Excell your software with top 8 performance testing tools for 2023.
Performance testing software plays a crucial role in the software release cycle, ensuring that products and web applications not only function properly but also deliver optimal performance under real load conditions. This way, you can ensure that the software can withstand the demands of hands-on usage.
To simplify the software performance testing process, the market offers a range of tools. They empower you to create and automate realistic scenarios across thousands of concurrent users, giving you confidence in the product’s readiness for deployment. Let’s talk about these tools and the distinctive features that contribute to their prominence.
Why Is Software Performance Critical?
As little as a decade ago, the focus was primarily on ensuring the software’s correctness and freedom from defects. However, user expectations have evolved significantly. End users now demand software that not only functions flawlessly but also deliver prompt responses to their actions.
The consequences of software failure in terms of performance can be far-reaching; for example, annoyed customers may choose to switch to a competitor’s better-performing product, resulting in a loss of business and revenue.
In critical systems, such as healthcare software, a slow response from the software can lead to incorrect decisions being made, potentially affecting the outcome of life-saving operations and patients’ well-being. In the finance and banking sectors, delayed or inaccurate processing of transactions can have severe implications: financial losses, customer dissatisfaction, legal and reputational risks.
Paying attention to software performance does three key things:
- Enhances customer satisfaction.
- Leads to improved operational efficiency.
- Helps identify and rectify bottlenecks, inefficiencies, and vulnerabilities.
User experience and satisfaction are key differentiators of your enterprise success. Failing to recognize the impact that performance has on customer retention and acquisition can have disastrous consequences for any IT strategy.
How to Measure Software Performance
By evaluating these performance metrics and applying methods, you’ll gain meaningful insights into how your software performs under different conditions and identify areas for improvement:
Response time encompasses the duration required to transmit the initial request, process it, and subsequently transmit the response back to the originating user.
To measure it correctly, it’s important to define specific user actions or transactions that represent typical interactions with the software. This could include submitting a form, retrieving data, or executing a specific operation. Also, the measurement process requires establishing response time thresholds or targets based on user expectations and industry standards.
Throughput can be measured in terms of transactions per second (TPS) or requests per second (RPS). It provides insights into the software’s capacity to process a certain volume of work and indicates its ability to handle user load efficiently. Higher throughput values indicate better performance and a greater capacity to handle larger workloads.
CPU usage indicates the percentage of CPU capacity being utilized by the software at any given time.
High CPU usage is a sign of inefficient algorithms, resource-intensive processes, or excessive computation, which leads to degraded performance, increased response times, and potential system instability. So, the goal is to reduce CPU usage, with special focus given to resource-intensive processes and tasks.
Memory, also known as RAM, is a critical resource that stores data and instructions that are actively used by the software during runtime. To measure memory usage, you’ll need to track various metrics: total memory consumption, peak memory usage, and memory leaks. And if you find inefficiencies, you can use memory management techniques like object pooling and caching to reuse memory resources and reduce memory allocation overhead.
Network latency refers to the delay or lag that occurs when data packets travel from one point to another in a network infrastructure, which directly affects the speed and responsiveness of digital interactions. Excessive delays can frustrate users and impact productivity, so ensuring efficient data transmission is of the utmost importance.
Measuring disk, I/O involves monitoring the rate at which data is transferred between the software and the disk; it includes such metrics as read and write speeds, throughput, and latency. High disk I/O performance is crucial for applications that heavily rely on data storage and retrieval, such as databases, file systems, and data-intensive applications.
Error rate represents the frequency or percentage of errors or failures. These can range from minor issues like validation errors or user interface glitches to more severe failures that cause the software to crash or produce incorrect results.
Monitoring the error rate over time provides insights into the robustness of your software, as well as highlights recurring patterns, common error scenarios, and areas of improvement.
To measure concurrent user capacity, software test tools simulate multiple users accessing the software simultaneously and monitor the system’s response. The goal is to determine the maximum number of concurrent users that can be supported without compromising performance, response time, or system stability. This metric is crucial for applications with high user traffic.
Load testing tools focus on evaluating the behavior of a software application under expected or anticipated load conditions. During load testing, performance testing tools generate a significant number of virtual users or transactions to mimic bigger loads. This helps determine if the system can scale, handle peak usage periods, and meet user expectations.
Profiling gathers data about the execution of the code, such as function calls, execution time, memory usage, and resource utilization. By profiling the code, developers identify performance bottlenecks, hotspots, or resource-intensive operations that may be impacting the overall performance of the application.
Top Software Performance Testing Tools: Overview
Dive into an overview of some of the top software performance testing tools available today. Each offers comprehensive features and functionalities to measure, analyze, and optimize the performance of your software application.
Apache JMeter is a versatile tool widely used by industry giants like Google, Microsoft, and Facebook. It offers web-based instrumentation and the ability to load and performance test different applications and protocols, including HTTP, HTTPS, SOAP, databases via JDBC, LDAP, JMS, and more.
JMeter is built on a 100% Java-based architecture, making it easy to deploy and use in diverse testing environments. Its multi-threading capability allows concurrent sampling, and it provides dynamic HTML reports for easy analysis.
LoadRunner is a load and performance testing tool with support for over 50 protocols and technologies, including MQTT, HTML5, WebSockets, RDP, Citrix, Java, .NET, Oracle, and SAP.
One of LoadRunner’s key features is its engine with load testing scripts, which allows users to easily create, record, correlate, replay, and enhance scripts. Its built-in integrations with various tools, such as IDEs, CI/CD systems, open-source test automation frameworks, monitoring tools, and source code management tools, enhance the overall testing process and facilitate seamless integration within existing workflows.
Gatling is an open-source load testing tool that is built on technologies like Scala, Akka, and Netty, which provide excellent performance and scalability. This makes Gatling suitable for testing applications with a large user base or high-volume transaction systems.
It offers seamless integration with Maven and Gradle builders, making it easy to incorporate into CI/CD pipelines. Gatling also provides a built-in assertions API, enabling the execution of functional tests alongside performance testing.
Apache Bench (ab)
Apache Bench (ab) is an open-source software designed for load testing HTTP or HTTPS web servers. It has a simple command-line interface and is suitable for basic load testing scenarios, with essential functionalities to assess response time, throughput, and concurrency handling. Being cross-platform, it can be used on Linux/Unix and Windows servers.
However, it’s a single-threaded program, limiting its scalability and multi-core processor utilization.
Locust is a Python-based tool that determines breaking points in terms of performance, security, and application load management. Thanks to the Python-based approach, the tool makes it easier to create HTTP requests and write concise scripts, lowering the entry threshold for junior engineers. It allows users to leverage their regular IDE for test development and take advantage of version control systems. And with its event-based architecture, it supports thousands of concurrent users without relying on callbacks.
Locust provides a user-friendly interface that presents test results in a dashboard format.
BlazeMeter is a tool that is fully compatible with Apache JMeter. One notable feature is the ability to create a personalized virtual software QA laboratory, which allows users to connect to up to eight geolocations and simulate real-world conditions across different browsers.
Also, with BlazeMeter, users can create load tests with any number of test steps, have a single platform with shared visibility, and access a repository where all tests are stored, making it easier to reuse and maintain test scripts, configurations, and data sets.
New Relic is an observability platform, with one of the most notable offerings being New Relic Mobile, specifically designed for mobile developers to analyze and optimize their app’s performance. The solution stands out for its ability to correlate performance issues across the entire stack, making it easier to identify and resolve bottlenecks.
What also sets New Relic apart is its AI assistance, which offers valuable insights, recommendations, and automated support throughout the performance testing and optimization process.
Dynatrace provides end-to-end infrastructure observability for modern multi-cloud environments. It offers an advanced AI causation engine, Davis, and complete automation capabilities. Among other strengths, there are integrations with popular test automation frameworks and load generation tools, including JMeter, LoadRunner, Neotys, and more.
Notably, Dynatrace is trusted by a range of prominent organizations, such as Dell Technologies, Air Canada, BT, Virgin Money, IP Australia Government, and ASICS.
Excell Your Software Performance
Measurement of product performance and load parameters is one of the 8 possible dimesions of software quality.
With Intetics Software Product Excellence, you can undergo a thorough audit and assessment of your product, thereby evaluating technical debt, measuring product efficiency, and ensuring compliance with industry standards. Combining deep software engineering knowledge with a focus on business objectives across various industries, the service also helps implement the right software processes within your enterprise.
The approach is based on 6K+ hours of original research and utilizes a unique system of metrics and measurements, incorporating ISO standards and the company expertise. The results are transparent and easily understandable by everyone on your team—from top management to technical specialists. It benefits investors assessing the value of a product, users concerned about data security and usability, clients evaluating product functionality, developers seeking code improvement, and managers tracking progress and comparing against KPIs.
The proprietary approach helps identify code-related issues and performance bottlenecks, as well as emphasize that software quality is not solely dependent on technical aspects. So, this comprehensive evaluation helps organizations deliver high-value, user-centric, and commercially successful software solutions.
Software performance testing tools and can help you deliver robust and high-performing software solutions. Here are some of the key points:
- Performance testing tools are essential for evaluating and optimizing software performance.
- They enable the measurement and evaluation of various performance metrics, such as response time, throughput, CPU usage, memory usage, network latency, disk I/O, error rate, concurrent user capacity, and more.
- Choosing the right performance testing tool depends on specific requirements and use cases.
- Look for tools that provide a balance between functionality, ease of use, and affordability, ensuring you get the most value for your investment.
- Ensure that your software not only performs well technically but also contributes to achieving your desired business outcomes and delivering value to your enterprise and stakeholders.
Our comprehensive software evaluation methodology directly addresses all the key points mentioned above. This holistic approach ensures that no stone is left unturned in uncovering potential issues and delivering products that meet and exceed your technical and business requirements. Explore Software Product Excellence for yourself. Reach out today.
What Are the 9 Types of Performance Testing?
The nine types of performance testing are as follows:
- Load testing: the behavior of a system under normal and anticipated peak load conditions
- Stress testing: the system’s performance beyond its normal capacity, pushing it to its limits to identify potential weaknesses or failures
- Spike testing: sudden and significant increases in user load to evaluate how the system handles abrupt spikes in traffic
- Endurance testing: system performance over an extended period
- Volume testing: performance under a large amount of data
- Scalability testing: the ability to scale up or down based on the changing load
- Reliability testing: the system’s ability to consistently perform a required function under specified conditions for a prolonged period
- Compatibility testing: performance across different hardware, software, operating systems, browsers, and network environments
- Isolation testing: isolating specific components or modules of the system to evaluate their individual performance
What Are the 3 Key Criteria for Performance Testing?
There are many criteria for performance testing, but if we had to pick just three, it would be:
- Response time: the time taken by a system to respond to a user’s action or request. The lower the response time, the more efficient and responsive the system is considered to be.
- Throughput: the number of transactions or requests that a system can handle within a given time. Higher throughput signifies better system performance and scalability.
- Stability: the ability of a system to maintain consistent performance over an extended period or under varying conditions. This helps make sure that the enterprise’s systems can handle continuous operation without experiencing significant performance issues or failures.
What Are the Different Types of Performance?
Here are a few examples of the different types of performance that can be evaluated and optimized based on specific requirements and objectives:
- System performance
- Application performance
- Web performance
- Database performance
- Network performance
- Mobile performance
- Load performance
- Stress performance
- Security performance
What Is Performance Testing in QA?
Performance testing in QA refers to a crucial testing process that focuses on evaluating how well a software application performs under different circumstances. By helping QA teams optimize the application’s performance, the process ultimately aims to deliver a high-performing and reliable software application that meets user expectations.