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Basics of load testing in Enterprise Applications using J-Meter

5 minutes read

We need to test websites and applications for performance standards before delivering them to the client. The performance or benchmark testing is an ongoing function of software quality assurance that extends throughout the life cycle of the project. To build standards into the architecture of a system — the stability and response time of an application is extensively tested by applying a load or stress to the system.

Essentially, ‘load’ means the number of users using the application while ‘stability’ refers to the system’s ability to withstand the load created by the intended number of users. ‘Response time’ indicates the time taken to send a request, run the program and receive a response from a server.

Load testing on applications can be a challenging ordeal if a performance testing strategy is not predetermined. Testing tasks require multifaceted skill-sets — from writing test scripts, monitoring and analyzing test results to tweaking custom codes and scripts, and developing automated test scenarios for the actual testing.

So, is load testing on applications really necessary?

Quality testing ensures that the system is reliable, built for capacity and scalable. To achieve this, the involved stakeholders decide the budget considering its business impact.

Now, this raises a question — how do we predict traffic based on past trends? and how can we make the system more efficient to handle traffic without any dropouts? Also, if and when we hit peak loads, then how are we going to address the additional volume? For this, it is crucial to outline the performance testing strategy beforehand.

5 Key Benefits of Performance Testing

  1. It identifies the issues at the early stage before they become too costly to resolve (for example, exposing bugs that do not surface in cursory testing, such as memory management bugs, memory leaks, buffer overflows, etc.).
  2. Performance testing reduces development cycles, produces better quality and more scalable code.
  3. It prevents revenue and credibility loss due to poor web site performance.
  4. To enable intelligent planning for future scaling.
  5. It ensures that the system meets performance expectations (response time, throughput, etc.) under-designed levels of load.

Organizations don’t prefer manual testing these days because it is expensive and requires human resources and hardware. It is also quite complex to coordinate and synchronize multiple testers. Also, repeatability is limited in manual testing.

To find the stability and response time of each API, we can test different scenarios by varying the load at different time intervals on the application. We can then automate the application by using any performance testing tool.

Performance Testing Tools

There are a bunch of different tools available for testers such as Open Source testing Tools — Open STA Diesel Test, TestMaker, Grinder, LoadSim, J-Meter, Rubis; Commercial testing tools— LoadRunner, Silk Performer, Qengine, Empirix e-Load.

Among these, the most commonly used tool is Apache J-Meter. It is a 100% Java desktop application with a graphical interface that uses the Swing graphical API. It can, therefore, run on any environment/workstation that accepts Java virtual machine, for example, Windows, Linux, Mac, etc.

We can automate testing the application by integrating the ‘selenium scripts’ in the J-Meter tool. (The software that can perform load tests, performance-functional tests, regression tests, etc. on different technologies.)

[Related: A Complete Guide to Regression Testing in Agile]

If the project is large in scope and the number of users keeps increasing day-by-day then the server’s load will be greater. In such situations, Performance testing is useful to identify at what point the application will crash. To find the number of errors and warnings in the code, we use the J-Meter tool.

How J-Meter Works

J-Meter simulates a group of users sending requests to a target server and returns statistics that show the performance/functionality of the target server/application via tables, graphs, etc.

The following figure illustrates how J-Meter works:

How J-Meter works - Load Testing on applications

The J-Meter performance testing tool can find the performance of any application (no matter whatever the language used to build the project).

First, it requires a test plan which describes a series of steps that the J-Meter will execute when run. A complete test plan will consist of one or more thread groups, samplers, logic controllers, listeners, timers, assertions and configuration elements.

The ‘thread’ group elements are the beginning of any test plan. Thread group element controls the number of threads J-Meter will use during the test run. We can also control the following via thread group: setting the number of threads, setting the ramp-up time and setting the loop count. The number of threads implies the number of users to the server application, while the ramp-up period defines the time taken by J-Meter to get all the threads running. Loop count identifies the number of times to execute the test.

After creating the ‘thread’ group, we need to define the number of users, iterations and ramp-up time (or usage time). We can create virtual servers depending on the number of users defined in the thread group and start performing the action based on the parameters defined. Internally J-Meter will record all the results like response code, response time, throughput, latency, etc. It produces the results in the form of graphs, trees and tables.

J-Meter has two types of controllers: Samplers and Logic controllers. Samplers allow the J-Meter to send specific requests to a server, while Logic controllers control the order of processing of samplers in a thread. They can change the order of requests coming from any of their child elements. Listeners are then used to view the results of samplers in the form of reporting tables, graphs, trees or simple text in some log files.

Please remember, always do performance testing by changing one parameter at a time. This way, you’ll be able to monitor response and throughput metrics and correct discrepancies accordingly. The real purpose of load testing is to ensure that the application or site is functional for businesses to deliver real value to their users — so test practically, and think like a real user.

If you’ve any queries or doubts, please feel free to write to hello@mantralabsglobal.com.

About the author: Syed Khalid Hussain is a Software Engineer-QA at Mantra Labs Pvt Ltd. He is a pro at different QA testing methodologies and is integral to the organization’s testing services.

Load Testing on Applications FAQs

What is the purpose of load testing?

Load testing is done to ensure that the application is capable of withstanding the load created by the intended number of users (web traffic).

Which tool is used for load testing?

There are open source and commercial tools available for load testing. 
Open Source Tools are — Open STA Diesel Test, TestMaker, Grinder, LoadSim, J-Meter, Rubis. Commercial testing tools are — LoadRunner, Silk Performer, Qengine, Empirix e-Load.

How load testing is done?

Load testing is done using test scripts, monitoring and analyzing test results and developing automated test scenarios.

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Smart Machines & Smarter Humans: AI in the Manufacturing Industry

We have all witnessed Industrial Revolutions reshape manufacturing, not just once, but multiple times throughout history. Yet perhaps “revolution” isn’t quite the right word. These were transitions, careful orchestrations of human adaptation, and technological advancement. From hand production to machine tools, from steam power to assembly lines, each transition proved something remarkable: as machines evolved, human capabilities expanded rather than diminished.

Take the First Industrial Revolution, where the shift from manual production to machinery didn’t replace craftsmen, it transformed them into skilled machine operators. The steam engine didn’t eliminate jobs; it created entirely new categories of work. When chemical manufacturing processes emerged, they didn’t displace workers; they birthed manufacturing job roles. With each advancement, the workforce didn’t shrink—it evolved, adapted, and ultimately thrived.

Today, we’re witnessing another manufacturing transformation on factory floors worldwide. But unlike the mechanical transformations of the past, this one is digital, driven by artificial intelligence(AI) working alongside human expertise. Just as our predecessors didn’t simply survive the mechanical revolution but mastered it, today’s workforce isn’t being replaced by AI in manufacturing,  they’re becoming AI conductors, orchestrating a symphony of smart machines, industrial IoT (IIoT), and intelligent automation that amplify human productivity in ways the steam engine’s inventors could never have imagined.

Let’s explore how this new breed of human-AI collaboration is reshaping manufacturing, making work not just smarter, but fundamentally more human. 

Tools and Techniques Enhancing Workforce Productivity

1. Augmented Reality: Bringing Instructions to Life

AI-powered augmented reality (AR) is revolutionizing assembly lines, equipment, and maintenance on factory floors. Imagine a technician troubleshooting complex machinery while wearing AR glasses that overlay real-time instructions. Microsoft HoloLens merges physical environments with AI-driven digital overlays, providing immersive step-by-step guidance. Meanwhile, PTC Vuforia’s AR solutions offer comprehensive real-time guidance and expert support by visualizing machine components and manufacturing processes. Ford’s AI-driven AR applications of HoloLens have cut design errors and improved assembly efficiency, making smart manufacturing more precise and faster.

2. Vision-Based Quality Control: Flawless Production Lines

Identifying minute defects on fast-moving production lines is nearly impossible for the human eye, but AI-driven computer vision systems are revolutionizing quality control in manufacturing. Landing AI customizes AI defect detection models to identify irregularities unique to a factory’s production environment, while Cognex’s high-speed image recognition solutions achieve up to 99.9% defect detection accuracy. With these AI-powered quality control tools, manufacturers have reduced inspection time by 70%, improving the overall product quality without halting production lines.

3. Digital Twins: Simulating the Factory in Real Time

Digital twins—virtual replicas of physical assets are transforming real-time monitoring and operational efficiency. Siemens MindSphere provides a cloud-based AI platform that connects factory equipment for real-time data analytics and actionable insights. GE Digital’s Predix enables predictive maintenance by simulating different scenarios to identify potential failures before they happen. By leveraging AI-driven digital twins, industries have reported a 20% reduction in downtime, with the global digital twin market projected to grow at a CAGR of 61.3% by 2028

4. Human-Machine Interfaces: Intuitive Control Panels

Traditional control panels are being replaced by intuitive AI-powered human-machine interfaces (HMIs) which simplify machine operations and predictive maintenance. Rockwell Automation’s FactoryTalk uses AI analytics to provide real-time performance analytics, allowing operators to anticipate machine malfunctions and optimize operations. Schneider Electric’s EcoStruxure incorporates predictive analytics to simplify maintenance schedules and improve decision-making.

5. Generative AI: Crafting Smarter Factory Layouts

Generative AI is transforming factory layout planning by turning it into a data-driven process. Autodesk Fusion 360 Generative Design evaluates thousands of layout configurations to determine the best possible arrangement based on production constraints. This allows manufacturers to visualize and select the most efficient setup, which has led to a 40% improvement in space utilization and a 25% reduction in material waste. By simulating layouts, manufacturers can boost productivity, efficiency and worker safety.

6. Wearable AI Devices: Hands-Free Assistance

Wearable AI devices are becoming essential tools for enhancing worker safety and efficiency on the factory floor. DAQRI smart helmets provide workers with real-time information and alerts, while RealWear HMT-1 offers voice-controlled access to data and maintenance instructions. These AI-integrated wearable devices are transforming the way workers interact with machinery, boosting productivity by 20% and reducing machine downtime by 25%.

7. Conversational AI: Simplifying Operations with Voice Commands

Conversational AI is simplifying factory operations with natural language processing (NLP), allowing workers to request updates, check machine status, and adjust schedules using voice commands. IBM Watson Assistant and AWS AI services make these interactions seamless by providing real-time insights. Factories have seen a reduction in response time for operational queries thanks to these tools, with IBM Watson helping streamline machine monitoring and decision-making processes.

Conclusion: The Future of Manufacturing Is Here

Every industrial revolution has sparked the same fear, machines will take over. But history tells a different story. With every technological leap, humans haven’t been replaced; they’ve adapted, evolved, and found new ways to work smarter. AI is no different. It’s not here to take over; it’s here to assist, making factories faster, safer, and more productive than ever.

From AR-powered guidance to AI-driven quality control, the factory floor is no longer just about machinery, it’s about collaboration between human expertise and intelligent systems. And at Mantra Labs, we’re diving deep into this transformation, helping businesses unlock the true potential of AI in manufacturing.

Want to see how AI-powered Augmented Reality is revolutionizing the manufacturing industry? Stay tuned for our next blog, where we’ll explore how AI in AR is reshaping assembly, troubleshooting, and worker training—one digital overlay at a time.

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