Abstract: Described herein are systems and methods of identifying and classifying performance bottlenecks for web applications. Such systems and methods use classification and analysis of performance testing data and data instrumentation via arithmetic and/or machine learning. Data is integrated from different sources including system data, historical and real time sources. Performance variations are analyzed as load changes and the impact of these variations on different sectors of the Application stack are analyzed. Bottlenecks are identified and classified based on the sector in the software stack and recommendations for optimization of an Application under Test are presented to address the bottlenecks are presented.

Abstract: Described herein are systems and methods of identifying and classifying performance bottlenecks for web applications. Such systems and methods use classification and analysis of performance testing data and data instrumentation via arithmetic and/or machine learning. Data is integrated from different sources including system data, historical and real time sources. Performance variations are analyzed as load changes and the impact of these variations on different sectors of the Application stack are analyzed. Bottlenecks are identified and classified based on the sector in the software stack and recommendations for optimization of an Application under Test are presented to address the bottlenecks are presented.

Abstract: A method and system for generating performance tests for cloud based applications using data from real traffic. HTTP and API call transactions are recorded and converted to performance tests that can be used as is or manipulated for increased variability allowing for the creation of realistic performance tests for web based applications, allowing for the measurement and analysis of user performance metrics under real conditions.