Top Software Testing Trends to Look Out For in 2022

Software testing

With technical breakthroughs and digital transformation, the globe is growing at a quicker rate.

As a result, the SAAS industry is experiencing more competition than ever before to develop high-quality, scalable, and secure solutions as rapidly as feasible. Such growth in the IT industry impacts the way software is designed, developed, tested, and deployed.

Software testing trends are vital in the software development process. Hence, testing tools and processes should evolve in tandem with evolving industry trends. Let’s glance at the drivers that will be changing software testing in 2022.

Drivers Leading the Changes in Software Testing in 2022:

software testing trends
Software Testing Trends

1. Faster Application

Organizations are adopting Agile methodology and DevOps to enable frequent client requirements to be promptly implemented in the application in a hassle-free manner, it’s why the faster application will be a trend in software testing in 2022.

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The major goal of the agile approach is customer satisfaction. Thus, requirements collection and implementation take less time. 

DevOps is a concept in which developer and operations procedures are managed together. 

DevOps is required to streamline and automate the processes involved in an application’s development.

Likewise, to deliver viable applications and required changes rapidly, QA professionals are now supporting DevOps engineers, leading to a new trend in the testing landscape known as QAOps.

2. Secured Applications

Users can now consume services via an app or a website, thanks to technological advancements. This ease of consumption also introduces potential security risks, not just to individuals but also to businesses, resulting in financial and reputational harm. 

Data theft, denial of service attacks, phishing, SQL injection, password attacks, etc, are all possible dangers. Data breaches in Central Bank and a data security incident in the Monongalia health system are recent examples of security risks.

According to the article, 76% of organizations worldwide expect to be targeted by a cyberattack this year, making application security one of the key drivers of the software testing trend in 2022.

3. Building Connected Applications with APIs

Around the world, applications are now being developed in such a way that they can be run in a distributed environment.

Applications within different systems may need to communicate with one another in exchange for providing restricted data and information to either develop a module or extend the functionality of the existing services.

An API is a reusable component that provides an interface through which these applications can interact.

Several apps and websites, for example, make use of the Google authentication API to allow users to sign up with Google. Hence, APIs should be rigorously tested to eliminate security threats and provide secure and reliable connections. 

4. Building Scalable Applications with Containerization and Microservices

When there is a sudden traffic spike to an application, it becomes impossible to maintain the application, and the entire system may collapse due to its inability to expand.

To avoid these circumstances, organizations are developing apps that employ the concept of containerization and microservices. Containerization allows the software to scale horizontally in a distributed system.

An application’s whole functionality is made up of loosely coupled tiny services. The process of scaling up an application is time-consuming and prone to various bugs, thereby requiring the testers’ undivided attention.

5. Building an Intelligent Application with AI/ML

The exponential growth of AI/ML has impacted every human life. Machine learning and artificial intelligence’s use in designing applications has improved the users’ experience. Hence growth is inevitable. 

According to Gartner, the AI market will rise to $62.5 billion in 2022, up 21.3% from the previous year.

Different branches of AI like Machine learning, deep learning, neural networks, robotics, and natural language processing are being leveraged to induce human-like interaction in their services. 

Being a new-age technology, AI/ML has its own sets of challenges. Thereby, testing the applications is a task, and the market for testing & QA is yet to expand in the coming years. 

Following are some foreseeable trends of software testing in 2022.

Read Also: Software Testing and AI. The 2020 round up

Top 8 Software Testing Trends in 2022:   

1. No Human Intervention In Testing with Autonomous Testing

Although automation testing is not a new concept in the testing world, there is still a lot of room for advancement. Testing must be completed quickly to keep up with the urgent need of meeting the client’s expectations. 

It is difficult to develop and maintain test cases manually, which can only be executed by QA engineers. Autonomous testing entails covering all types of test cases by understanding all of an application’s functionality, including border cases that testers may overlook. 

Autonomous testing tools are developed by incorporating AI/ML in a way that can generate test cases with some predictions/specifications provided by a QA engineer and can remodel by self-learning. 

There must be a correlation between the learning model and test cases so they can update each other with very little or no human intervention at all. 

2. Convergence of Different Tools for Test Planning, Test Design, and Test Execution

Testing tools that can’t be converged with other tools will quickly become obsolete. With the rise of the tech industry in a variety of fields, applications may be produced utilizing a variety of tools at various stages of the SDLC. 

Similarly, multiple testing tools can be used for design testing, executing test cases, and planning test scenarios at each phase of development. 

As a result, tools that can easily be integrated with other tools throughout the development process will be in great demand.

3. Model-Driven Architecture Based Testing (MDABT) to Increase Test Coverage

System Architecture is taken into account for curating numerous models to test the application’s overall perspective.

To maximize test coverage, Model Driven Architecture Based Testing is carried out by building automated test cases. MDABT is a more advanced method of performing MBT.

Model-based testing (MBT) is a technique for automating the creation and execution of test cases based on system specifications and behavior.

Models are used by MBT to automate the creation and execution of test cases. A model is a representation of the desired behavior of a system under test that is usually abstract and partial. 

4. Shift Left Testing

To deliver a quality product in an agile methodology, errors should be identified and prevented as early as feasible in the software development life cycle.

It will aid in minimizing the number of defects encountered during production deployment, lowering the effective cost.

Shift left testing begins with the gathering of requirements and continues until the product is delivered. In the shift-left approach, testers work with BAs (Business Analysts) and developers.

Input at each stage from QA engineers and automated test cases provides better test coverage of the application.  The primary purpose of shift left testing is to provide a high-quality product.

5. More Focus on Security Testing to Mitigate Risks

When an application is released to end-users, it should ensure the security of the application, the customer, and the organization to mitigate risks against all possible threats and vulnerabilities.

Any loophole in the app will serve as a doorway for malicious activity, putting customers’ privacy and confidential data in danger, as well as bringing the company to criticism and financial damage.

Security testing techniques, such as pen tests, could be used to identify flaws that attackers could exploit. Monitoring access restrictions, static code analysis, strong passwords, brute force attacks, URL manipulation, and other aspects of security testing should all be considered.

6. API Testing for Checking APIs and Integrations

API testing is critical because APIs provide an interface that allows communication between diverse services.

APIs should be rigorously tested to ensure that they only deliver the functionality for which they were created. API testing guarantees that APIs are secure, reliable, and perform effectively. The purpose of load testing is to see how many integrations an API can manage.

API testing ensures that connections between different services are reliable, stable, secure, and scalable. With the increase in cloud computing and microservices, API testing will also trend soon.

7. Testing of AI/ML Models in Application

Learning models are used to train AI/ML-enabled applications. To train an AI model, data is separated into training and testing sets.

Testing these models is tough since it necessitates the generation of large amounts of real data to achieve the greatest level of precision and accuracy in the application.

Currently, AI model testing is mostly done by developers, but as AI technology advances in the next years, testers will be required to test AI models as well.

8. User Acceptance Test is Going to be More Important

Any application must meet the basic condition of user satisfaction. Because of the great availability and fierce competition in the market, switching to another application is rather simple if the user is dissatisfied with any part of the current application.

As a result, any software product must undergo user acceptance testing. User acceptance testing is the final chapter of an application’s testing before it is released into production.

It is performed to gather feedback and determine customer satisfaction levels.

9. Integration Testing to Ensure Smooth Working

Software engineers develop an application by breaking down each feature into its module. With the growing adoption of agile methodology, these modules are developed in different sprints.

Sometimes during the merge of two or more application modules, defects may be raised. Integration testing is performed to find out such defects to ensure the smooth working of the entire application.

It is the second stage in testing after unit testing.

10. Accessibility Testing is Going to be More Crucial

A software product’s mass appeal can only be ensured if it is accessible to all other users, including those with physical, auditory, visual, speech, learning, cognitive, language, and neurological disabilities.

Accessibility testing is carried out to ensure that differently-abled people can also interact with and use the application.

An application should have screen readers, screen magnification software, speech recognition software, and customized keyboards for work and communication.

ML, Neural networks, Deep learning, and Natural Language Processing (NLP) are examples of AI disciplines that can be used to create highly accessible applications.

11. Performance Testing will be of Great Importance

Performance testing is done to ensure whether the application will be able to handle the fluctuations in load in the real world. After deploying the application in production, the application might face a different amount of traffic each day.

For example, during the holiday season, websites advertise massive bargains to attract more customers, resulting in a great increase in the application’s traffic. The application must be able to cope with the load.

As a result, numerous design strategies, such as scalability, data sharding, load balancing, etc. should be effectively incorporated within the Software development life cycle.

12. Rapid Software Releases with Scriptless Test Automation

The QA engineer must devote time and effort to scripting or writing test cases. Testing apps without writing actual code or scripts for various test scenarios is known as scriptless testing. 

The scriptless Testing procedure can be automated by recording a tester’s action on UI. The Selenium framework is commonly used to create automated test scenarios.

Automated scriptless testing saves time for QAs, allowing the testing process to be streamlined to quickly deploy applications to customers and beat the high competition.    

13. High AI Intervention for Maximum Precision and Accuracy

AI models can be trained for multiple sets of data due to the availability of large amounts of data. The more the data, the more precise and accurate the prediction will be.

Applications based on AI technologies, such as machine learning, deep learning, natural language processing, and others that deliver maximum precision are excellent in terms of user experience and satisfaction.

Organizations should focus more on AI intervention in their applications to improve product usability. 

14. Chatbots Testing

What began as a convenience has now become a requirement for every software product.

According to the Deloitte Digital Report, by 2022, chatbots would save businesses $8 billion each year.

Chatbots have significantly increased sales for all businesses since they automate the entire customer care process and are available 24/7.

Chatbots provide a way for people to interact via text or voice. Incorporating chatbots into apps needs testing for tone, accuracy, and response speed, as well as data security. The customer should not be confused by the assistance provided.

Hence the chatbot should be powered by AI. Security testing, user acceptance testing, and other types of testing should all be included in chatbot testing.

15. Big Data Testing

Living in the decade where meaningful data is a goldmine, we ought to test applications that support terabytes and petabytes of data known as big data that traditional systems cannot handle.

The three main stages of big data testing are:

  1. Data gathering,
  2. Data validation/processing, Employing map-reduce logic and
  3. Producing key-value pairs of data, and output validation.

Big Data can be processed using Batch, Real-Time, and Interactive processing. Different approaches, such as Performance testing, Architecture testing, functional and non-functional testing, etc. can be utilized to test big data applications.  

Major issues related to big data testing include the requirement for highly skilled personnel, the amount of data, and the unavailability of a single platform that can perform end-to-end testing. 

16. Internet of Things (IoT) Test Automation

Unlike traditional technologies, an IoT system incorporates both hardware and software components.

IoT is a smart way of living in which hardware systems, such as vehicles, medical equipment, home appliances, etc can be controlled remotely. While driving his car, a person can turn on the TV at his home with his mobile device, which is an example of IoT.

With the increasing trend of IoT, Test automation is the only way to completely test an IoT application considering the complexity of all of its components and connection among them.

The main objective of automated test cases is to maximize test coverage with the internet’s evolving nature and to ensure proper connection and data security in between.

17. Blockchain Testing

Blockchain is a very new yet rapidly evolving technology.

According to the Blockchain Technologies Global Market Report 2022, blockchain is anticipated to rise at a compound annual growth rate (CAGR) of 56.9%, from $5.49 billion in 2021 to $8.61 billion in 2022.

Starting with cryptocurrency and finance, blockchain has a lot to offer in other sectors as well, such as insurance, media, communication, banking, retail, etc. Blockchain is a series of data stored as a block and chained by cryptography techniques.

It executes irreversible transactions, thereby providing security to organizations’ delicate data shared among multiple users.

Testing blockchains is tough because it necessitates a thorough understanding of the technology, which is rare as of now.

Blockchain testing involves smart contract testing, node testing, testing of the chain size, block size, collisions, etc.

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