Emerging Trends in Performance Testing

Creating a visually appealing website with seamless functionality is great, but if it crashes easily or fails to work under higher traffic, it can never be a successful one. Hence, performance testing is a crucial parameter when it comes to software testing. It gives a clear picture of how the website/ application is performing in terms of speed, thereby, offering scope to increase its robustness and reliability.

Performance testing is a rapidly developing field and has witnessed enormous advancements, especially in the recent years. Teams are trying to move to quicker, cheaper, agile, and more accessible methods to improve the performance testing process.

Like the previous years, this year too will witness new trends in performance testing that will enable more responsive development in shorter spans with fewer risks factors. The emerging trends in performance testing are discussed here in detail.

Latest trends in performance testing

The new trends in performance testing are still at a nascent phase and will make their presence in the market much sooner than we anticipate. Here are some of the popular testing trends that will transform software QA in the near future.

Artificial Intelligence

The use of Artificial Intelligence (AI) in performance testing for websites and apps is not new. AI automation is slowly making its presence as a go-to option for testing and QA teams at every stage of performance testing.

The use of artificial intelligence in performance testing for websites and applications is expected to grow further in the upcoming years and become a significant trend of all time.

Internet Of Things Testing Market

The Internet of Things (IoT) has seen rapid growth in the last few years, and this growth is expected to continue in the future too at a larger scale. This means that there will be millions of devices operating in various unique environments. Testers will face new challenges to ensure that the testing cycle, performance, and security aren’t compromised. To mitigate these risks, testers will have to adopt an IoT-focused approach, leading to the rise of Cloud-based and IoT testing environments.

Cloud-based Testing

Cloud computing services are becoming popular for functional and non-functional software testing. There are a plethora of benefits of using Cloud-based tools for performance testing. Some of them are:

  • High Scalability: With a Cloud-based platform, unlimited users can carry out performance testing simultaneously.
  • Low Cost: It allows on-demand resource provisioning for performance testing for websites and software without the need of building infrastructure, thereby, helping reduce performance testing costs.
  • Supports Production Environment Testing: Generally, traditional, older tools allow performance testing only in the test environment. However, with Cloud-based tools for performance testing, the testing can be carried out in the production environment as well.

Open-source Tools for Performance Testing

Open-source tools promote collaboration by giving testers the ability to view and edit the source code. This leads to the team working efficiently and helps create a better product while reducing the production cycle time. Additionally, they also provide an easy learning platform for new testers. No doubt that open-source performance testing tools have become quite popular in the testing community and will remain an integral part of it.


DevOps is a collaborative approach combining Development (Dev) and IT Operations (Ops). It involves all the stakeholders in the software development process until the product is delivered to the client. DevOps aims to reduce the software development life cycle while delivering high-quality end-products to the client. To accomplish this, DevOps involves a highly interconnected, collaborative, and agile approach. Looking forward, DevOps seems to be the go-to approach for many organizations due to the various benefits it delivers.

Production Testing

Another emerging trend in performance testing is testing the software or website in the production environment. Generally, performance testing is done in the development, staging, and pre-production environments. However, in production testing, the new code changes are tested on live user traffic on the production software itself.

Production testing allows only a small set of users to be exposed to the software. The testing team then carries out performance testing for websites or applications and rolls out new features to check user responses. They can verify whether the software or website works as intended or not. Some of the techniques used for production testing include:

  • A/B testing: Testers can compare two versions/features at the same time to see which one provides a better user experience.
  • Blue-Green deployment: It involves running two production environments that are as identical as possible. It helps reduce downtime and risks as it enables gradual and safe transfer of user traffic from a previous version of the app or software to the new one.
  • Security Testing: Data threats and attacks have increased in the last few years, resulting in tangible and intangible losses for every party involved. Thus, every stakeholder, including businesses, has realized the importance of data safety. Testing teams, too, have prioritized security testing in performance testing to avoid any undesired instances. The threats are expected to only increase as we steadily move to a more interconnected world. That is why software testing teams must become competent to detect and neutralize threats at the earliest.

Behavior-driven development

Behavior-driven development (BDD) is an agile approach that encourages collaboration with shared tools and processes to create a mutual understanding between testers on how the end-product will behave. In BDD, the testing team needs to build test cases based on user behavior and interactions to create a high-quality end-product. BDD is expected to gain further prominence as AI goes mainstream in performance testing.

These are the top emerging trends in performance testing that one should watch out for in the next few years. However, given the unprecedented changes, we might see the addition of these new trends much sooner in the future. Similarly, some of the emerging trends may vanish before they become mainstream due to various challenges in implementing them on a larger scale. Businesses, testers, and individuals will need to keep themselves updated about new developments in the industry to stay ahead of the curve.

Why choose Yethi for performance testing?

Yethi is a niche QA service provider for global banks and financial institutions that offers efficient end-to-end testing. Our flagship, Tenjin, is a codeless test automation platform that can carry out all aspects of functional and non-function testing with nearly 100% accuracy. Tenjin executes high-level performance testing to identify the responsiveness, availability, and scalability of the system. It performs multiple rounds of tests to check the consistency of the system. Our aim is to ensure that your application performs at its best even during increased, load, stress, and volume.

AI in Testing – Different Areas Where AI Supports Testing

Adapting automated algorithm for software testing is a massive trend in the industry, and with advances in AI, it can reach unprecedented heights. The incorporation of AI in software testing can significantly reduce human time by effortlessly performing repetitive tasks. Its reasoning and problem-solving capabilities will help identify bugs easily, thereby, improving the software quality significantly. It also increases the productivity rate and agility while ensuring reduced time, cost, and effort.

Although codeless test automation has proven to improve software quality, it is interesting to note that AI-driven automation can improve the speed like never before and offer greater accuracy. Organizations are in a constant effort to leverage the best out of AI technology and utilize it to transform their test automation practices. Indeed, there are no limits to AI capabilities! Let’s take a closer look at how AI in testing automation is transforming software testing and see how companies worldwide have already adopted this technology to improve their QA processes.

The Role of AI in Software Testing

AI-powered testing is gaining popularity for the immense benefit it offers. The main application of AI in software testing is to increase software automation capabilities, reduce time, save money, ease defect tracking, and use its intelligence to analyze customer behavior and change their operational strategies accordingly.

Artificial Intelligence can be a considerable asset when it comes to automating the process of testing tools. It’s capable of performing tasks beyond human capability and ensures an accurate result which is not less a revolution. AI will also make detailed reports personifying the process to assure a greater outlook on how the team is doing with their transitions from traditional, manual testing to something that is easier for a person to verify within a shorter amount of time.

However, automated tools require a significant amount of initial configuration: testers and architects need to enhance the tool’s functionality and control its performance during the work. Once the organization moves beyond these limitations, it can achieve greater results with AI technology.

How AI can help in software testing

AI in test automation is like having a robot do things for you, checking if your website is up and running or testing the quality of the latest update coming through on your app. This type of software package can help speed up any process that may be crucial for this kind of job.

Test Case Writing

Artificial Intelligence can cut out the background noise of traditional test case writing. With software becoming intelligent by the day, it’s not long before Artificial Intelligence will make an appearance in automated testing to offer new capabilities other than reducing time spent on repetitive and non-value-added work. By implementing AI solutions, organizations will successfully address the current problems surrounding manual testing, namely reducing cost and maximizing productivity with minimal human intervention.

API Test Generation

API testing is a way of evaluating the quality of interactions between different programs. Testing assures that connections to databases, servers, or other protocols are stable, requests are processed correctly, and end-users will get a correct output after a particular interaction.

For this reason, when your app’s developers use multiple tools, automation for testing is vital for ensuring all functionality is checked thoroughly and every request is processed correctly every time.

Artificial Intelligence is excellent at spotting flaws in code and its tests. It can very quickly determine if an API is fully functional or suffering from errors and omissions. It’s AI’s job to analyze if there might be issues, generate test cases to track problematic areas, and interact with each other to avoid conflicting goals.

Visual Validation Testing

Automating visual testing is not an easy task due to the countless number of possible test scenarios that can cause bugs. In addition, a tester must be able to put themselves into the mindset of the end-user and see the UI with their eyes to evaluate all possible issues. Given this challenge, it’s understandable why nuances accompany visual switching tests over to automated solutions – it’s difficult for testers to specify every necessary detail, so there is no room for bugs.


Artificial Intelligence offers software testers and developers the ability to extrapolate users’ needs by analyzing historical usage data. This functionality allows testers to deliver better services that meet quickly evolving user expectations. As each user is analyzed, the AI learns how they interact with the system and begins to generate more accurate predictions based on previous behavior, leading the testers closer to acquiring full data confirmation of their results.

The Current Trends of AI in Software Testing

The current trend in testing tools and frameworks is to use automated systems that can discover, create model, enable test generation, detect failures, and even fix the issues they identify. This newfound innovation using intelligent test bots to automate tasks such as application discovery, modeling, and failure detection is helping developers get their jobs done more quickly and accurately than ever before. Furthermore, by leveraging machine learning techniques to design test bot behavior, there’s no stopping the team from succeeding.

The Future Trends of AI in Software Testing

One of the hottest trends that has bloomed over the past decade is artificial intelligence (AI) to optimize already existing tools and technologies. For example, web and mobile applications can now be functionally tested using AI, while visual testing of user interfaces is possible thanks to technology like machine learning. Beyond this, we can expect to see entire technology stacks for automated testing replaced by AI in future years too!


Fortunately, many AI-powered platforms offering a wide range of testing solutions have already been launched and are used by millions of people. Artificial Intelligence is becoming increasingly popular among software testers and businesses for optimizing testing processes. As a result, the industry is starting to reap the benefits that scientifically sophisticated and specific product testing can deliver, such as gains in efficiency, intelligent decision making, self-healing capabilities, and faster test execution times.

Yethi’s AI-driven test automation 

Yethi, market leaders in software testing for banks and financial institutions, will soon launch a powerful AI-enabled test automation solution to keep up with the AI-powered test automation trend. The new AI-technology enabled testing models is designed to carry out predictive analysis and optimize the results based on the customer’s previous requirements. The innovative product includes  smart selection of test cases to train the model and save the changes on real-time. The AI-model for test automation has already started yielding results right from its development stage with increased productivity, speed, scalability, security, and stability, while reducing time, cost, and effort.

Uncertainty and innovation at speed in banking

The banking and financial landscape is undergoing a huge transformation to support the continually changing customer behavior, which is determined by the changing uncertainty. Uncertain situations, like the COVID-19 pandemic, have driven banks and financial companies to innovate in order to succeed. Organizations are leveraging the power of technology to cope up with these times of changing uncertainty. 

The rapidly increasing COVID-19 situation has led the banking and financial sector to strengthen its digital transformation journey. Companies are redefining their operating models with more agile and practical systems to support the complex digital workflows and offer a smooth user experience. They are utilizing an innovative approach to establish new business models, which will, in turn, require advanced test automation practices to identify any associated discrepancies or risks. Test automation is an integral part of any innovation; it is much more than a mere QA check, it offers huge scope to improve the product quality and establish a unique brand. 

How uncertainty encourages banks/financial companies to innovate 

Like the 2008 financial crisis or the ongoing COVID-19 pandemic, uncertain situations have always had an immense impact on the banking and the financial sector. Being the core part of the global economic growth, the BFSI sector should strengthen and prepare itself for any kind of market, economic, natural, or political uncertainties. Only organizations that thrive in these situations and offer an unparalleled user experience even under the worst scenario can succeed, while others lose their existence. 

Innovation is the only key to sustain and succeed through uncertainty. It allows organizations to come up with new techniques, tools, and models that will be most appropriate to the new scenario. For example, the COVID-19 pandemic had restricted people visiting banks, which led banks/financial institutions to be a part of the digital revolution. Organizations started innovating their systems/processes to make it digitally available for their customers. This trend of digital innovation in the banking sector created an array of opportunities to improve all their systems. Hence, any uncertain situation can be easily converted to a favourable one by utilizing the power of innovation. 

Innovation and test automation: How are they connected?  

Innovation comes to the rescue during uncertain situations, but the question is how reliable the innovative approach is? The reliability of any innovation lies in the quality of the end-product/service. The only way to make any innovation work both for the banks and customers is to ensure the quality is exceptional. Performing test automation of the new banking/financial process will identify any errors and allow the developer to fix them before its launch. If innovation is inevitable to succeed during uncertainty, test automation is the key to make innovation successful. 

The technological innovations in banks and financial institutions are revolutionizing the entire banking/financial system. However, they are mostly associated with operational and infrastructural flaws. A good test automation strategy can help organizations to identify any errors offering a flawlessly working system to their customers. This process of performing testing improves the user experience, creates brand credibility, and increases business revenue.  

The current challenges faced by the global software testing fraternity 

The current market scenario, which is rapidly changing, demands organizations to incorporate QA processes that can support the high market volatility. Though organizations are already on the path to develop intelligent test automation solutions (some of them are already made available), there are many challenges that are being worked on. Some of the major test automation challenges faced by banks and financial companies are: 

Test case generation issues 

Banks and financial institutions offer a wide range of complex, financial services such as money transfer, savings management, loan origination and completion, credit card management, real-time updates etc. These are highly complex processes involving sensitive financial data, hence, there is no scope of error offered to the organizations. Further, the organizations are required to ensure complete privacy and security to avoid any kind of violation. With so many critical parameters involved, the organization should have ample data to generate a good test case repository and help save time, cost, and effort. Creating the right test cases will help banks and financial companies to improve their QA process considerably, whereas a set of poorly written test cases still remains a major challenge for a company’s growth.  

Performance failure scenarios 

Banks and financial institutions have thousands of customers accessing the organization’s system, all at one time. Any failure in the performance can even cause financial losses and destroy the brand completely. It is important for organizations to monitor their system performance to ensure a seamless user experience. Though banks and financial companies are investing in improving the stability and scalability under high traffic, unexpected performance failures remain a matter of concern, even today. 

Longer time to perform test execution 

Automated testing system performs regression testing every day at a particular time to ensure there is no discrepancy. Undoubtedly, test automation has reduced manual effort considerably by automating the entire process, however, the run time is still high and one of the challenges the banks are facing today. Reducing the test execution time will be a major milestone that is being solved with intelligent automation. 

Regulatory and security concerns 

The increasing scenario of cyber threats has led to strict regulatory laws, it is important to integrate a test automation solution to comply with the financial regulations. Test automation helps in a thorough audit to reduce/eliminate the chances of increasing fraudulent. Excellence in test automation is important to succeed while ensuring there are no significant breaches. 

The changing trends of test automation in banking/financial sector 

Gone are the days when people had to visit the bank for even simple money withdrawal, digitization has changed the way banks operate. The entire banking/financial process can be completed with a few clicks, at the comfort of home. No longer queues or no more taking a personal day off to complete a transaction, banks are coming up with innovative and practical solutions to ease the complex financial processes. 

Test automation has redefined the modern QA system, emerging as a key facilitator for the success of any organization. The testing framework has evolved over time to ensure that quality remains the prime interest. Organizations are now preferring agile test execution methods to support the advanced continuous integration and continuous delivery pipelines. The improving emphasize on quality can help organizations to brace themselves and face unexpected scenarios. 

The emerging trends in test automation that can help organizations emerge as future-proof and offer great business solutions are: 

  • Codeless test automation: Automating the test without the need for extensive coding. 
  • Shift left testing: Incorporating testing at the early stage of the software development lifecycle. 
  • Cloud-based testing: Testing software using Cloud-computing services. 
  • AI-powered test automation: Leveraging the power of Artificial Intelligence to perform testing. 
  • Mobile test automation: Automation testing for native and hybrid mobile apps. 

With the growing technological advancement in the banking and financial sector, it can be concluded that the need for test automation will remain the priority. Testing teams in collaboration with the development teams are planning well-established, well-aligned, agile, and practical solutions that will redefine the concept of test automation for the better. 

The future test automation models: Innovation is the key 

Uncertainty in the market scenario has redefined the banking experience. Banks and financial institutions are strengthening their market presence with agile solutions to support future models. Major emphasize is being laid on the test automation solution to improve the customer experience which will, in turn, increase the business revenue tremendously. This new trend of improved QA has made way for efficient business models, which is possible only by an innovative approach. 

Innovation in software testing allowed the evolution of tedious manual testing to easy and much efficient test automation solutions. Innovation is the key to reimagine the test automation models for advanced and intelligent solutions. AI and ML integrated models are already making their way into the market to create future-proof technology, more future solutions are in the pipeline.