Abstract: A computer-implemented method for creation of a test plan for load testing a website includes receiving a set of input parameters, which includes a specified time period range. Beacon data of real user sessions on the website that fall within the specified time period range is retrieved from a data storage repository. A set of peak traffic time periods, which includes a peak day, a peak hour, a peak minute, and a peak second, is identified. User session statistics are calculated for the peak day, the user session statistics including median session duration, session length, and think times. A page group distribution table is generated that includes a list of all page groups and page group hit percentages, relative to all other page groups, for the real user sessions during the peak hour. A set of test scenario paths representative of the page group distribution table is then generated.

Abstract: A computer implemented method creating a benchmark for consumer satisfaction of website or web application performance includes aggregating real user measurement (RUM) data captured from devices of users during sessions on a website or web application, the RUM data including a landing page load time of each user and whether a bounce occurred. The probability that each tenant will bounce is then modeled. Next, an ordered discrete set of lethal dose (LD) values is calculated and the tenants are then filtered for erroneousness. An ordered set of mean LD values is calculated, and that set is reduced to exactly 10m ordered mean LD values. The 10m ordered mean LD values are mapped to a corresponding number of ordered CPI scores ranging from 0 to 100, wherein the mapping results in the benchmark.

Abstract: A computer implemented method creating a benchmark for consumer satisfaction of website or web application performance includes aggregating real user measurement (RUM) data captured from devices of users during sessions on a website or web application, the RUM data including a landing page load time of each user and whether a bounce occurred. The probability that each tenant will bounce is then modeled. Next, an ordered discrete set of lethal dose (LD) values is calculated and the tenants are then filtered for erroneousness. An ordered set of mean LD values is calculated, and that set is reduced to exactly 10m ordered mean LD values. The 10m ordered mean LD values are mapped to a corresponding number of ordered CPI scores ranging from 0 to 100, wherein the mapping results in the benchmark.

Abstract: A computer-implemented method creating a benchmark for consumer satisfaction of website or web application performance includes aggregating real user measurement (RUM) data captured from devices of users during sessions on a website or web application, the RUM data including a landing page load time of each user and whether a bounce occurred. The probability that each tenant will bounce is then modeled. Next, an ordered discrete set of lethal dose (LD) values is calculated and the tenants are then filtered for erroneousness. An ordered set of mean LD values is calculated, and that set is reduced to exactly 10m ordered mean LD values. The 10mn ordered mean LD values are mapped to a corresponding number of ordered CPI scores ranging from 0 to 100, wherein the mapping results in the benchmark.