The Web Pages section contains detailed statistics for each web page, including how the minimum, average, and maximum load times changed at each point in the test, the bytes transferred, and bytes per second.

The errors section shows how the errors changed throughout the test, giving detailed information about each error, including when it happened and a description of the problem. To avoid the very common situation where the same error is repeated over and over again, errors are summarized.

Open Errors Report. More than one test case can be run simultaneously, so this report helps find out which of the master are having performance problems. Open Master Report. A Baseline Report is very important because it tells what the initial performance of the system is when not under load.

The web server will never be any faster than this! A baseline report starts from a particular load configuration, and then calculates the beginning performance, and the requirements for the test. The bandwidth section of the report calculates the bandwidth requirements to run the specified test cases at the described load.

This is great for bandwidth capacity planning before starting a large load test. Open Bandwidth Report. The goals section measures whether the configured test can run as described and possibly meet the performance goals.

For example, typically test cases are recorded over a local LAN with much more bandwidth available than the users will see from home or a remote office. By specifying the user's bandwidth, this bandwidth can calculate whether the performance goals can be met over that connection before a test is even run, saving the trouble of actually running the test.

Open Goals Report. The master section of the report calculates the master requirements to run the specified test cases at the described load. This is great for master capacity planning before starting a large load test.

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About You. Concurrent Users 0 - - 1, 1, - 5, 5, - 10, 10, - 1,, Unknown. Detailed list of pages which aren't meeting performance goals Bandwidth bottleneck detection Detailed page load time analysis pinpointing the cause of a slow page Live screen captures of all page errors.

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View your test execution in real-time to see a graphical representation of the execution plan, average response times, and errors that occur. When a test has finished, the results can be accessed through the Stress Test Report, which contains the following sections:.

Includes summary of the execution plan, including maximum, actual, and expected number of virtual users, along with average response time of transactions, number of sessions, session errors, load injector CPU usage, and much more. Drill-down even further into element-level details through waterfall charts.

Analyze start time, duration, and execution status for all user sessions or zones, as well as any client-side errors and failed sessions. The Session Report also includes the Reference Server recording.

The Reference Server runs a single virtual user that represents an outside visitor, examining device availability during the test.

This allows you to compare load injector sessions and the reference user during a stress test. For web applications and web page load tests, the reference server records video in the way it appears to your visitor, allowing you to review actual behavior of your applications and sites under load.

Overview of the script that was executed, the dynamic variables if applicable , and any profile details, such as user behavior and delays. The line chart shows the change in the actual number of virtual users over the testing period vs.

the expected number of virtual users based on the test scenario. If the Actual Number of Users line reaches the Max. Number of Users line, all virtual users allocated for the test were used and the site was tested under the planned maximum load. Transactions per minute Goal-Based Load Test only The chart reflects goal vs.

actual number of transactions per minute at each test iteration. On the chart you can find the following lines:.

The Average Response Time chart shows the change in the actual duration of transactions. The Y-axis represents the time in seconds. If there are no significant line fluctuations on the chart, your web site handled the test load successfully.

The Cumulative Sessions Count graph shows the total number of sessions started over the test. The chart allows assessment of the total number of virtual users simulated on the target resource over the test period. The Y-axis shows the number of sessions. Each node represents the total count, calculated as a sum of sessions started by the time of calculation.

The Number of Errors by Errors Type dot chart illustrates the number of error sessions by error type. The number is specified on the Y-axis. Use the chart to determine which error types predominated during a specific moment.

See the Session Report to review failures. For error description, check Error Codes. Load Injector Load The chart shows CPU load metrics received from Dotcom-Monitor LIs.

Use it to assess how your test affects the performance of LIs running the test from different geographical zones. This load level excludes the server hardware delays that influence the server performance and slow down data processing. Infotip Nodes on the charts are active, so clicking a node will open its infotip.

To see detailed information for the node-related sessions, click Show Details at the bottom of the infotip.

The report for sessions running at the selected period of time will be displayed on the Log page. What are you looking to accomplish? The choice is yours! The performance tests you run will help ensure your software meets the expected levels of service and provide a positive user experience.

They will highlight improvements you should make to your applications relative to speed, stability, and scalability before they go into production. Applications released to the public in absence of testing might suffer from different types of problems that lead to a damaged brand reputation, in some cases, irrevocably.

The adoption, success, and productivity of applications depends directly on the proper implementation of performance testing.

While resolving production performance problems can be extremely expensive, the use of a continuous optimization performance testing strategy is key to the success of an effective overarching digital strategy. In each case, operational teams expose the application to end users of the product architecture during testing.

Development performance tests focus on components web services, microservices, APIs. The earlier the components of an application are tested, the sooner an anomaly can be detected and, usually, the lower the cost of rectification. As the application starts to take shape, performance tests should become more and more extensive.

There are many different types of performance tests. The most important ones include load, unit, stress, soak and spike tests. Load testing simulates the number of virtual users that might use an application. In reproducing realistic usage and load conditions, based on response times, this test can help identify potential bottlenecks.

Unit testing simulates the transactional activity of a functional test campaign; the goal is to isolate transactions that could disrupt the system.

Stress testing evaluates the behavior of systems facing peak activity. These tests significantly and continuously increase the number of users during the testing period.

Soak testing increases the number of concurrent users and monitors the behavior of the system over a more extended period. The objective is to observe if intense and sustained activity over time shows a potential drop in performance levels, making excessive demands on the resources of the system.

Spike testing seeks to understand implications to the operation of systems when activity levels are above average. Unlike stress testing, spike testing takes into account the number of users and the complexity of actions performed hence the increase in several business processes generated.

Performance testing can be used to analyze various success factors such as response times and potential errors. With these performance results in hand, you can confidently identify bottlenecks, bugs, and mistakes — and decide how to optimize your application to eliminate the problem s.

The most common issues highlighted by performance tests are related to speed, response times, load times and scalability. Excessive load time is the allotment required to start an application. Any delay should be as short as possible — a few seconds, at most, to offer the best possible user experience.

Poor response time is what elapses between a user entering information into an application and the response to that action. Long response times significantly reduce the interest of users in the application. Limited scalability represents a problem with the adaptability of an application to accommodate different numbers of users.

For instance, the application performs well with just a few concurrent users but deteriorates as user numbers increases. Bottlenecks are obstructions in the system that decrease the overall performance of an application. They are usually caused by hardware problems or lousy code.

While testing methodology can vary, there is still a generic framework you can use to address the specific purpose of your performance tests — which is ensuring that everything will work properly in a variety of circumstances as well as identifying weaknesses.

Comprehensive knowledge of this environment makes it easier to identify problems that testers may encounter. Before carrying out the tests, you must clearly define the success criteria for the application — as it will not always be the same for each project.

Identifying key scenarios and data points is essential for conducting tests as close to real conditions as possible:. After running your tests, you must analyze and consolidate the results. Once the necessary changes are done to resolve the issues, tests should be repeated to ensure the elimination of any others.

Performance tests generate vast amounts of data. The best performance tests are those that allow for quick and accurate analysis to identify all performance problems, their causes.

With the emergence of Agile development methodologies and DevOps process practices, performance tests must remain reliable while respecting the accelerated pace of these cycles: development, testing, and production.

To keep pace, companies are looking to automation , with many choosing NeoLoad — the fastest and most highly automated performance testing tool for the design, filtering, and analysis of testing data. Agile development methodologies can provide a solution. Despite the adoption of Continuous Integration by Agile and DevOps environments, performance testing is typically a manual process.

The goal of each performance tester is to prevent bottlenecks from forming in the Agile development process. To avoid this, incorporating as much automation into the performance testing process where possible can help. The complete automation of performance testing is possible during component testing.

However, human intervention of performance engineers is still required to perform sophisticated tests on assembled applications. The future of performance testing lies in automating testing at all stages of the application lifecycle.

NeoLoad is the performance testing platform developed by Neotys to automate the execution, design, update, and analysis of test tasks. When designing performance tests, NeoLoad automates correlation and randomization tasks, enabling you to create tests ten times faster than other tools.

It also allows you to import existing functional Selenium test scripts for use in performance testing. For Continuous Integration, NeoLoad integrates with all leading CI servers, such as Jenkins, Bamboo, and TeamCity, via an API tool.

At this point, custom integrations with various tools in the continuous deployment chain are possible. One of the main challenges for performance engineers is updating test cases when the application changes.