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Description

When you engage with a web page, Screenster captures your interactions seamlessly. There’s no need for coding, referencing element IDs, or inserting checks and assertions. After recording your test, you can rerun it while allowing Screenster to enhance its performance. It automatically identifies parameters, and self-healing selectors are saved for every action taken. You can review and approve any alterations to the baseline, updating the default state of the user interface as needed. Additionally, you have the option to exclude specific UI elements from the comparison process. Forget about browser plugins, desktop installations, or complicated manuals; just enter a URL for a Screenster server and start your experience. Screenster is adept at detecting both visual and content modifications, utilizing advanced algorithms to compare test outcomes against the baseline and identify new, removed, or modified elements. Its intelligent selectors can effortlessly track moved or altered page components. Furthermore, Screenster intelligently adjusts timeouts and employs AI to determine the optimal moment to proceed to the next action. You can choose to execute your tests on our cloud platform or opt to download and install Screenster Server locally for on-premise use. Not only does Screenster simplify the testing process, but it also enhances efficiency and accuracy, making it an invaluable tool for developers and testers alike.

Description

Selector’s software-as-a-service leverages machine learning and natural language processing to deliver self-service analytics that facilitate immediate access to actionable insights, significantly decreasing mean time to resolution (MTTR) by as much as 90%. This innovative Selector Analytics platform harnesses artificial intelligence and machine learning to perform three critical functions, equipping network, cloud, and application operators with valuable insights. It gathers a wide array of data—including configurations, alerts, metrics, events, and logs—from diverse and disparate data sources. For instance, Selector Analytics can extract data from router logs, device performance metrics, or configurations of devices within the network. Upon gathering this information, the system normalizes, filters, clusters, and correlates the data using predefined workflows to generate actionable insights. Subsequently, Selector Analytics employs machine learning-driven data analytics to evaluate metrics and events, enabling automated detection of anomalies. In doing so, it ensures that operators can swiftly identify and address issues, enhancing overall operational efficiency. This comprehensive approach not only streamlines data processing but also empowers organizations to make informed decisions based on real-time analytics.