- Verizon in home agent optimize system speed how to#
- Verizon in home agent optimize system speed software#
As the test agents run, system resources (CPU-time, disk space, memory) are used and the overall performance of the Web-enabled application slows. Each test period increases the number of concurrently running agents. For example, how we would know when the system is not able to handle 50 concurrent users? Imagine running multiple copies of an intelligent test agent concurrently for multiple periods of time. One technique to translate the goal into an actionable result is to look at the goal in reverse. For the moment concurrency means the state where two or more people request a function at the same time. The definition for concurrency is covered later in this chapter. The goal identifies the forecasted total number of concurrent users. Each function is driven by a Java servlet. Imagine a company Web site redesign that added several custom functions. Goal: Our new Web site needs to handle peak loads of 50 concurrent users. Therefore, a starting point in analyzing results is to understand the goals of the test and see how the goals can be translated to results.įollowing are a few test goals and how the goals may be translated to actionable re. The developer looking at the same results log would be satisfied that the module under test functioned. The log file on a server undergoing a scalability and concurrency test the analyst will be looking for log entries that indicate when a thread becomes deadlocked because it is waiting on resources from another thread. Table 12-1 Actionable knowledge changes depending on who is running the test Consider the following tests and how the actionable knowledge changes depending on who is running the test.
Verizon in home agent optimize system speed software#
Results data provides actionable knowledge, but the meaning may be contingent on your role in the software process.
Verizon in home agent optimize system speed how to#
So, before showing how to analyze the test result data generated from the intelligent test agents in the previous chapter, this section presents what we can reasonably expect to uncover from conducting a test. In this regard Alexander Pope had it right when he wrote: “A little learning is a dangerous thing Drink deep, or taste not the Pierian spring.” Thoroughly analyzing test results produces actionable knowledge whereas, looking only at the surface of the test result data can lead to terrible problems for yourself, your company and your project. You may be tempted to conduct a cursory review of test results for actionable knowledge. Turning Test Agent Results into Actionable KnowledgeĬhapter 11 ended with a strong word of caution. This chapter explains how to turn the recorded results into actionable knowledge. The master component of the test handled configuration, test agent thread creation, and recorded the results to a special log file. It presented the test goals, user archetypes, and test agents for an example stock-trading firm.
With the method presented in this chapter, you will be able to demonstrate the system’s ability to achieve scalability, reliability, and functionality.Ĭhapter 11 took the techniques presented in earlier chapters to command services over a variety of protocols (HTTP, HTTPS, SOAP, XML-RPC) and build a test modeled after a set of user archetypes. This chapter shows how to understand and analyze a Web-enabled application while the test is running and how to analyze the results data after the test is finished. The log file is one of many places you can observe problems and find places to optimize the Web-enabled application.
Looking into the logged data allows us to see many immediate problems with the Web-enabled application under test. It also usually generates a huge amount of logged data. All of this activity provides a near-production experience from which we can uncover scalability problems, concurrency problems, and reliability problems. First, we checked for the correct functional results, then we checked the host's ability to serve increasing numbers of concurrent users. We designed user archetypes, wrote multi-protocol intelligent test agents, and made requests to an application host. Structure, software design, and protocol design to implement a Web-enabled application with great scalability, reliability, and functionality. The stock trading information system, in Chapter 11, presents a methodology, infra. 12Ĭ H A P T E R 1 2 Turning Test Agent Results into Actionable Knowledge The Java Design and Test Automation Guide Chap.
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