisions about feature engineering and model selection. - Detecting outliers: Outliers can significantly impact machine learning models. Visualizations can help you identify these outliers and decide how to handle them (e.g., removal, transformation). - Exploring data distributions: Understanding how your data is distributed is crucial for choosing appropriate models and preprocessing techniques. - Histograms and box plots can help you visualize data distributions and identify potential issues like skewness or imbalances. 2️⃣ Building Better Models: - Feature selection: By visualizing relationships between features and ...
Do you still write C# the way you did 7 years ago? Here's how you can easily get up to speed: C# 9 1. Records 2. Init-only setters 3. Top-level statements 4. Improved pattern matching 5. Target-type new C# 10 6. File scoped namespace 7. Global using directive 8. Constant interpolated strings C# 11 9. Raw literal strings 10. List patterns 11. File-scoped types 12. Required members C# 12 13. Primary constructors 14. Collection expressions 15. Inline collections with ranges and slices 16. Default values for lambda expressions C# 13 17. LINQ Index method 18. LINQ AggregateBy method 19. LINQ CountBy 20. Params collection 21. UUID version 7 22. Lock 23. Implicit index access In today's newsletter, you get those features explained in the "C# Upskill Checklist." For every feature you get: 1. Short explanation 2. Code example 3. Console output of the code snippet (where applicable)
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