
Alevtina developed automated test suites for two core business features across RedRoverSchool’s JenkinsQA repositories. In JenkinsQA_Python_2025_spring, she implemented end-to-end Selenium tests in Python using Pytest, targeting shopping cart add and remove flows to improve coverage and accelerate CI feedback. Later, in JenkinsQA_Java_2025_fall, she enhanced the Booking Price Calculation test suite with Java, Selenium, and TestNG, expanding test coverage, refactoring test architecture, and standardizing resource cleanup for more reliable execution. Her work focused on maintainable, traceable automation that reduced manual regression effort and stabilized test feedback, demonstrating depth in both Python and Java-based test automation.

October 2025 monthly summary for RedRoverSchool/JenkinsQA_Java_2025_fall. Focused on strengthening the Booking Price Calculation test suite, stabilizing test execution, and improving resource lifecycle management. Delivered measurable improvements in test coverage and reliability, enabling faster feedback to development teams and reducing risk of booking price regressions in production.
October 2025 monthly summary for RedRoverSchool/JenkinsQA_Java_2025_fall. Focused on strengthening the Booking Price Calculation test suite, stabilizing test execution, and improving resource lifecycle management. Delivered measurable improvements in test coverage and reliability, enabling faster feedback to development teams and reducing risk of booking price regressions in production.
April 2025: Delivered automated end-to-end Selenium tests for the shopping cart in RedRoverSchool/JenkinsQA_Python_2025_spring, expanding test coverage, reliability, and CI feedback speed for cart-related changes. Work is encapsulated in a traceable commit set, enabling quicker validation of cart behaviors and reducing manual regression effort.
April 2025: Delivered automated end-to-end Selenium tests for the shopping cart in RedRoverSchool/JenkinsQA_Python_2025_spring, expanding test coverage, reliability, and CI feedback speed for cart-related changes. Work is encapsulated in a traceable commit set, enabling quicker validation of cart behaviors and reducing manual regression effort.
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