Top 5 Software Testing Trends to Look Out in 2020

  1. Growing Use of Technology like Machine Learning and Artificial Intelligence
  2. Digital Transformation
  3. Transition to the Cloud and Increasing Adoption of IoT
  4. Performance Testing to Performance Engineering
  5. Big Data Testing

With the acceptance of digital technologies, software testing has taken a new spin. Organizations are increasingly considering approaches like Agile and DevOps, which encourage collaboration between testing teams and development teams. According to Gartner, by 2020, the costs of developing the IT industry will grow from 3.76 to 3.87 trillion dollars. Thus, IT plays a crucial role in our lives.


The global software testing market is also expected to grow at 14% CAGR (Compound Annual Growth Rate) by 2023. With the improvement and progress in the software methodologies and widespread acceptance of DevOps and Agile, software testing has evolved into a robust practice. 


Considering the current trends in technology and testing, we have projected top 5 software testing trends to look out for 2020:


1. Exponential Use of Technology like Machine Learning and Artificial Intelligence
Machine learning (ML) is the next exponential tech trend, and it is knocking on the front door. It has already led a revolution in the development sector and use of applications. Certainly, the market for machine learning is projected to grow from $1.41 billion to $8.81 billion by 2022. There will be more testing solutions for recurring tasks.

Major tasks performed by the adoption of Machine Learning:

• Optimizing the Test Suite – unique code checks

• Forecast – Prediction of the main test configurations

• Log Analytics – Identification of checks that can (or could) be executed automatically

• Defect analytics – Discovering high-risk application points for prioritizing regression tests


The market scenario for artificial intelligence is also growing faster. It is anticipated that by 2025 it will attain 190.6 billion dollars. This suggests that the IT business is progressively moving to machine learning.

Influence of AI on testing optimization. Source of the image

Artificial Intelligence shows that the evolution of software testing is entering a new phase, the benefits of which have yet to be learned.


2. Digital Transformation
 According to the World Economic Forum, the value of digital transformation for both society and industry could reach $100 trillion by 2025. Enterprises continue to undergo intense digital transformation and become more dependent on new-edge technologies. This sets high requirements to the stability and reliability of software. Hence, the significance of software quality assurance increases as well.


The share of IT budgets spent on Quality Assurance has full-fledged improved by 35% since 2015 and is predicted to face another increase by 2020. At the same time, digital transformation presupposes experimenting with digital features, which calls for the agility and flexibility of the development and QA processes. Therefore, quality assurance faces contradicting demands for flexibility and stability, which sets a challenge for QA managers. They continue to search for non-trivial means of setting QA processes in a way that allows meeting both requirements.


3. Transition to the Cloud and Increasing Adoption of IoT
With great stability, a growing number of enterprises move data storage and processing to the Cloud. According to the current research by Sogeti, 75-76% of all apps are cloud-based. At the same time, organizations are progressively adopting the Internet of Things (IoT) as this technology gives access to previously unavailable customer data and allows enterprises to make informed business decisions on the basis of data. The same study by Sogeti reports that 95-97% of the examined enterprises have implemented IoT solutions in any way.


Quality assurance for IoT and cloud-based apps calls for extreme specialized skills of QA team and QA engineers to better understand these apps’ implications on company’s business procedures.

The IoT creates a new height in systematic software testing. The following kinds of checks will be conducted in the IoT zone:

  • Scalability Testing
  • Testing the Compatibility of Device Versions
  • Monitoring Connection Delay
  • Safety Analysis (Device Authenticity, Availability and Accuracy of Authorization)
  • Data Integrity Evaluation

Despite the ever growing role of the IoT, 34% of the World Quality Report revealed that their products have Internet of Things functionality, but they still do not have a testing approach.

4. Performance Testing to Performance Engineering
Whether it is SMEs or big giants Performance Testing will continue to bring results and guarantee robustness. By 2020, performance engineering is gradually expected to replace performance testing methods. Performance Engineering evaluates customer experience, which results in commercial viability. Furthermore, it is significant to gauge the product performance to build robustness even in a crisis condition. Dates back the task of software testing was to assure product performance, however, now it is not sufficient to pay attention only to this element. Additionally, it is significant to focus on other elements like customer value, convenience and practicality of use, and configuration quality.


5. Big Data Testing
The trend of big data testing has been fueled crucially because of the robust procedures that various enterprises are following to churn out the best of marketing strategies. Acknowledging this fact, we must claim that the need for testing big data apps will witness a new height in 2020. In Big Data testing, software testers have to verify that terabytes of data are processed effectively using commodity cluster and other supportive elements. This kind of testing focuses on functional testing and performance testing. The quality of data is also a crucial factor in big data testing. The data quality is checked on the basis of distinct traits like consistency, validity, conformity, accuracy, data completeness, duplication, etc.


In addition, manual testing is gradually being replaced by automated testing. In 2020 we will see a hybrid of these two types, since there are still not sufficient tools to completely automate data processing and monitoring. But in subsequent years, it will be possible to see an almost complete replacement of manual testing with automatic.


Software testing will evolve into new dimensions by 2020 and the QA tester will have to grow to fill in larger shoes in future. The software tester will have to compete with a varied IT landscape, filled with new-edge technologies that demand automated, continuous integration among other traits, rooted deeply in the basics of software testing.

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How IoT and Machine Learning is changing the World?

  • What is Machine Learning?
  • What is the Industrial Internet of Things (IIoT)?
  • How IoT (Internet of Things) and Machine Learning affect our life?
  • Challenges- IoT and Machine Learning
  • Solution

IoT and Machine Learning are getting smarter. Companies are incorporating artificial intelligence (AI)—in specific, machine learning—into their IoT apps. From smart thermostats to wireless sensors, IoT devices are gradually but positively garnering mainstream adoption. Virtual assistants like Siri, Alexa, and Cortana are only making this technology easy to adopt.

The core purpose behind advancement in the IoT space is to help information move smoothly and seamlessly. For as much as we condemn technology, we can all recall a moment when the right message has appeared at the right time, with perfect user experience.

The truth of IoT and Artificial Intelligence – specifically machine learning – is far less sinister. It is shaping the way we live, travel, work, and communicate. In fact, it is shaping our lives smartly and the decisions we make.

The proliferation of smart IoT devices is shaping the future and gives instant access to the information world.

Let’s have a glance at these burning IoT statistics:

  • There are about 17 billion inter-connected devices in the globe as of 2018. With more than 7 Billion of these IoT (internet of things) devices. (Source- IoT Analytics)
  • According to McKinsey Global Institute, each second, 127 new IoT devices connect to the net.
  • The global IoT market is expected to be worth $1.7T in 2019. (Source: CBI Insights)
Worldwide Iot Active Connection Graph
Worldwide IoT Active Connection Graph

What is Machine Learning?

ML is one of the critical components of AI, where a computer is programmed with the ability to improve its performance. In short, Machine Learning is all about analyzing big data – the automatic extraction of information & using it to make predictions.

Netflix, Amazon, Google, and other E-Commerce platforms use it to bring semantic outcome. It is based on algorithms that analyze the user’s purchasing history to predict their preference. Machine learning is gradually integrating into all verticals, through automation of physical labor, we are improving the connectivity and shaping the future of AI and the IoT.

What is the Industrial Internet of Things (IIoT)?

The Industrial IoT or Industry 4.0 or the 4th industrial revolution are the terms generally used for IoT technology in a business setting. The concept is similar to the consumer IoT; to use wireless networks, a mix of sensors, big data and analytics to optimize industrial processes.

IoT devices provide information and analytics to connect the world of hardware devices and high-speed internet.

We can separate the Industrial IoT into two main categories:

  • Industrial IoT- The Industrial IoT, connects devices and machines in sectors like healthcare, transportation, power generation, etc.
  • Commercial IoT– Commercial IoT sits between industrial IoT and consumer and shares aspects of both.

How IoT (Internet of Things) and Machine Learning affect our life?

IoT and ML are improving the way we live and communicate in our lives. For instance, the AlterEgo headset easily responds to our brainwaves to control appliances and on the other hand, Alexa and Amazon’s Echo enables the voice-activated control of your high-tech smart-house.

This amalgamation of IoT and Machine Learning is changing various industries and the relationships that companies have with their clients. Businesses can easily gather and transform data into valuable information with IoT.

IoT is also transforming business models by helping companies to move from concentrating on products & services to companies that give the best outcomes. By impacting organizations’ business models, the blend of IoT-enabled devices & sensors with ML creates a collaborative world that aligns itself around results & innovation.

Challenges- IoT and Machine Learning

These days’ enterprises are flooded with data that comes from IoT devices and is seeking AI to help manage the devices. It is tough to manage and extract crucial information from these systems than we might expect.

There are aspects to IoT like data storage, connectivity, security, app development, system integration, and even processes that are changing in this space. Another layer of complexity with the Internet of Things has to do with functionality level.

Critical challenges that companies face with IoT and ML are with the application, ease of access, and analysis of IoT data. If you have a set of data from varied sources, you can run some statistical analysis with that data. However, if you want to be proactive to predict events to take future actions, a business needs to learn how to use these technologies.

Many firms are turning to the main cloud platform providers — for instance, Google, Amazon, Microsoft, Alibaba Cloud, or IBM. These companies offer a range of services to store IoT data and prepare it for data analytics, plus to train and run machine-learning models. They also assist in creating graphs, dashboards, and other simple-to-grasp layouts to visualize the information these models generate. Overall, IoT and Machine Learning are combined to provide high visibility and control of the wide range of devices connected to the Internet.

Solutions-How can we help you?

Futurists say ML (Machine Learning) and the Internet of Things (IoT) will transform business profoundly than the digital and industrial revolutions combined.

Are there some kinds of risks? Yes, as with any new technology, we have to accept both the profit and risks that come with mainstream adoption. We can do this with the confidence only when these technologies are tested against several odds.

One of the innovative solutions for seamless operation flow is IoT testing. There will be several other types of testing which require to be considered to cover the comprehensive functionality of IoT devices.

As part of ImpactQA’s Advisory Services, we also provide an implementation plan to help our clients improve time-to-market while keeping their business goals in mind. We use our assessment frameworks (like Chatbot testing framework, RPA Testing framework, etc.), based on industry best standards, focusing on processes, tools, and infrastructure.

Collaborate with our specialists to improve all QA areas – people, processes, tools, and infrastructure across the delivery life-cycle.

Learn More

How IoT and Machine Learning is changing the World?

  • What is Machine Learning?
  • What is the Industrial Internet of Things (IIoT)?
  • How IoT (Internet of Things) and Machine Learning affect our life?
  • Challenges- IoT and Machine Learning
  • Solution

IoT and Machine Learning are getting smarter. Companies are incorporating artificial intelligence (AI)—in specific, machine learning—into their IoT apps. From smart thermostats to wireless sensors, IoT devices are gradually but positively garnering mainstream adoption. Virtual assistants like Siri, Alexa, and Cortana are only making this technology easy to adopt.

The core purpose behind advancement in the IoT space is to help information move smoothly and seamlessly. For as much as we condemn technology, we can all recall a moment when the right message has appeared at the right time, with perfect user experience.

The truth of IoT and Artificial Intelligence – specifically machine learning – is far less sinister. It is shaping the way we live, travel, work, and communicate. In fact, it is shaping our lives smartly and the decisions we make.

The proliferation of smart IoT devices is shaping the future and gives instant access to the information world.

Let’s have a glance at these burning IoT statistics:

  • There are about 17 billion inter-connected devices in the globe as of 2018. With more than 7 Billion of these IoT (internet of things) devices. (Source- IoT Analytics)
  • According to McKinsey Global Institute, each second, 127 new IoT devices connect to the net.
  • The global IoT market is expected to be worth $1.7T in 2019. (Source: CBI Insights)
Worldwide Iot Active Connection Graph
Worldwide IoT Active Connection Graph

What is Machine Learning?

ML is one of the critical components of AI, where a computer is programmed with the ability to improve its performance. In short, Machine Learning is all about analyzing big data – the automatic extraction of information & using it to make predictions.

Netflix, Amazon, Google, and other E-Commerce platforms use it to bring semantic outcome. It is based on algorithms that analyze the user’s purchasing history to predict their preference. Machine learning is gradually integrating into all verticals, through automation of physical labor, we are improving the connectivity and shaping the future of AI and the IoT.

What is the Industrial Internet of Things (IIoT)?

The Industrial IoT or Industry 4.0 or the 4th industrial revolution are the terms generally used for IoT technology in a business setting. The concept is similar to the consumer IoT; to use wireless networks, a mix of sensors, big data and analytics to optimize industrial processes.

IoT devices provide information and analytics to connect the world of hardware devices and high-speed internet.

We can separate the Industrial IoT into two main categories:

  • Industrial IoT- The Industrial IoT, connects devices and machines in sectors like healthcare, transportation, power generation, etc.
  • Commercial IoT– Commercial IoT sits between industrial IoT and consumer and shares aspects of both.

How IoT (Internet of Things) and Machine Learning affect our life?

IoT and ML are improving the way we live and communicate in our lives. For instance, the AlterEgo headset easily responds to our brainwaves to control appliances and on the other hand, Alexa and Amazon’s Echo enables the voice-activated control of your high-tech smart-house.

This amalgamation of IoT and Machine Learning is changing various industries and the relationships that companies have with their clients. Businesses can easily gather and transform data into valuable information with IoT.

IoT is also transforming business models by helping companies to move from concentrating on products & services to companies that give the best outcomes. By impacting organizations’ business models, the blend of IoT-enabled devices & sensors with ML creates a collaborative world that aligns itself around results & innovation.

Challenges- IoT and Machine Learning

These days’ enterprises are flooded with data that comes from IoT devices and is seeking AI to help manage the devices. It is tough to manage and extract crucial information from these systems than we might expect.

There are aspects to IoT like data storage, connectivity, security, app development, system integration, and even processes that are changing in this space. Another layer of complexity with the Internet of Things has to do with functionality level.

Critical challenges that companies face with IoT and ML are with the application, ease of access, and analysis of IoT data. If you have a set of data from varied sources, you can run some statistical analysis with that data. However, if you want to be proactive to predict events to take future actions, a business needs to learn how to use these technologies.

Many firms are turning to the main cloud platform providers — for instance, Google, Amazon, Microsoft, Alibaba Cloud, or IBM. These companies offer a range of services to store IoT data and prepare it for data analytics, plus to train and run machine-learning models. They also assist in creating graphs, dashboards, and other simple-to-grasp layouts to visualize the information these models generate. Overall, IoT and Machine Learning are combined to provide high visibility and control of the wide range of devices connected to the Internet.Top 5 Mobile Application Testing Tools

Solutions-How can we help you?

Futurists say ML (Machine Learning) and the Internet of Things (IoT) will transform business profoundly than the digital and industrial revolutions combined.

Are there some kinds of risks? Yes, as with any new technology, we have to accept both the profit and risks that come with mainstream adoption. We can do this with the confidence only when these technologies are tested against several odds.

One of the innovative solutions for seamless operation flow is IoT testing. There will be several other types of testing which require to be considered to cover the comprehensive functionality of IoT devices.

As part of ImpactQA’s Advisory Services, we also provide an implementation plan to help our clients improve time-to-market while keeping their business goals in mind. We use our assessment frameworks (like Chatbot testing framework, RPA Testing framework, etc.), based on industry best standards, focusing on processes, tools, and infrastructure.

Collaborate with our specialists to improve all QA areas – people, processes, tools, and infrastructure across the delivery life-cycle.

Learn More

How AI Adoption Actually Bang and Turn QA Expectations?

Software testing industry is becoming extensive with every passing day. With the sudden boost in the technology challenges, apps are growing in complication which creates an incessant need for effective software testing. Software testing is the premeditated way where an app can be observed under definite conditions and where software testers can detect the risks [...]Learn More