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Yearly Archives: Knowledge CenterApril 2020

Is Machine Learning All Set to Replace Selenium Automation Testing
23 Apr, 2020

Is Machine Learning All Set to Replace Selenium Automation Testing

  • QA InfoTech
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Machine Learning replacing Selenium Automation Testing

Artificial Intelligence (AI) as well as Machine Learning (ML) are the latest buzz in the field of digital transformation. But are they ready to replace and take over Test Automation done with Selenium? Let’s find out!

Selenium is known to be a web-based testing tool. It is an open-sourced tool widely used in comparison to the other licensed ones. It, being an open-sourced, portable framework; could be easily used for automating applications. 

Some of the features of Selenium are as follows:

  • Highly flexible in regression and functional testing. Regression testing mainly helps in executing tests against altered applications. These can check the running of these altered codes accordingly. Whereas, the functional testing helps to test the business requirements against the software and ensures that there is no defect with the software.
  • These tests could be written in various programming languages, for instance, C++, Python, Ruby, and Java. 
  • Selenium easily supports cross-browser testing. This enables test cases implementation across different browsers a number of times.

Both AI and Machine Learning are not only simplifying software automation but also contributing to the complete makeover of the tech industry. Since the conception of AI/ML, the future of the Selenium Test Automation seems like the setting sun. Even though both have unique features to support their existence, it is hard not to look towards Machine Learning for some phenomenal leaps in technological advancement. 

Are you wondering how? Here, are some examples of how Machine learning can change the face of test automation by preventing some of the cases:

  • Manual work for writing test cases can be reduced
  • In case, the test gets brittle, it may lead to false failures because of the framework skipping steps or dropping some of the tests.

After going through Selenium test automation, one would find Artificial Intelligence a completely contrasting concept. With respect to AI, human intelligence used by machines that mainly deals in simulation is a fresh concept for many of us. This technology allows the system to work on the assigned tasks that must be performed by the user. AI technology can easily transform and develop businesses. 

Some of the features that help in better understanding of Machine Learning are as follows:

  • Bots are being used by various organizations as an alternative option for financial prudence.
  • Machine Learning is the best option for tasks committed by programs that require repetitive execution.
  • For operational activities, data extraction and calculations, Machine Learning is being considered for helping with business processes for different organizations.
  • From the security perspective, test automation using Machine Learning can protect the user data more effectively and reduce the risk factor to a great extent.

It was long back when vendors like the Silk test and WinRunner dominated the market. It was after that the Selenium came in and boasted about focusing more on clearing the programming issues and plight of the developers while they created automated tests. Even then, the testers were not satisfied because of Selenium’s high maintenance and flaky tests that drove them crazy. 

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Also Read: Best Practices For Selenium Automation Testing

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As Google CEO, Sundar Pichai said in the Google conference, “We are moving from a mobile-first to an AI-first world”. He was very fore-sighted in saying so. The world is changing at a great pace and our future is getting largely dominated by human intelligence stimulated machine learning. AI automation being leveraged by Machine Learning is all about using spidering to write the tests automatically for the better application.

AI-assisted automation tools

Some of the automation tools that use AI-assisted technology and are leveraging machine learning are as follows:

  • SauceLabs

SauceLabs is one of the world’s biggest continuous testing cloud for mobile and web applications. It works towards providing a flawless digital experience by furnishing scalability, coverage, and analytics quickly. It has access to a large reserve of data that can come up with insights and leverage Machine Learning. 

SauceLabs allows the testers to run their tests on more than 700 different browser platforms, in the cloud. It is one of the pioneers in using Machine Learning in automation testing. Sauce Performance, an addition to the original platform, helps the developers to measure the performance of the application metric in the early developmental phase of the software.

  • Applitools

This is another tool making a big difference in the field of Machine Learning. It has opened new innovative ways of testing. It works on the idea of visual validation testing. It brings to use a sophisticated and intricate algorithm to bring out potentially active bugs on the applications without the tester specifically bringing out the elements. 

With Applitools, it is easier to create visual testing as it can be done without requiring percentages, configurations or visual processing settings. The adaptive algorithm of this tool makes it stand far above the reach of Selenium automation.

  • Testim

Another tool to leverage Machine Learning is Testim. It is known to speed up the process of execution, authoring and maintenance of automated tests. It reduces flaky tests and goes through intensive maintenance themselves that was the main challenge faced with Selenium Automation testing. So, most of the organizations are becoming inclined towards bringing machine learning to the picture and leaving Selenium testing behind.

  • Test.AI

This tool is way different from the rest as it has been designed to combine the AI brain with Appium and Selenium to produce the best of both worlds. Here, there is no need for messing with the element identifiers and no requirement for codes. This AI automatically helps the applications to execute test cases by just identifying the elements and screen dynamically in the application. 

It is a smart tool that helps to identify the changes in the elements itself and adjust it by making manual changes with the tester. Even if this tool is still in the Beta stage, it has a lot of potential to change Machine Learning as we see it.

Other tools that make it to the list are:

  • Mabl
  • Sealights
  • ReTest
  • ReportPortal

There are many organizations that are making a shift from Selenium to Machine Learning for automating web applications. One can shift to ML and AI technology completely or they can try and create custom solutions that can adjust with existing Selenium automation. This could be a costlier process that would need hiring good engineers specializing in ML, writing codes and performing intricate procedures.

Why is Machine Learning gaining importance?

With Machine Learning, a lot of positive changes are taking place that is making it acceptable throughout the software testing world. It is necessary to understand the way ML works in the day-to-day scenario to make automation safer and more efficient.

Domain Model Creator

Having to train a Machine Learning-based algorithm, one must come up with a testing model. This may need someone who has domain knowledge and efficiently indulges in creating models to help in development endeavors. This change has made it easy for those who know how to automate by analyzing and gaining an insight into the intricate data structures, algorithms, and statistics. 

Testing of UI Interfaces not necessary

There is another change brought about by Machine Learning that has made the difference in the world of automation in the absence of user interface. Being backed by Machine Learning, automation has been made easier for many engineers who were initially dependent on testing UI interfaces.

Automated testing by Visual Validation

Machine Learning works on pattern recognizing techniques. The most popular pattern is the image that is used by automated tools for visual validation. Its tools like Applitools use Machine Learning to detect differences that may be missed out by the testers. This tool makes sure that the user interface directly faces the user and recognizes the features such as color, eyes, size, and shapes without overlapping or hiding other user interface elements.  

Running only the important tests

Many times, the entire test is run, but the minor fault remains hidden. This is not the case with ML. Many big, global companies are taking the help of AI tools through which ML is used to pinpoint the exact error that needs to be rectified. This saves a lot of time and helps analyze flag areas and current test coverage.

Spidering AI

Another popular practical application of AI is the use of ML to automatically write software tests by spidering. For instance, the latest AI/ML tools like Mabel helps to point to your app and automatically begin crawling. 

While the tool is crawling, it also collects data relevant to features such as capturing screenshots, downloading the HTML code for every page, calculating the page loading time and so on. It runs the same steps again and again. Over a period of time, it builds up a comprehensive data and trains your ML models for the expected patterns of your app. On running it, it compares the current state with all the known patterns that have been learned before. In case it notices deviation, such as JavaScript errors, a visual anomaly or slower page loading speed, it will immediately flag it as an insight or potential issue. 

Even though this approach is still in its infancy, according to Oren Rubin, the CEO, and founder of ML tool Testim, the future is bright for this method. Thus, it can help us in understanding which parts of the application need testing. ML tools can do extensive and tedious work while the human tester can simply verify it. 

More Reliable Automated Tests

Selenium or UFT tests often fail due to changes made by the developers in the application. It can be something as simple as renaming a field ID that ends up failing your test. The current solutions are chiefly based on one selector or one path that basically uses only one way to find fields in the app. However, AI tools can employ advanced ML in order to adjust to these changes automatically. This, in turn, makes our tests more reliable and easy to maintain. 

Current AI/ML testing tools can learn about your application, understand relationships between parts of the DOM and learn about changes in the framework with time. Once it begins to observe how the application changes, it can automatically decide which locators should be used to identify an element during runtime, without you having to lift a finger. Furthermore, if your app modifies, the ML tool can learn the script and automatically adjust itself accordingly. 

In summary, Machine Learning has already brought about some dramatic improvements in the field of technology, but it still has a long way to go. To deliver the highest level of understanding and accuracy, it still needs the assistance of human intelligence. It has its limitations, but the future looks bright for the technology.

Top 6 Trends in Mobile App Testing Services
20 Apr, 2020

Top 6 Trends in Mobile App Testing Services

  • QA InfoTech
  • Blogs,Mobile Testing
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most popular mobile app testing trendsThe almost endless proliferation of smartphones has led to the creation of one of the largest industries in recent times – the mobile apps industry. The Play Store, App Store, Windows Store, and Amazon Appstore have over 5.55 million apps between them. And the numbers continue to rise. The industry is already as big as $189 billion. While this industry has its own trends that come, go, or evolve, the mobile app testing sector has its own importance.

Benefits of Mobile App Testing

If you want to succeed, you should be serious about the quality of your app. Quality translates into greater reviews on different app stores, which translates into increased downloads and sales. This is where app testing services come into the picture. Some of the advantages of quality app testing are as follows:

  • Enhanced UI & UX: App testing allows your QA team to get into users’ shoes to assess the UX. The process explores the logic and goals behind the app and checks it for convenience and intuitiveness. 
  • Performance: The job of QA engineers is to ensure that an app performs as per expectations. The app is checked for its performance in different conditions such as changing networks and varying load conditions. App testing ensures that your application performs smoothly in all different conditions.
  • Building Loyal Users: Using the right app testing services can make all the difference in building loyal users. It is important to test often and many times.

Latest Trends

The mobile app testing sector is always at the edge of innovative technologies. Here are 6 of the most popular trends which are shaping or re-shaping this industry.

1. Security Testing

It is not just websites and computer systems that are vulnerable to online security threats including hackers. The mobile app industry has also been at the receiving end for some time. The latest in-app testing is security testing, as many advanced manual, functional, and automation testing tools are available in the market enabling quality for this much needed area. 

Security testing is focused on ensuring that clients and end-users have apps that are from potential threats. Highly secured apps also play a big role in building the image for businesses. This further means they can provide higher ROI due to their wider acceptance.

2. M-Commerce App Testing

M-Commerce is poised to touch almost 45% or $284 billion of the overall e-commerce market in the United States. That is not just a staggering market size, but also a stunning market share. What this means is that mobile commerce has also become almost equally important to e-commerce for businesses. And so has m-commerce become important to app testing services.

Many businesses now realize the importance of developing and launching custom m-commerce apps to drive more sales and to stand out from the crowd. Growth is further fueled in this segment as consumers increasingly rely on and trust digital pay channels like e-wallets. 

The growing use of m-commerce apps has made it essential for businesses and app testing services to test them broadly. These apps are tested for many aspects, with significant importance to:

  • User experience
  • Security
  • Performance

And all the stakeholders realize that failing to do so can affect consumer trust.

3. Open Source Automated Testing

The level of flexibility offered by open-source tools is unmatched. They support testing of all types of apps ranging from Native to Hybrid apps. Besides, they provide the code libraries for all programming languages and can be customized to adapt to various technologies. More and more app testing services have realized these benefits and increasingly use test automation initiatives. 

The growing use of tools developed on open standards is a big trend in mobile app testing (and across the overall software development spectrum). Developers no longer have to rely on proprietary full stack automation tools that were rigid and expensive. 

This helped in the creation of many open source tools and made it easier for DevOps teams to integrate a wide range of tools to create and maintain their toolchain. So the new trend is the creation of DevOps toolchain with pre-integrated foundation and open standards among an increasing number of DevOps teams. And this will also see increased adoption among app testing services.

The need for improved testing speeds and developer feedback continues to grow in terms of importance across businesses of all sizes. And open source tools have proven to be cost-effective and flexible solutions that can meet the growing testing needs. 

Large numbers of apps are released daily for different types of users and with different goals. There are also regular updates that take place on a regular basis to enhance the UX. This creates much pressure on both the development and app testing services. 

In the near future, you can expect release cycles to become shorter, creating tighter testing schedules. Mobile app environments are becoming less complex, the time to test code has become shorter, and the code can be run on different mobile platforms at the same time. Test automation is going to play a big role in further shortening this time.

4. Use of AI & ML in App Testing

Almost all sectors, especially in the IT field, are embracing or preparing to embrace AI and ML. The use of Artificial Intelligence and Machine Learning has improved the processes and workflows related to the testing cycle. 

Some of the key features of this development are as follows:

  • AI is not expected to take the place of human QA experts, but to increase the efficiency of the testing process
  • It is expected that QA experts will be using AI more to complete certain tasks
  • The use of AI Testbots means that there will be no need for designing test scenarios or write code, as the tools can study the app UI and detect critical errors within the user flow, at a much faster rate than human testers
  • AI can run heavy-duty tasks, including repetitive tasks, such as implementation, execution, and analysis of tests. Humans can be allowed to focus on tasks that involve critical thinking and creativity.

The use of AI for app testing services can also help companies to make the most of DevOps and Agile concepts in a better way.

5. Automated Voice Testing

This is a unique new development in the area of app testing services. Automated Voice Testing or AVT has already been in trend and continues to grow in terms of its importance. The system makes use of a speech recognition engine to control the device automatically even when it is executed by a human user. 

Voice testing has already been automated. Such tests are applicable for all types of voice apps that run on any device.

6. Cloud-Based App Testing

Mobile cloud systems allow app testing services to assess the performance and looks of the apps across different devices without the need to invest in additional hardware or devices. Testers are increasingly relying on these cloud systems to create a custom testing environment. These systems can work with most of the real-time devices connected to the network. 

The demand for cloud testing is new. And investing in such platforms is beneficial for teams to lower costs and save valuable time.

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You may also enjoy: Benefits of Cloud Based Regression Testing

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It is not possible to test apps on all the devices that your target audience will be using. Just this year, more than 1.5 billion smartphone sales will take place, and they will run on different operating systems, versions, UI, and screens. Unless you can conduct manual tests on all these variations of devices available today, using cloud-based testing is the ideal solution to boost testing efficiency.  

Cloud devices allow you to check user-app interaction and ensure the product is of high quality. They provide the perfect solution for large-scale app testing.

With the increase in testing challenges, there has been a surge in need for a stable testing platform that has:

  • Testing frameworks
  • Desktop browsers
  • Emulators
  • Different mobile devices

This can help in reducing the inconsistencies that exist in the current testing environment. This trend will also help in reducing turnaround time and enhance quality.

New mobile devices with different and more complex configurations continue to get added to the market. It is expected that this trend will continue to grow over time. This will make things even more challenging and difficult for app testing services. Successful services focus on creating newer and better test automation strategies and frameworks to ensure that apps are secure and responsive across all devices. 

Continual innovation requires continual growth. This is not possible without continual testing across new test automation systems. Such platforms make it easier for you to check for defects at an advanced stage in the testing cycle. Thus, a roust automation test suite is one of the biggest focus areas in recent times. It can help in detecting newer defects within your applications.

New ASBA Approach to Address DevOps Security Vulnerabilities
17 Apr, 2020

New ASBA Approach to Address DevOps Security Vulnerabilities

  • QA InfoTech
  • Blogs,DevOps
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ASBA Approach For DevOps Security Vulnerabilities

Security has been a challenge in the DevOps environment, even when many IT companies have embraced unique models like DevSecOps. This model integrates security and QA teams along with development and operations teams throughout the application lifecycle. 

While DevOps may solve many challenges that exist in the software development process, it is not completely a foolproof approach to use as is, out of box; for example, it may not solve or rather may bring in newer  new security challenges for the industry. 

Left Shift of Security

The DevOps culture of continuous application creation, delivery and updates seems to be the perfect solution at first glance. However, from the perspective of security organizations and teams, this model complicates rather limits code analysis and adherence to other security routines before application deployment.

The exciting thing is that DevOps has an opportunity built into it to get security right. Teams can introduce security during the early phase of the development cycle, thus getting the chance to address issues from the very beginning. The traditional approach to software development follows a left to right path. It includes planning followed by coding, testing, and eventually, deployment. Security comes into the picture close to the deployment stage. It is anchored onto the code instead of being integrated from the beginning.

DevOps allows shifting everything to the left. Thus, security tasks can be moved to the left within the application development process. 

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Also Read: What does 2020 look like for DevOps?

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The ASBA Approach

Recent developments have led to the creation of a new approach to cloud workload protection and monitoring. This new approach is known as Application Security Behavior Analytics or ASBA. There are 3 key components to this new approach to address the security challenges in the application:

1. Real-Time Application Behavior Profiling

Business applications usually have similar behaviors and communications with other entities. For example, an e-commerce application may look almost like an ERP software. Both applications involve synchronized activity between caches, web servers, databases, and load-balancers. There may be interconnected applications, web, and database servers all having correlated behaviors. For example, an increase in database activity may be connected with an increase in application server activity. 

But there is a significant behavioral difference between an ERP and an e-commerce application. ERP is accessible only to users on a LAN, not on the internet. However, attackers with malicious intent usually don’t care about these meticulous behavioral characteristics. They think that once the enterprise security is breached, they will not be detected. And this is what usually happens.

The ASBA approach is capable of discovering crucial anomalies. For example, it can detect an anomaly involving a database that has become busy when the front-end server remains idle. Any deviations from the standard baseline may be a sign of malicious attacks, including those from inside or outside. It may be a sign that the network has been accessed from an unusual or unrecognized location and at unusual hours. This approach can detect such incidents in the real-time, facilitating quick response, automated remedy, and forensic investigation.

The new system profiles the behavior of applications to create a foundational model of regular activity. This helps detect anomalous activities and potential threats. This is done by monitoring almost endless parameters against each workload which includes their processes, relationships, dependencies, and temporal fluctuations. This data is then compared with data sourced from other workloads.

2. CI/CD & SDLC Security Monitoring

An increasing number of organizations using digital information are using new and innovative development techniques. However, as part of the process, they usually expose themselves to new potential threats by creating new openings of a security breach.

CI/CD works by accessing an organization’s IP. Unfortunately, it doesn’t have a dedicated security cover. However, it needs protection against insider threats, process leaks, and external attackers. CI tools are quite capable and are effective in automating development functions such as workflows, promotions, quality gates, and defect tracking, amongst others. Any attack or failure can mean the organization can suffer massive damages.

A breach or failure in the CI toolchain can stop software development activities and prevent recovery of services that may have failed. This is where the ASBA approach can help enhance security and fix potential loopholes. It can profile, identify, alert, and diminish failures or loopholes in the toolchain. It can do the same for the processes delivered by the toolchain before significant damage is inflicted. 

ASBA monitors different elements which includes anyone or any system that may have gained access to the source repository. It can also monitor those who may have pulled from the CI/CD tool, where changes may have been made to the workflow. ASBA is also capable of providing forensic data and eventually blocks any unwanted and unauthorized activities.

When third-party code is used in the development, it creates another kind of security risk. The threat becomes even more profound when open-source components are included. The combination of vulnerabilities within the code and dependency trees with the inefficiencies of scanners in detecting flaws, causes a significant increase in security risks.

The new ASBA approach creates effective protection through the detection of behavior anomalies. It provides this defense without concern for the technique used for the attack.

Development teams may use test-driven development (TDD) on a routine basis for automating software testing. Yet very few teams embrace required security validation. Very few product teams assess the implementation of code, APIs, and artifacts from the security perspective before release. Code quality and analysis have been evaluated for their security advantages and they cannot be relied upon entirely.

The new approach can develop a system of crucial feedback loop. This can increase security within the validation stage.

3. Run-Time Security

In an ideal world, a deployed software includes all security lessons learnt during the process of development and testing. However, in the real world, this is hardly ever the case. ‘Testing during production’ is usually the norm in the DevOps environment. However, it is nothing more than a maxim because deployment operations, architectures, scale and other elements during production stand apart from each other. This distinct nature of each element increases further after A/B and other tests, deployment, and operations. 

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Also Read: Performance Testing Within DevOps Practices

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ASBA helps in addressing the vast gap that gets created between application testing and deployment. It provides insights as part of the feedback system, leading to optimization of reliability engineering. This makes it easier to achieve service-level goals. 

ASBA offers answers that are actionable and prevents the need to query unstructured data. This is made possible because of the real-time alerts and forensic auditing potential of this approach. This is a big step forward from the conventional application performance management solutions. Hence, there is the visibility of potential risks through the entire SDLC. This is also the case during the deployment and operation of the application.

The Need to Bridge the Communication Gap

While this new ASBA approach is meant to plug most of the security loopholes in DevOps, it is also essential for security practitioners to bridge the communication gap existing between the security team and the other teams within the organization. Even today, security is mostly an afterthought, largely due to the lack of understanding and awareness amongst teams.

As the world continues to move towards DevOps, security teams must point out and elaborate on their concerns in a language that is readily understandable for the operation and development teams.. It is recommended for security teams to emphasize the priorities of the development and operations teams and also help them align with the jargon they use. When the security team understands what the other teams care about, they can contribute to the latter’s success too

Security Threats – The Scenario

Security breaches can cause much more damage than just affect the operations in the short-term. They can also cause long-term and expensive disruptions. The 2017 Uber breach incident is a perfect example here. A developer had mistakenly published credentials on a popular software development hosting service. Even when this was a mistake that was common during the process of code compilation within the agile development environment, hackers were quick to fabricate a breach that affected more than 50 million users and 0.6 million drivers.

Conclusion

So what can the DevOps community do in addition to following the new ASBA approach to enhance security? A highly secure DevOps environment should run on separate tools, policies, and processes to enable secure and quick releases. The security breach mentioned above could have been thwarted, by a final security scan which detected any embedded credentials within the code. 

When all these approaches come together, it can create an almost impenetrable environment for software development, deployment, and management. Significant developments are taking place in the DevOps environment in terms of security enhancements. This makes it easier for more organizations to embrace this model in the near future making the digital world not just experience rich but also safe and secure

Advanced Mobile App Testing Trends Driving Innovation and Efficiency
07 Apr, 2020

Advanced Mobile App Testing Trends Driving Innovation and Efficiency

  • QA InfoTech
  • Blogs,Mobile Testing
  • no comments

Mobile App Testing Trends 2020

Mobile applications have become a major medium for businesses to reach a large number of online customers and drive sales. According to a research report, an average American is glued to a mobile device for an average of 3 hours and 43 minutes on a daily basis. And checks out the smartphone for around 80 times a day. Hence, it has become business-critical to have a capable mobile app.

Mobile applications that fail to meet the expectations of the end-user tend to face the wrath in the form of low ratings and negative reviews; directly impacting the business and brand.

To have a successful mobile app, it is crucial to have an application that is equipped with intuitive and immersive user experiences (UX) capable of delivering great performance in any condition. Changing market dynamics and increasing customer expectations are continuously evolving the mobile app testing industry. To enable high performance delivered at a faster time-to-market, advanced mobile app testing trends have emerged.

To achieve quality-at-speed, businesses are focusing on thorough mobile app testing before every release of the application. As a result, the mobile app testing market is poised to witness a brisk growth at the rate of 19.5% CAGR between 2016 and 2026 to reach $13.3 billion.

Let us have a look at advanced mobile app testing trends that will drive growth in 2020.

Shift from Performance Testing to Performance Engineering (PE)

UX of a mobile application plays a key part in its success. Hence, it has become evident to take a decisive shift from performance testing to performance engineering for more immersive and powerful user experience. PE will focus more on intricate application architecture and criticalities of implementation to the delivery of a preferred mobile app that meets all the required performance specifications. Performance engineering also helps to cut down costs and achieve greater return on investment (ROI).

Uptrend in Security Testing

Cybersecurity attacks and data security have become a global concern among customers from all quarters. To win customers’ trust and confidence, it has become imperative for businesses to roll out bulletproof mobile applications.

To address this issue, mobile application security testing uptrend will happen. Automated mobile app security testing will become the norm to deliver more secure and reliable apps. The promise of safe and secure mobile applications will help businesses attract more customers and achieve greater ROI.

Automated Voice Testing (AVT)

AVT leverages the speech recognition engine to automatically operate the device through a human-like conversation. It is worth noting that this technology is both platform and device agnostic. The automation of AVT tests is executed voice levels. Hence, such automated tests can be easily done on any type of voice application running on any gadget.

M-Commerce App Testing

With billions of mobile devices and the majority of people making online purchases through smartphones; m-commerce has become crucial for all businesses. To reach more customers, businesses are continuously launching highly customized and capable mobile commerce applications to secure more downloads and drive sales.

Ease payment through e-wallets and other digital channels has also accelerated the growth of m-commerce. The need for complex functionalities and robust user interface has made it crucial to execute thorough testing of m-commerce apps. UX, security, and performance of the application should be foolproof to deliver engaging and reliable customer experience.

5G Device Integration

With 5G already rolled out in multiple territories or Japan, China and UK; the high-speed technology is poised to become a global norm. With ramping up the 5G device ecosystem, more businesses will look to launch apps that are compatible with 5G technology.

Internet of Things (IoT) Testing

Majority of industry segments are integrating the Internet of Things into the business process to become more cost-efficient and accurate. Businesses are continuously releasing mobile applications to ensure smoother and reliable integration with multiple connected devices. Demand for IoT testing is expected to rise further to ensure flawless delivery of such mobile apps.

Increasing Use of Open Source Automation Tools

Open source tools are known for their flexibility and support the testing of all types of mobile applications fundamentally including Android, iOS and hybrid apps. Aside, the majority of open-source tools come loaded with code libraries for multiple programming languages that can be customized easily for different modifications. Hence, the usage of open source automation testing tools will further increase in test automation initiatives.

AI/ ML in Mobile App Testing

AI and ML are being leveraged in different business applications for greater efficiency and accuracy. Similarly, test automation is also leveraging AI/ ML to enhance processes and optimize workflows of the test cycle. Predictive analytics of test cases and log analytics are making automation testing more efficient and accurate. Advanced ML algorithms are poised to dominate automation testing to enable quality-at-speed.

With continuously increasing expectations of end-users for mobile applications, it is essential for every business to release a customer-friendly application that also secures and delivers optimum performance. A robust mobile application testing strategy is also required to ensure efficient implementation of constantly evolving technologies and more feature-rich hardware entering the market in a shorter period of time.

Hence, it is crucial to choose the right software testing tools along with testing strategy and resources. QA InfoTech is one of the leading software testing and quality assurance independent offshore vendors that specializes in mobile application testing for Android, iOS and hybrid applications.

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