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Determine Navigation Paths for Key Scenarios
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Human beings are unpredictable, and Web sites commonly offer redundant functionality. Even with a relatively small number of users, it is almost certain that real users will not only use every path you think they will to complete a task, but they also will inevitably invent some that you had not planned. Each path a user takes to complete an activity will place a different load on the system. That difference may be trivial, or it may be enormous there is no way to be certain until you test it. There are many methods to determine navigation paths, including: Identifying the user paths within your Web application that are expected to have significant performance impact and that accomplish one or more of the identified key scenarios. Reading design and/or usage manuals. Trying to accomplish the activities yourself. Observing others trying to accomplish the activity without instruction (other than would be given to a new user prior to his or her first use of the system).
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Analyzing empirical data from Web server logs captured during pre-production releases and usage studies.
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Determine Individual User Data and Variances
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During the early stages of development and testing, user data and variances are most often estimated based on expected usage and observation of users working with similar applications. These estimates are generally enhanced or revised when empirical data from Web server logs becomes available. Some of the more useful metrics that can be read or interpreted from Web server logs include: Page views per period. A page view is a page request that includes all dependent file requests (.jpg files, CSS files, etc). Page views can be tracked over hourly, daily, or weekly time periods to account for cyclical patterns or bursts of peak user activity on the Web site. User sessions per period. A user session is the sequence of related requests originating from a user visit to the Web site, as explained previously. As with page views, user sessions can span hourly, daily, and weekly time periods. Session duration. This metric represents the amount of time a user session lasts, measured from the first page request until the last page request is completed, and including the time the user pauses for when navigating from page to page. Page request distribution. This metric represents the distribution, in percentages, of page hits according to functional types (Home, login, Pay, etc.). The distribution percentages will establish a weighting ratio of page hits based on the actual user utilization of the Web site. Interaction speed. Also known as user think time, page view time, and user delay, this metric represents the time users take to transition between pages when navigating the Web site, constituting the think time behavior. It is important to remember that every user will interact with the Web site at a different rate. User abandonment. This metric represents the length of time that users will wait for a page to load before growing dissatisfied, exiting the site, and thus abandoning their user session. Abandoned sessions are quite normal on the Internet and consequently will have an impact on the load test results.
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Determine the Relative Distribution of Scenarios
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Having determined which scenarios to simulate and what the steps and associated data are for those scenarios, and having consolidated those scenarios into one or more workload models, you now need to determine how often users perform each activity represented in the model relative to the other activities needed to complete the workload model. Sometimes one workload distribution is not enough. Research and experience have shown that user activities often vary greatly over time. To ensure test validity, you must validate that activities are evaluated according to time of day, day of week, day of month, and time of year. The most common methods for determining the relative distribution of activities include: Extract the actual usage, load values, common and uncommon usage scenarios (user paths), user delay time between clicks or pages, and input data variance (to name a few) directly from log files. Interview the individuals responsible for selling/marketing new features to find out what features/functions are expected and therefore most likely to be used. By interviewing existing
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users, you may also determine which of the new features/functions they believe they are most likely to use. Deploy a beta release to a group of representative users roughly 10-20 percent the size of the expected user base and analyze the log files from their usage of the site. Run simple in-house experiments using employees, customers, clients, friends, or family members to determine, for example, natural user paths and the page-viewing time differences between new and returning users. As a last resort, you can use your intuition, or best guess, to make estimations based on your own familiarity with the site.
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Once you are confident that the model is good enough for performance testing, supplement the model with the individual usage data you collected previously in such a way that the model contains all the data you need to create the actual test.
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