This document provides an overview of using LoadRunner to perform load and performance testing. It covers topics such as why performance testing is important, definitions of different types of testing, benchmark design, LoadRunner components, the load testing process, building scripts using the Virtual User Generator, playing back scripts, solving common issues, preparing scripts for load testing, creating load testing scenarios in the LoadRunner Controller, running load tests, and analyzing results.
This document provides an overview of LoadRunner performance testing tool. It introduces key concepts such as performance testing, the need for automated performance testing, and the core activities involved. It describes LoadRunner components like VuGen for creating scripts and the Controller for creating scenarios. It also covers topics like protocol support, installation, terminology, recording and enhancing scripts, creating scenarios both manually and goal-oriented, and running/monitoring scenarios.
It's a very basic introduction of Load Runner for beginners, i explored it at my own, prepared slides & shared it with my colleagues.
What is Load Runner & why we need Performance testing etc.
Enjoy :)
When you know the basics of Performance testing, the next question that comes to mind is how we can conduct Performance Testing. There are multiple tools available in the industry to meet the purpose. Among them, the most dominant one is Microfocus LoadRunner. This particular tool ease down the whole process of performance testing and helped to achieve the goal. In this session, you learn about LoadRunner, its fundamental components, and finally the LoadRunner usage in Performance Testing through a Demo.
Introduction to Performance Testing & LoadrunnerAisha Mazhar
This document discusses HP LoadRunner, a performance testing tool. It provides an overview of LoadRunner, including what performance testing is, the types of performance testing, limitations of manual testing, LoadRunner components, designing and executing scenarios, and analyzing results. The key points are that LoadRunner automates performance testing by using virtual users to simulate real user loads and measure system behavior, components include VuGen, Controller, and Load Generators, and it allows designing, running, and analyzing load testing scenarios to evaluate system performance.
** Performance Testing Using JMeter: https://www.edureka.co/jmeter-trainin... **
This edureka PPT on "JMeter vs LoadRunner" will provide you in-depth knowledge about how these two tools are used for performance testing. It will compare the tools based on certain parameters which will help you in deciding the best out of the two.
Introduction to JMeter
Introduction to LoadRunner
Parameters of Comparison
Availability
Load Generation Capacity
Execution
Analysis Report
Open-source & Community
Scripting
Building Test Scenarios
Elements
Software Testing Playlist: http://bit.ly/2uYgRJj
Software Testing Blog Series: http://bit.ly/2B7C3QR
Selenium playlist: https://goo.gl/NmuzXE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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Facebook: https://www.facebook.com/edurekaIN/
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LinkedIn: https://www.linkedin.com/company/edureka
Detailed presentation on performance testing and Loadrunner.
Complete course is available on udemy.
Use below link to get the course for just 20 USD
https://www.udemy.com/performance-testing-using-microfocus-loadrunner-basics-advanced/?couponCode=PTLR20D
This document provides an overview of LoadRunner performance testing tool. It introduces key concepts such as performance testing, the need for automated performance testing, and the core activities involved. It describes LoadRunner components like VuGen for creating scripts and the Controller for creating scenarios. It also covers topics like protocol support, installation, terminology, recording and enhancing scripts, creating scenarios both manually and goal-oriented, and running/monitoring scenarios.
It's a very basic introduction of Load Runner for beginners, i explored it at my own, prepared slides & shared it with my colleagues.
What is Load Runner & why we need Performance testing etc.
Enjoy :)
When you know the basics of Performance testing, the next question that comes to mind is how we can conduct Performance Testing. There are multiple tools available in the industry to meet the purpose. Among them, the most dominant one is Microfocus LoadRunner. This particular tool ease down the whole process of performance testing and helped to achieve the goal. In this session, you learn about LoadRunner, its fundamental components, and finally the LoadRunner usage in Performance Testing through a Demo.
Introduction to Performance Testing & LoadrunnerAisha Mazhar
This document discusses HP LoadRunner, a performance testing tool. It provides an overview of LoadRunner, including what performance testing is, the types of performance testing, limitations of manual testing, LoadRunner components, designing and executing scenarios, and analyzing results. The key points are that LoadRunner automates performance testing by using virtual users to simulate real user loads and measure system behavior, components include VuGen, Controller, and Load Generators, and it allows designing, running, and analyzing load testing scenarios to evaluate system performance.
** Performance Testing Using JMeter: https://www.edureka.co/jmeter-trainin... **
This edureka PPT on "JMeter vs LoadRunner" will provide you in-depth knowledge about how these two tools are used for performance testing. It will compare the tools based on certain parameters which will help you in deciding the best out of the two.
Introduction to JMeter
Introduction to LoadRunner
Parameters of Comparison
Availability
Load Generation Capacity
Execution
Analysis Report
Open-source & Community
Scripting
Building Test Scenarios
Elements
Software Testing Playlist: http://bit.ly/2uYgRJj
Software Testing Blog Series: http://bit.ly/2B7C3QR
Selenium playlist: https://goo.gl/NmuzXE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Detailed presentation on performance testing and Loadrunner.
Complete course is available on udemy.
Use below link to get the course for just 20 USD
https://www.udemy.com/performance-testing-using-microfocus-loadrunner-basics-advanced/?couponCode=PTLR20D
HP LoadRunner is software for load testing applications to validate performance and identify bottlenecks. It replaces real users with thousands of virtual users to generate measurable loads on systems. Load testing with LoadRunner helps mitigate risks during launches by exactly mimicking real user behavior and pinpointing issues. It works by recording user processes as automated scripts, designing load scenarios, and analyzing results to determine if service level objectives are met.
Load testing is done to determine system limits, verify response times under high load, check stability, and predict future needs. Open source tools like JMeter, Yandex Tank, and Taurus can be used. With JMeter, a test plan is created with thread groups, HTTP requests, and listeners to start load testing. Issues like slow responses or server crashes are identified. Short term fixes include restarting servers or tuning configurations, while long term solutions involve moving to the cloud, using caching, or splitting applications into microservices. Other commercial load testing tools are also available from companies like SOASTA and BlazeMeter.
This document discusses performance testing tools and techniques. It defines performance from the perspectives of developers, infrastructure, and end users. Key aspects covered include defining realistic user scenarios, available tools like JMeter, ApacheBench, Gatling and Locust, and the importance of continuous performance testing. The document recommends using the Apdex score as part of your definition of done, specifying good test scenarios, running tests simultaneously, choosing the right tool for your needs, and considering tools like Taurus that enable continuous performance testing.
The document discusses performance testing using Apache JMeter. It covers topics like an overview of performance testing, the purpose of performance testing, key types of performance testing like load testing and stress testing. It also discusses pre-requisites of performance testing, the performance testing life cycle, challenges of performance testing and how to record and playback tests using JMeter.
This document provides an agenda and overview for a performance testing training with JMeter. It begins with an introduction to performance testing, including the purpose and types of performance testing. It then covers getting started with JMeter, including installation, setup, and running JMeter. The remainder of the document outlines the content to be covered, including building test plans with JMeter, load and performance testing of websites, parameterization, adding assertions, and best practices. The goal is to teach participants how to use JMeter to perform various types of performance testing of applications and websites.
This document provides an overview of performance and load testing basics. It defines key terms like throughput, response time, and tuning. It explains the difference between performance, load, and stress testing. Performance testing is done to evaluate system speed, throughput, and utilization in comparison to other versions or products. Load testing exercises the system under heavy loads to identify problems, while stress testing tries to break the system. Performance testing should occur during design, development, and deployment phases to ensure system meets expectations under load. Key transactions like high frequency, mission critical, read, and update transactions should be tested. The testing process involves planning, recording test scripts, modifying scripts, executing tests, monitoring tests, and analyzing results.
The document discusses performance testing, including its goals, importance, types, prerequisites, management approaches, testing cycle, activities, common issues, typical fixes, challenges, and best practices. The key types of performance testing are load, stress, soak/endurance, volume/spike, scalability, and configuration testing. Performance testing aims to assess production readiness, compare platforms/configurations, evaluate against criteria, and discover poor performance. It is important for meeting user expectations and avoiding lost revenue.
Apache JMeter is an open-source performance testing tool used to test the performance of web applications. It works by acting like a group of users sending requests to a target server and collecting response times and other statistics. JMeter is useful for performance testing because it is free to use, supports multiple protocols, has a user-friendly GUI, and can generate detailed reports on test results. To perform a test, users create a test plan with thread groups to simulate users, HTTP requests to send to the server, and listeners to monitor responses and performance.
The document provides an overview of load testing and the LoadRunner tool. It discusses:
- Why load testing is important to test application performance, stability, and ability to handle expected user loads.
- The components of LoadRunner including VuGen for recording scripts, the Controller for managing tests, and Analysis for reporting.
- How LoadRunner replaces human users with virtual users (Vusers) that emulate user actions and loads via scripted scenarios. This allows testing at large scales that would be difficult with real users.
The document summarizes the results of performance testing on a system. It provides throughput and scalability numbers from tests, graphs of metrics, and recommendations for developers to improve performance based on issues identified. The performance testing process and approach are also outlined. The resultant deliverable is a performance and scalability document containing the test results but not intended as a formal system sizing guide.
QualiTest provides load and performance testing services to determine a system's behavior under normal and peak load conditions. Their testing process identifies maximum operating capacity and elements that cause degradation. They ensure applications can handle predicted traffic volumes. QualiTest uses various load testing tools and methodologies to simulate real-world usage and stress test systems. Their testing delivers reports on defects, tool evaluations, and ongoing support for quality improvement.
NeoLoad is a load testing tool that allows users to record browser sessions, define virtual users and test scenarios, run load tests, and analyze results. The document provides an overview of NeoLoad and guides users through setting up and running a sample load test in 3 main steps: recording a test scenario, running the test, and analyzing results. Key features of NeoLoad discussed include recording browser sessions, configuring virtual users and populations, running tests while monitoring performance, and filtering and graphing results.
Performance testing validates an application's responsiveness, stability, and other quality attributes under various workloads. It involves load testing, stress testing, endurance testing, spike testing, volume testing, availability testing, and scalability testing. The key parameters analyzed are response time, throughput, and memory utilization. Performance testing helps determine an application's speed, scalability, stability, and ability to handle changes in load and traffic over time.
This document discusses performance testing and provides information on several related topics:
- It defines performance, load, and stress testing and explains their differences.
- It outlines why performance testing is important, when it should be conducted, and what aspects of a system should be tested.
- The performance testing process is described as involving planning, creating test scenarios and scripts, running tests, monitoring tests, and analyzing results.
- Automated performance testing is presented as more effective than manual testing due to issues with resources, coordination, and repeatability when using human testers.
Performance testing with JMeter provides an introduction to key concepts and how to implement performance tests using JMeter. Some important steps include designing test plans, preparing the environment, determining metrics and goals, notifying stakeholders, and using JMeter elements like thread groups, samplers, listeners, assertions and configuration elements to simulate load and measure performance. JMeter is an open source tool that can run in GUI or non-GUI mode for load testing web applications and determining maximum operating capacity and bottlenecks under heavy loads.
The document provides an overview of various types of performance testing that can be conducted including speed tests, contention tests, volume tests, stress/overload tests, fail-over tests, spike tests, endurance tests, scalability tests, and availability tests. For each type of test, it describes the purpose and provides examples of accomplishments. It also outlines the course topics to be covered related to performance planning, load testing, and tools.
Loadrunner is a flagship load testing product from HP that commands over 70% of the market share. It can simulate thousands of users accessing a website or application simultaneously to test performance under heavy load. Loadrunner uses a 3-tier architecture with load generators that simulate users, a controller to manage the test, and monitoring tools to analyze performance. It supports testing many common protocols and can test websites, applications, databases, and other systems.
HP LoadRunner is software for load testing applications to validate performance and identify bottlenecks. It replaces real users with thousands of virtual users to generate measurable loads on systems. Load testing with LoadRunner helps mitigate risks during launches by exactly mimicking real user behavior and pinpointing issues. It works by recording user processes as automated scripts, designing load scenarios, and analyzing results to determine if service level objectives are met.
Load testing is done to determine system limits, verify response times under high load, check stability, and predict future needs. Open source tools like JMeter, Yandex Tank, and Taurus can be used. With JMeter, a test plan is created with thread groups, HTTP requests, and listeners to start load testing. Issues like slow responses or server crashes are identified. Short term fixes include restarting servers or tuning configurations, while long term solutions involve moving to the cloud, using caching, or splitting applications into microservices. Other commercial load testing tools are also available from companies like SOASTA and BlazeMeter.
This document discusses performance testing tools and techniques. It defines performance from the perspectives of developers, infrastructure, and end users. Key aspects covered include defining realistic user scenarios, available tools like JMeter, ApacheBench, Gatling and Locust, and the importance of continuous performance testing. The document recommends using the Apdex score as part of your definition of done, specifying good test scenarios, running tests simultaneously, choosing the right tool for your needs, and considering tools like Taurus that enable continuous performance testing.
The document discusses performance testing using Apache JMeter. It covers topics like an overview of performance testing, the purpose of performance testing, key types of performance testing like load testing and stress testing. It also discusses pre-requisites of performance testing, the performance testing life cycle, challenges of performance testing and how to record and playback tests using JMeter.
This document provides an agenda and overview for a performance testing training with JMeter. It begins with an introduction to performance testing, including the purpose and types of performance testing. It then covers getting started with JMeter, including installation, setup, and running JMeter. The remainder of the document outlines the content to be covered, including building test plans with JMeter, load and performance testing of websites, parameterization, adding assertions, and best practices. The goal is to teach participants how to use JMeter to perform various types of performance testing of applications and websites.
This document provides an overview of performance and load testing basics. It defines key terms like throughput, response time, and tuning. It explains the difference between performance, load, and stress testing. Performance testing is done to evaluate system speed, throughput, and utilization in comparison to other versions or products. Load testing exercises the system under heavy loads to identify problems, while stress testing tries to break the system. Performance testing should occur during design, development, and deployment phases to ensure system meets expectations under load. Key transactions like high frequency, mission critical, read, and update transactions should be tested. The testing process involves planning, recording test scripts, modifying scripts, executing tests, monitoring tests, and analyzing results.
The document discusses performance testing, including its goals, importance, types, prerequisites, management approaches, testing cycle, activities, common issues, typical fixes, challenges, and best practices. The key types of performance testing are load, stress, soak/endurance, volume/spike, scalability, and configuration testing. Performance testing aims to assess production readiness, compare platforms/configurations, evaluate against criteria, and discover poor performance. It is important for meeting user expectations and avoiding lost revenue.
Apache JMeter is an open-source performance testing tool used to test the performance of web applications. It works by acting like a group of users sending requests to a target server and collecting response times and other statistics. JMeter is useful for performance testing because it is free to use, supports multiple protocols, has a user-friendly GUI, and can generate detailed reports on test results. To perform a test, users create a test plan with thread groups to simulate users, HTTP requests to send to the server, and listeners to monitor responses and performance.
The document provides an overview of load testing and the LoadRunner tool. It discusses:
- Why load testing is important to test application performance, stability, and ability to handle expected user loads.
- The components of LoadRunner including VuGen for recording scripts, the Controller for managing tests, and Analysis for reporting.
- How LoadRunner replaces human users with virtual users (Vusers) that emulate user actions and loads via scripted scenarios. This allows testing at large scales that would be difficult with real users.
The document summarizes the results of performance testing on a system. It provides throughput and scalability numbers from tests, graphs of metrics, and recommendations for developers to improve performance based on issues identified. The performance testing process and approach are also outlined. The resultant deliverable is a performance and scalability document containing the test results but not intended as a formal system sizing guide.
QualiTest provides load and performance testing services to determine a system's behavior under normal and peak load conditions. Their testing process identifies maximum operating capacity and elements that cause degradation. They ensure applications can handle predicted traffic volumes. QualiTest uses various load testing tools and methodologies to simulate real-world usage and stress test systems. Their testing delivers reports on defects, tool evaluations, and ongoing support for quality improvement.
NeoLoad is a load testing tool that allows users to record browser sessions, define virtual users and test scenarios, run load tests, and analyze results. The document provides an overview of NeoLoad and guides users through setting up and running a sample load test in 3 main steps: recording a test scenario, running the test, and analyzing results. Key features of NeoLoad discussed include recording browser sessions, configuring virtual users and populations, running tests while monitoring performance, and filtering and graphing results.
Performance testing validates an application's responsiveness, stability, and other quality attributes under various workloads. It involves load testing, stress testing, endurance testing, spike testing, volume testing, availability testing, and scalability testing. The key parameters analyzed are response time, throughput, and memory utilization. Performance testing helps determine an application's speed, scalability, stability, and ability to handle changes in load and traffic over time.
This document discusses performance testing and provides information on several related topics:
- It defines performance, load, and stress testing and explains their differences.
- It outlines why performance testing is important, when it should be conducted, and what aspects of a system should be tested.
- The performance testing process is described as involving planning, creating test scenarios and scripts, running tests, monitoring tests, and analyzing results.
- Automated performance testing is presented as more effective than manual testing due to issues with resources, coordination, and repeatability when using human testers.
Performance testing with JMeter provides an introduction to key concepts and how to implement performance tests using JMeter. Some important steps include designing test plans, preparing the environment, determining metrics and goals, notifying stakeholders, and using JMeter elements like thread groups, samplers, listeners, assertions and configuration elements to simulate load and measure performance. JMeter is an open source tool that can run in GUI or non-GUI mode for load testing web applications and determining maximum operating capacity and bottlenecks under heavy loads.
The document provides an overview of various types of performance testing that can be conducted including speed tests, contention tests, volume tests, stress/overload tests, fail-over tests, spike tests, endurance tests, scalability tests, and availability tests. For each type of test, it describes the purpose and provides examples of accomplishments. It also outlines the course topics to be covered related to performance planning, load testing, and tools.
Loadrunner is a flagship load testing product from HP that commands over 70% of the market share. It can simulate thousands of users accessing a website or application simultaneously to test performance under heavy load. Loadrunner uses a 3-tier architecture with load generators that simulate users, a controller to manage the test, and monitoring tools to analyze performance. It supports testing many common protocols and can test websites, applications, databases, and other systems.
This document discusses performance testing of applications. It defines performance testing and describes different types of performance testing tools that can be used for testing applications from the client or server side. It emphasizes the importance of performance testing to ensure applications can handle expected user loads and transactions and provide positive user experiences. Key goals of performance testing are to test response times, speed, resource usage, stability, and throughput under different loads. Examples are provided of how performance issues negatively impacted major companies through lost revenue and customers.
The document provides a short history of performance engineering, beginning in the 1960s with the introduction of instrumentation tools for mainframe systems and the first studies of human response times. Key developments include the establishment of the performance engineering community in the 1970s, the first commercial performance analysis tools and distributed computing in the late 1970s, and the publication of early books on software performance engineering and applying existing expertise to web performance in the 1990s. The history shows that performance has been an ongoing concern across different computing paradigms, with new challenges arising with each new technology.
The document discusses performance optimization at InfoJobs, describing how they use Scrum for development across 6 teams, monitor real user experience (RUX) to track performance in production, and how the QA team performs load testing to validate performance before new releases go live while also generating comparison reports on metrics like page load times and slowest pages.
The document discusses microservice performance. It recommends measuring performance correctly by recording timestamped requests with latency and success/failure data. Latency distributions have heavy tails so percentiles are important to understand. Throughput and latency are related by Little's Law. Latency stacks across services so simulation tools are useful. Amdahl's Law and Universal Scalability Law can help identify optimization targets and forecast scalability. The key is to measure performance correctly to identify potential issues and optimize the right parts of the system.
The document discusses various ways to test the quality and functionality of web applications. It describes testing content, structure, navigation, interfaces, performance, security, compatibility with different configurations, and usability. The goals of testing are to uncover errors in content, functionality, design, and user experience. A variety of techniques are proposed to thoroughly test the various components and aspects of a web application.
How to Get Automatic Analysis for Load Test ResultsClare Avieli
The ability to fully automate your results analysis is vital in today’s Continuous Integration and Continuous Deployment era. Agile practices like Microservices exacerbate this need, giving you tens of services to test, ten times a day! Analysis by the human eye is impossible - you need automation.
But automating the analysis is extremely difficult due to the ever-increasing complexities of load testing processes and timeline based reports. As such, contemporary load testing tools and services offer excellent ways to present reports but fall short when it comes to the analysis.
In this presentation, Andrey Pokhilko (founder of JMeter-plugins.org and Loadosophia) explores how to take automatic result analysis and decision making to a new level.
Join us and discuss:
• Why is it tough to fully automate analysis and decision making on test results
• How humans analyze the test in practice - which KPIs they look at
• Which decisions can be made automatically during test execution
• Which facts can be automatically concluded post-test
• Practical results from several months of method application
Performance testing for web applications – techniques, metrics and profilingTestCampRO
The document discusses techniques for performance testing web applications, including staging the testing environment, building test assets, running tests, and analyzing metrics. It describes deploying a testbed, eliminating deployment issues, analyzing client data to develop test scenarios, executing manual and automated tests, and gathering metrics on system performance, databases, and applications. The goal is to identify potential performance issues before load testing at higher user volumes expected by clients.
How to successfully load test over a million concurrent users stp con demoApica
Does your company attract millions of visitors, users or even subscribers to your site or application? Whether you answered yes or no, it’s still a great idea to know what it takes to test 2+ million concurrent users, fast. In this presentation, you’ll get a first-hand, live walk-through of Apica Load Test doing a mega test of 2 million concurrent users.
Performance Test Automation Framework PresentationMikhael Gelezov
This document discusses problems with manual performance testing such as being time consuming, prone to human errors and high costs. It then describes a performance test automation framework that uses tools like Jenkins, JMeter and Grafana to run performance tests continuously, monitor results in real-time, and generate reports to address these issues. The framework allows scripts to be committed to a repository, triggered by Jenkins for execution across test environments, and results analyzed through live dashboards and detailed reports.
Overview
1. The main goals of performance testing
2. The Advantages of Performance Tests
3. The Disadvantages of Performance Tests
4. Types of performance tests
5. Determining a successful performance testing project
Enjoy!
This document provides guidelines for testing e-commerce software. It discusses the need for testing to enhance integrity and detect errors. The objectives of testing are reliability, quality, assurance and performance. Challenges include the rapid change of technology and varied customer profiles. The document outlines best practices and describes different types of testing for the web, middle, and data tiers including content, functionality, load, security and more.
The document discusses gathering requirements for performance testing an application. It lists questions to ask about the application type and architecture, test environment, workload model, and performance goals. Key information needs include the application technology, database and server used, network details, protocols, user sessions and load over time, and goals for response times and system utilization under load. The requirements gathered will help determine the appropriate performance tests and pass/fail criteria.
Overview of JMS messaging API.
JMS (Java Messaging Service) is an API for asynchronous message based communication between Java based applications.
JMS implementations (instances that implement the JMS API) are called JMS providers.
JMS defines two messaging domains. Point-to-point queues are typically used between one or multiple message senders and a single message receiver.
Topics are multi-point queues where messages are distributed to multiple receivers. As such topics resemble a black board.
Like many other message oriented middleware technologies, JMS provides advanced functions like persistent message delivery mode or different message acknowledgment modes.
Additionally, messages can be sent and received in a transacted mode thus ensuring that either all or no messages are sent and received.
JMS integrates into EJB (Enterprise Java Beans) through message driven beans.
Are you new to performance testing? This slides are for those of you who want to explore and learn where and how to start testing application performance. During this web event, our performance testing experts will reveal the key pieces and parts of performance testing, including the phases of the test and how HP LoadRunner supports each phase.
In this presentation which was delivered to testers in Manchester, I help would-be performance testers to get started in performance testing. Drawing on my experiences as a performance tester and test manager, I explain the principles of performance testing and highlight some of the pitfalls.
The document provides an overview of load testing with LoadRunner. It discusses topics like why performance testing is important, definitions of stress, load and performance testing, benchmark design, LoadRunner components and the load testing process. It also describes how to record a script with LoadRunner's Virtual User Generator, set runtime behavior, solve common playback issues, prepare a script for load testing by adding transactions and checkpoints, and verify the success of a test.
The document provides an introduction to Oracle Application Testing Suite e-Load and its features for load testing web applications, including setting up virtual users and profiles, running load tests, and analyzing results. It describes how to configure e-Load settings for aspects like authentication, browser emulation, caching, and download management to simulate real user behavior under load.
QuickTest allows automated testing of websites to address the drawbacks of manual testing such as being time-consuming, tedious, and unable to thoroughly test every feature before public release. It can create tests that check all aspects of a website and run them faster than any human each time the site changes. A document then describes the process of recording a test on the Mercury Tours website that books a flight, running the test, and analyzing the results to ensure the site functions correctly. It also discusses adding a page checkpoint to check properties of a webpage.
QuickTest allows automated testing of websites to test all features faster and more thoroughly than manual testing. It simulates human actions like clicking and entering text. Tests can be run repeatedly and reliably to ensure a website works as expected even after changes. Well-designed automated tests cover all website features, saving time and catching bugs that could be missed with only manual testing.
This document provides an overview of performance testing concepts and LoadRunner software. It discusses the need for performance testing, different types of performance testing, and introduces LoadRunner components and functionality. The document then walks through the process of creating a LoadRunner script using VuGen, including recording a script, customizing runtime settings, and viewing test results. Key LoadRunner concepts like correlation, parameterization, and functions are also explained briefly.
HP LoadRunner software allows you to prevent application performance problems by detecting bottlenecks before a new system or upgrade is deployed. The testing solution LoadRunner enables you to test rich Internet applications, Web 2.0 technologies, ERP and CRM applications, and legacy applications. It gives you a picture of end-to-end system performance before going live so that you can verify that new or upgraded applications meet performance requirements
Quick guide to plan and execute a load testduke.kalra
The document provides guidance on developing a load testing approach, emphasizing the importance of requirements analysis, defining test scenarios based on user load and activity analysis, and configuring and executing load tests in LoadRunner while collecting key performance metrics. Proper planning including understanding the goal of testing, estimating user loads, and mirroring the production environment is recommended to efficiently perform load testing and generate useful reports.
The document discusses client side performance testing. It defines client side performance as how fast a page loads for a single user on a browser or mobile device. Good client side performance is important for user experience and business metrics like sales. It recommends rules for faster loading websites, and introduces the WebPageTest tool for measuring client side performance metrics from multiple locations. WebPageTest provides waterfall views, filmstrip views, packet captures and reports to analyze page load times and identify optimization opportunities.
The document provides an overview and agenda for a LoadRunner training course. It introduces LoadRunner and its components, including VuGen for recording scripts, the Controller for managing tests, and Analysis for reporting. It discusses the LoadRunner workflow and how it emulates real users to load test applications. Key topics covered include virtual users (Vusers), scripts, scenarios, protocols, and runtime settings.
Slides from my 4-hour workshop on Client-Side Performance Testing conducted at Phoenix, AZ in STPCon 2017 (March).
Workshop Takeaways:
Understand difference between is Performance Testing and Performance Engineering.
Hand’s on experience of some open-source tools to monitor, measure and automate Client-side Performance Testing.
Examples / code walk-through of some ways to automate Client-side Performance Testing.
See blog for more details - https://essenceoftesting.blogspot.com/2017/03/workshop-client-side-performance.html
Top 20 LoadRunner Interview Questions and Answers in 2023.pdfAnanthReddy38
What is LoadRunner?
LoadRunner is a performance testing tool developed by Micro Focus. It allows testers to measure and analyze system performance under various load conditions.
What are the components of LoadRunner?
LoadRunner consists of three main components: Virtual User Generator (VuGen), Controller, and Analysis.
What is VuGen in LoadRunner?
VuGen (Virtual User Generator) is a tool in LoadRunner used for recording and scripting user actions on the application under test.
What is the purpose of the LoadRunner Controller?
The LoadRunner Controller is used to configure and manage load tests. It allows you to define scenarios, allocate resources, and monitor test execution.
Loadster Load Testing by RapidValue SolutionsRapidValue
This document explains about Loadster Load testing and provides the details about the steps that are required to perform the load test. This document is prepared after the successful implementation of the Load Test in one of our customer projects. The blog, essentially, gives you an idea of how Load Testing is considered to be a method to determine how an application will behave under load. “Load”, generally, refers to the total user traffic at a given time. This document also explains how Load testing software can be used to gain knowledge about several different metrics like stress, stability, spike, scalability, baseline.
Loadster workbench is an integrated environment which enables you to perform load testing. Load test can be performed using any number of virtual users. It gives us provision to record and edit the scripts and the test results are obtained after performing the test. It has an in-built load engine through which we can perform load testing with concurrent virtual users.
Project Repository
Loadster has a repository called project repository for each project, script etc. Test results, after completing the load test, is also stored in this repository.
Dashboard
When the load test starts providing information, regarding the number of users, time taken to complete the test is displayed on the dashboard. In spite of that, there are number of options provided on the left side of dashboard namely response times, network throughput, transaction throughput, transaction, error, virtual user, and load engine information. You can view a graph of load test by clicking on any of these options as the load test runs.
Diana Carciu - Performance Testing with SoapUi and Siege.pptxCodecamp Romania
This document provides an overview of performance testing with SoapUI and Siege. It discusses why performance testing is important for aspects like speed, scalability, and stability. It then describes what performance testing is and how to conduct it, including load testing, stress testing, and endurance testing. The document also provides examples of using SoapUI for testing web services and Siege for load testing websites. It shares some best practices for performance testing and resources for further information.
This document discusses performance testing and provides an overview of two tools that can be used: SoapUI and Siege. It explains why performance testing is important to evaluate the speed, scalability, and stability of an application. Some key aspects that are measured include response time, throughput, server resources, and behavior under different load levels. The document demonstrates how to conduct performance tests using these two tools and highlights some considerations for a performance test plan.
The document discusses test automation concepts and introduces QuickTest Professional (QTP) 9.2. It covers the benefits of automation, the automation life cycle, supported technologies and browsers, the object repository, recording and run modes, options, and basic VBScript concepts used in QTP.
The document discusses test automation concepts and introduces QuickTest Professional (QTP) 9.2. It covers the benefits of automation, the automation life cycle, supported technologies, record and run modes, main tools and features in QTP, and key areas like script structure, parameterization, checkpoints, and exception handling.
The document provides an overview of automation testing concepts using QuickTest Professional (QTP) 9.2. It discusses what automation testing is, its benefits, and the automation life cycle. It also covers topics like supported technologies, add-ins, recording and run modes, and the main QTP window. Sample script snippets demonstrate commonly used QTP functions.
The document provides an overview of load testing using NeoLoad. It discusses why load testing is important, the differences between functional and load testing, and the main components of NeoLoad including scripting, execution, analysis and monitoring. It then describes the basic process of creating a NeoLoad test including recording a scenario to create a virtual user, setting the population size and scenario, running the test, and analyzing results on things like response times, errors and graphs. Communication between NeoLoad and the server is agentless using push technology.
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The document discusses automating QuickTest operations using the QuickTest automation object model. It describes how the object model provides objects, methods and properties to control QuickTest programmatically. Examples given include writing programs to configure QuickTest, run tests/components, and perform repetitive tasks like regression testing more efficiently.
The document discusses severity and priority levels for software testing. There are 5 severity levels - critical, major, moderate, minor, and cosmetic. Critical defects terminate the system or corrupt data while cosmetic defects are related to aesthetics. Priority is based partially on severity but also considers frequency of failure, visibility, and project impact. The priority levels are resolved immediately, give high attention, normal queue, low priority, and suspend.
The document provides an overview of software testing techniques and strategies. It discusses unit testing, integration testing, validation testing, system testing, and debugging. The key points covered include:
- Unit testing involves testing individual software modules or components in isolation from the rest of the system. This includes testing module interfaces, data structures, boundary conditions, and error handling paths.
- Integration testing combines software components into clusters or builds to test their interactions before full system integration. Approaches include top-down and bottom-up integration.
- Validation testing verifies that the software meets the intended requirements and customer expectations defined in validation criteria.
- System testing evaluates the fully integrated software system, including recovery, security, stress,
This document provides an overview of automation fundamentals and an introduction to QuickTest Professional (QTP) 9.2. It discusses what test automation is, the benefits of automation, the automation life cycle, and when automation is applicable. It also describes the QTP user interface, how to record and run tests, view results, and work with objects and the object repository. The key points covered are test automation concepts, the QTP interface and features, best practices for recording, running and viewing tests, and how QTP recognizes and stores objects.
The document discusses how to use correlation in LoadRunner scripts to handle dynamic values, providing an example of correlating a timestamp and checksum that change with each request. It walks through the steps of finding dynamic values, capturing them using the web_reg_save_param function, replacing hardcoded values in the script with parameters, and verifying that the correlated script now works properly by submitting dynamic rather than static values.
This document provides an overview of automation fundamentals and an introduction to QuickTest Professional (QTP) 9.2. It discusses what test automation is, the benefits of automation, and factors to consider in automation planning. It also covers supported technologies and browsers in QTP, the add-in manager, and the main QTP window interface. The document provides a high-level introduction to recording and running tests in QTP.
The document provides an overview of manual test scripting including:
- Introduction to software testing techniques like black box and white box testing
- Details on test plans, test cases, and their purpose
- Guidelines for designing test cases using techniques like boundary value analysis and equivalence partitioning
- Format and elements of test cases
- Process for integration testing and writing integration test cases
- Best practices for testability, naming conventions, and reviewing test cases
Unit testing refers to testing individual units or components of an application to ensure they are working as intended. It is typically performed by developers during coding to validate each part of the program. The goal of unit testing is to isolate units and validate their correctness independently before integration testing. Common techniques for unit testing include equivalence partitioning, boundary value analysis, and positive/negative testing.
This document provides an overview of QuickTest Professional 8.0 (Basic). It describes the QuickTest window and elements, introduces object repositories and synchronization, checkpoints, parameters, and testing best practices. Key topics covered include recording and running tests, viewing results, changing object logical names, adding synchronization steps, using constant and regular expression checkpoints, and parameterizing tests using different data types in the data table. The document is intended to help users learn the basics of QuickTest Professional and properly structure, record, parameterize and execute automated tests.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.
13 20 Our recommended rule of thumb when selecting which test cases to automate: The more repetitive the execution, the better candidate the test is for automation.