Best Performance Testing Software for 2020

Performance Testing Software is a software testing process that is utilized for testing the constancy, dependability, scalability, resource handling, speed, and reaction time of a software application under a specific workload. The main objective of performance testing is to identify and lessen the performance issues in the software application. It is a subcategory of performance engineering and known as “Perf Testing”.

The main aim of Performance Testing is verifying a software program's

Speed - Decides if the application responds quickly

Scalability - Determines the capacity of software application to handle maximum user load

Stability - Determines whether the application is stable enough under differing loads

The best performance testing software makes a real-world load model, based on mixture of end-to-end user experience. The software can script vibrant journeys that create options from page content as real users would – revealing all blockages and performance issues. Journey Load testing is done from the user’s perspective creating a growing number of concurrent users holding out multi-page transactions.

Load testing method of performance testing software tests products, websites, and servers by adding up load pressure and gauging the response. Many Developers use this software as a part of a greater software testing program to authenticate developed products, websites, and servers can manage the various stresses of day-to-day use, as well as excessive-impact uses (e.g., enterprise application use, holiday shopping, etc.). Load testing devices work properly with load balancing software, mostly being integrated to stress spots of impact during testing and then handle those stresses as they occur.

Market Overview

The worldwide Performance Testing Software market size is projected to grow at a CAGR of 9.9% from USD 1.3 billion in 2019 to USD 2.0 billion by the end of 2024. The main driving factors for the market include the growing demand of organizations to improve the user experience for contending in the current global market and creating brand awareness, thus helping organizations to accept new approaches for releasing their websites or mobile apps to the public fast. To stay with the continuous delivery model, software developers are deploying performance testing to increase user insights and ensure software quality as per the user at an extremely quickening pace and in a cost-effective way.

Performance testing is the technique of verifying the speed, stability, and reaction of a computer, network, software application, or device under a workload.

The best Performance Testing Software consists of quantitative tests done in a lab or in the production environment in limited circumstances. Standard factors consist of processing speed, workload efficiency, data transfer rate, network bandwidth and throughput, and reliability. For example, a business firm can measure the reaction time of a software application when a user requests an action or the number of millions of instructions per second (MIPS) at which a mainframe functions.

The MicroQuadrant matrix by 360Quadrants offers exhaustive information about the best Performance Testing Software vendors. It offers a comprehensive summary explaining the inferences and analysis of how well each vendor performs within the predefined MicroQuadrant standards. The vendor evaluations are classified on 2 broad factors, i.e. Product Maturity and Company Maturity. The vendors have been evaluated based on parameters studied under Product Maturity and Company maturity. Parameters included under product maturity are key product offerings & services, features, product innovation, deployment model, pricing options, and support services. The parameters included under the company maturity are regional presence, industry verticals catered, partner ecosystem, and inorganic strategies like mergers & acquisitions, and joint ventures.

Visionary Leaders provide a strong product portfolio to their clients. Eggplant, LoadUI Pro, NeoLoad, RedLine13, and UbikLoadPack have been identified as VIsionary Leaders in the Performance Testing Software space. These companies majorly invest in R&D to launch pioneering products in the best Performance Testing Software. These companies have a strong global presence and adopted several organic and inorganic strategies to achieve continuous and progressive growth in the best Performance Testing Software market.

Dynamic Differentiators have a wide network of channel partners and resellers to improve the deployment of their solutions. AppAchhi has bee identified as a Dynamic Differentiator in the best Permonance Testing Software space. By implementing the strong organic and inorganic business strategies, dynamic differentiators have been continuously generating positive revenue growth in the best Performance Testing Software.

Innovators provide innovative solutions and they also have an extensive range of product offerings. BlazeMeter Performance Testing and  HealthStream Learning Center have been identified as Innovators in the best Performance Testing Software space. By offering niche and customized software requirements to the clients, the innovators have been at the forefront in deploying their solutions.

The emerging vendors in the best Performance Testing Software space offer customized and focused performance testing software to their clients. Appium has been identified as an Emerging player in the Performance Testing Software space. Emerging players always adopt new ways of working to drive business outcomes in the market.

What are the key market drivers of Performance Testing Software?

Performance testing software has developed with the transformation of technology. There is no uncertainty that the industry has developed a great deal over recent times. There is also a lot of shift going on in the application types and methods that are to be tested. Additionally, the business application testing process has also shifted due to the increasing number of IoT devices. Therefore, it makes connected devices more complicated as they require to be tested for that purpose. So, with the shifting testing environments, the tests change their method of testing too. There is an extensive range of the best performance testing software services available that cater to different types of industries.

1. Developers working more on performance tests

IT departments of all companies follow the method that tells ‘test early, test often’. Gone are the outdated methods of testing, where only testing experts were responsible for software quality assurance. All stakeholders work as a team to produce the best efforts to ensure software quality. Additionally, to keep the expenses low as code moves quicker to the right side of the deployment process, testing earlier becomes compulsory. This must be done through the left-side developer code creation is on-going. Hence, it also results in shifting all the accountability of the developers to other team members.

2. Improved use of AI to fully automate testing processes

In the existing technological era, organizations are unable to go forward without AI (Artificial Intelligence). Things are very different in the ever-changing and increasing environment. Testing experts must have dynamic solutions for working in distinct environments. Therefore, the solution is to apply the use of AI in the current testing space.

3. Increased reliance on defining test processes

The best solution is to achieve all design test scripts that can be utilized to build software apps that can be used as needed in different situations. Like the way, designers handle infrastructure a code, the virtual environments necessary. Hence, test designers method test processes as code to create functional, combined, and performance tests. This is the way performance testing software services can work best for organizations making their way forward.

4. Using more functional testing assets

Functional testing solutions are a crucial part of software release. Some believe that it is the adhesive that binds a developer’s actions to the broader facet of the development process. For example, when a unit testing is performed for a developer’s code, it may meet the specifications but fail to meet the due diligence with respect to functional testing. With respect to the best performance testing software, shift-left, testing as code, and using more AI down the release processes can work in the greatest interest of businesses.

What are the different types of Performance Testing Software?

1. Load testing – Load Testing enables developers to understand the behavior of a system under a particular load value. In the load testing method, an organization replicates the anticipated number of concurrent users and transactions over a period to confirm anticipated response times and locate blockages. This method of test permits developers to decide the number of users an application or system can manage before that app or system goes live on the market. Moreover, a developer can load test particular capabilities of an application, such as a checkout cart on a webpage. A team can perform load testing as part of a continuous integration (CI) process, in which they instantly test changes to a code base using automation tools.

2. Stress testing - Stress Testing has two sub-categories called soak and spike testing, that places a system under higher-than-anticipated traffic loads so developers can check how well it works above its anticipated capacity limits. Stress tests permit the software team to understand and recognize a workload's scalability. Stress tests apply strain on hardware devices, like CPUs, memory and hard disks, and solid-state drives, to determine the probable breaking point of an application on these partial resources. System strain may cause slow data exchanges, data corruption, security issues, and memory shortages. Stress tests can also explain how long KPIs take to come back to normal operational levels after an incident. Stress tests can happen before or after a system goes live. A type of production-environment stress test is called chaos engineering and there are dedicated tools for it. An organization might also execute a stress test before a certain major event, such as Black Friday on an e-commerce software application, estimating the expected load using the same tools as load tests.

3. Soak testing - Also known as endurance testing, replicates a steady growth of end-users over time to test systems' long-term sustainability. Throughout the test, the test engineer supervises KPIs, such as memory usage, and tests for failures, such as memory deficiencies. Soak tests also evaluate throughput and response times after continuous use to check if these metrics are reliable with their status at the beginning of a test.

4. Spike testing - A subset of stress testing, evaluates the performance of a system under a rapid and substantial increase of simulated end users. Spike tests let you determine whether a system can handle an unexpected, extreme workload increase over a small period, frequently. Like stress tests, an IT team usually performs spike tests prior to a large event in which a system will probably undergo greater than normal traffic volumes.

5. Endurance testing - Endurance testing also called as soak testing is an assessment of how well software performs with a usual workload over a prolonged volume of time. The aim of this testing is to validate for system glitches like memory leaks. (A memory leak usually happens when a system is unable to release discarded memory. The memory leak can damage system performance or cause it to fail.)

6. Scalability testing - This testing is used to decide whether the software can effectively manage increasing workloads. This can be verified by slowly adding to the user load or data volume while supervising system performance. Additionally, the workload may remain at the same level while resources such as CPUs and memory are altered.

7. Volume testing - Volume testing verifies how efficiently the software works with a large, projected amount of data. It is also called as flood testing because the test floods the system with data.

What is the Importance of Performance Testing Software?

Features and Functionality backed by a performance testing software system is not the only issue. A software application's performance such as its response time, resource usage, scalability, and reliability, does matter. The main aim of Performance Testing is not just to find bugs but to remove performance blockages.

Performance Testing software is done to give stakeholders essential information regarding their software application about speed, scalability, and stability. More significantly, Performance Testing reveals what needs to be enhanced before the product goes live. Without Performance Testing, the software may possibly suffer from issues like running slow while many users use it simultaneously, discrepancies across different operating systems, and weak usability.

Performance testing software will decide if their software complies speed, scalability, and stability requirements under anticipated workloads. Applications sent live to market with weak performance metrics due to missing or poor performance testing software are expected to gain a bad reputation and unable to meet projected sales goals.

Additionally, mission-crucial applications such as space release programs or life-saving medical devices should be performance tested to make sure that they run for a long period without discrepancies.

As per Dunn & Bradstreet, nearly 59% of Fortune 500 companies face an estimated 1.6 hours of downtime each week. Assuming the average Fortune 500 company with at least 10,000 employees is paying USD 56 per hour, the labor element of downtime can cost for such an organization around USD 896,000 weekly, converting into more than USD 46 million per year.

For instance, just a 5-minute downtime of Google.com (19-Aug-13) is expected to cost the search giant around USD 545,000.

It is projected that companies lost sales worth around USD 1100 per second due to a recent Amazon Web Service downtime.

Therefore, performance testing software is essentially important.

What are the Common Challenges Observed in Performance Testing Software?

At that time of performance testing of software, developers seek for performance symptoms and issues. 1. Speed issues - Slow reactions and prolonged load times for instance — often are observed and addressed. But there are other performance problems that can be noted:

2. Bottlenecking - This issue occurs mostly when data flow is disrupted or terminated as there is not enough capacity to manage the workload.

3. Poor scalability - If the software is unable to handle the desired number of concurrent tasks, outcomes could be delayed, mistakes could rise, or other unforeseen behavior could occur that influences:

Disk usage

CPU usage

Memory leaks

OS restrictions

Bad network configuration

4. Software configuration issues – This type of issues mostly occur due to settings are not set at a sufficient level to manage the workload.

5. Insufficient hardware resources - Performance testing software may disclose physical memory restrictions or low-performing CPUs.

What are the major Performance Testing Software Steps?

1. Identify the testing environment – The first step in testing the performance of the software is to identify the hardware, software, network structures, and tools available that permits the testing team to design the test and recognize performance testing software challenges soon. Performance testing environment options comprise:

The subgroup of the production system with fewer servers of lesser specification

The subset of production system with fewer servers of a similar specification

Model of productions system

Actual production system

2. Identify performance metrics – Apart from identifying metrics like reaction time, throughput, and limitations, recognize what are the success measures for performance testing software.

3. Plan and design performance tests - Recognize performance test scenarios that consider user variability, target metrics, and test data. This will help you to create one or two models.

4. Configure the test environment - Organize the elements of the test environment and instruments required to supervise resources.

5. Execute your test design - Develop the tests.

6. Execute tests – Apart from running the performance tests, monitor and acquire the data generated.

7. Analyze, report, retest - Analyze and evaluate the data and share the findings. Run the performance tests once again using similar parameters and separate parameters.

What Performance Testing Metrics are Measured?

Metrics are necessary to understand the quality and efficiency of performance testing software. Improvements cannot be done unless there are measurements. Here are two definitions that need to be clarified:

Measurements — It is the data being collected such as the seconds it takes to react to a request.

Metrics — An estimate that uses measurements to describe the quality of results such as average reaction time (total reaction time/requests).

There are several ways to measure speed, stability, and scalability but every round of performance testing cannot be supposed to use all of them. Between the metrics used in performance testing, the following are widely been used:

Response time- Overall time to send a request and obtain a response.

Wait time- It is also referred to as average latency, this explains developers how much time it takes to obtain the first byte after a request is sent.

Average load time- It is an average time required to take to deliver each request is the key indicator of quality from a user’s point of view.

Peak response time- This is the calculation of the longest amount of time it requires to fulfill a request. A peak response time that is considerably longer than average may suggest an abnormality that will create difficulties.

Error Rate- This measurement is a percentage of requests causing in errors compared to all requests. These errors mostly occur when the load exceeds its capacity.

Concurrent users- This the most common gauge of load — no. of active users at any point. Also called as load size.

Requests per second- No. of requests that are handled.

Transactions passed/failed- A calculation of the overall numbers of successful or unsuccessful requests.

Throughput- Calculated by kilobytes per second, throughput indicates the amount of bandwidth used during the test.

CPU utilization- It is the amount of time CPU needs to process requests.

Memory utilization- It is the amount of memory requires to process the request.

What are the key benefits of Performance testing Software?

1.  Validates the functional features – The use of performance testing helps in effectively validate the software features. This will additionally benefit the businesses to make effective decisions upon the software setup.

2. Monitors the speed, accuracy, and stability - The approach helps you to measure the app performance such as speed, accuracy, and stability. Performance testing practice also helps in monitoring these key features in critical situations.

3. Improves Customer Satisfaction - The precise use of performance testing enables you to measure the software’s response for the customers. This will help you to identify and sort out the performance errors and ultimately helps to increase customer satisfaction.

4. Issues are identified quickly - The approach of performance testing enables the development teams to find and fix the errors before the app goes live. This will help you to prevent situations of app failure and efficiently improves the scope of software as the errors are resolved at the earliest.

5. Improves load capacity – The use of Performance Testing Software effectively improves the load capacity and app improvisation. This effectively enables the businesses to raise their user volumes for the application.

To accomplish the best result with the best performance testing software, it is crucial to select a tool that is effective to meet the needs. Hence, enterprises must select the best tool as per their business needs.

What is the major difference between Performance Testing and Performance Engineering?

It is important to understand the key difference between Performance Testing and Performance Engineering. Below are few details mentioned about the differences.

Performance Testing is a process that considers testing and reporting the present performance of a software application under a variety of parameters.

Performance engineering is the process by which software application is tested and modified with the aim of realizing the needed performance. This process targets to enhance the most critical application performance feature i.e. user experience.

Generally, testing and tuning have been noticeably separate and frequently competing areas. In the recent past, however, some pockets of testing and developing teams have collaborated individually to create tuning teams. Since these teams have come together with significant success, the idea of combining performance testing with performance tuning has captured on, and now it is known as performance engineering.

Conclusion

In Software Engineering, Performance testing is essential before marketing any software product. It confirms customer satisfaction & safeguards an investor's investment against product failure. Expenses of performance testing are generally greater than made up for with enhanced customer satisfaction, retention, and loyalty.

What are the current trends to watch for in the Performance Testing Software market?

1. Agile and DevOps – Many businesses have adopted Agile as a response to rapidly changing needs and DevOps as a response to the need for speed.

DevOps includes practices, processes, rules, and tools that support to integrate development and operation activities to decrease the time required from development to operations. DevOps has been an extensively adopted solution for businesses that are seeking ways to reduce the software lifecycles from development to delivery and operation.

The acceptance of both Agile and DevOps facilitates the teams to develop and deliver quality software quicker, which in turn is also called “Quality of Speed”. This acceptance has earned much interest over the past five years and continues to strengthen in the coming years too.

2. Test Automation - To implement DevOps practices efficiently, software teams cannot overlook test automation because it is an important element of the DevOps process.

The teams must find opportunities to switch manual testing with automated testing. As test automation is an essential bottleneck of DevOps, at least, most regression testing must be automated.

Due to the wide acceptance of DevOps and the truth that test automation is underutilized, with less than 20% of testing is automated, there is a huge scope to improve the acceptance of test automation in business organizations. More advanced techniques and tools should arise to permit better use of test automation in projects.

Current popular automation tools like Katalon, Selenium, and TestComplete remain to grow with new features that make automation much simpler and very effective too.

3. API and Services Test Automation - Dissociating the client and server is an existing trend in creating both Web and mobile applications.

API and services are reused in more than one application or element. These changes, in turn, need the teams to test API and services separate from the application using them.

When API and services are utilized throughout client applications and components, testing them is highly effective and efficient than testing only the client. The current trend is that the demand for API and services test automation persists to grow, possibly outperforming that of the functionality utilized by the end-users on user interfaces.

Owning the right tool, process, and solution for API automation test is more important than ever. Thus, it is worth your effort in understanding the best API Testing Tools for your testing projects.

4. Artificial Intelligence for Testing - Though applying the artificial intelligence and machine learning (AI/ML) methods to meet the challenges in software testing is not new in the software research fraternity, the current advances in AI/ML with a great amount of data accessible offer new opportunities to apply AI/ML in testing.

Though, the application of AI/ML in testing is still in the initial stages. Businesses will find methods to improve their testing practices in AI/ML.

AI/ML algorithms are created to create better test cases, test data, test scripts, and reports. Predictive models may help to make decisions about what, where, how, and when to test. Smart and Intelligent analytics and visualization help the teams to identify faults, to know test coverage, areas of high risk, etc.

It is expected to see more applications of AI/ML in tackling problems such as test case prioritization, fault classification, quality prediction, and assignment in the coming years.

5. Mobile Test Automation - The current trend of mobile app development continues to increase as mobile devices are becoming more capable.

To wholly support DevOps, mobile test automation must be a part of DevOps toolchains. But, the current use of mobile test automation is very minimal, partly due to the shortage of methods and tools.

The existing trend of automated testing for mobile app persists to increase. This trend is mainly driven by the demand to shorten time-to-market and more innovative methods and tools for mobile test automation.

The incorporation between cloud-based mobile device labs such as Kobiton and test automation tools like Katalon can support in bringing mobile automation to the next level.

6. Test Environments and Data - The quick growth of the Internet of Things (IoT) implies more software systems are operating in several different environments. This makes a challenge for the testing teams to conform to the right level of test coverage. The shortage of test environments and data is a major challenge while applying to test in agile projects.

It is seen that there is consistent growth in providing and using cloud-based and containerized test environments. The application of AI/ML to produce test data and the growth of data projects are some solutions for the shortage of test data.

7. Integration of Tools and Activities - It is difficult to use any testing tool that cannot be integrated with the other tools for application lifecycle management. Software teams must integrate the tools used for all development phases and activities so that multi-source data can be collected to apply AI/ML approaches efficiently.

For Instance, applying AI/ML to identify where to focus testing on, requires not only data from the testing phase but also from the requirements, design, and implementation stages.

Apart from the trends of growing transformation toward DevOps, test automation, and AI/ML, it is seen that testing tools allow integration with the other tools and activities in ALM.

Conclusion

These are the major Evolving Software Testing Trends that one should look for in 2020 as we live in the world of exceptionally exponential changes powered by technology and digital transformation.

Business organizations and individuals must be aware of the developments in the industry. Knowing about these trends will give test professionals, organizations, and teams the prospect to remain ahead of the curve.