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Description

Quash is an innovative quality assurance platform powered by AI, specifically crafted to enhance mobile testing by automatically creating and running functional and visual tests without the need for conventional scripting methods. It systematically maps each screen within your mobile application, converts product requirement documents into actionable test cases, and executes them through an AI engine that comprehends app navigation and user intent, effectively eliminating unreliable selectors and delicate test scripts. The platform intelligently learns the context of your application by analyzing app data, allowing it to adapt in real-time, which leads to considerably improved coverage and more seamless release processes by identifying edge cases at an earlier stage. Following test executions, Quash offers comprehensive bug reports featuring screenshots, session logs, insights into root causes, and recommended solutions, all accessible through a centralized dashboard that integrates test generation, execution, bug tracking, and analytics. Additionally, it is compatible with existing CI/CD pipelines, test management workflows, and bug-reporting tools, ensuring a smooth integration into current development processes while enhancing overall efficiency. Ultimately, Quash aims to transform the mobile testing landscape by providing developers with the tools they need to deliver high-quality applications faster and more reliably.

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.