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Testing Trends to Look At in 2020

Salute, Khabrovsk. In anticipation of the launch of the Mobile QA Engineer 2.0 course , we have prepared for you a translation of another material on testing trends in 2020.



The field of software testing is evolving day after day. We are witnessing the development of trends that appeared in 2019, as well as the formation of new ones. This year, our team of testing automation experts made several predictions regarding the main trends in software testing. We invite you to familiarize yourself with them!

To get an idea of ​​software testing trends in 2019, you can read our article .

1. Artificial Intelligence and Machine Learning in Testing


Based on the many reports received, it is safe to say that intelligent automation will remain on the radar of testers in 2020.

The use of artificial intelligence and machine learning is no longer a new idea in the field of software testing. AI makes testing more literate. Teams can use AI / ML to optimize their automation strategies, adapt faster, and increase work efficiency.

In 2019, quality assurance teams (QA) used AI / ML to predict the quality of tests, prioritize test cases, classify errors, detect test objects, interact with tested applications (AUT), and many other goals.

It is expected that artificial intelligence will be applied everywhere in all areas of information technology. Investments in this area are approximately at the level of 6-7 billion dollars in North America alone. According to forecasts, by 2025, investment will reach almost $ 200 billion. We expect to see the use of AI in a large number of testing areas, of course, many of them relate to reports and analytics:

  • Log analytics: Identify unique test cases that require manual and automated testing.
  • Test Suite Optimization: Detect and eliminate redundant, useless test cases.
  • Providing Test Requirements Coverage: Retrieving keywords from the Requirements Tracing Matrix (RTM).
  • Predictive analytics: Predicting key parameters and specifics of user behavior, as well as identifying areas of the application that you should focus on.
  • Error analysis : Identification of areas of the software product and errors that are associated with business risks.

Another pillar on which intelligent automation is based is machine learning. In 2020, ML will enter a new level of application. According to the Capgemini World Quality report, 38% of organizations planned to implement machine learning projects in 2019. Industry experts predict that this figure will increase this year.

What does this mean for organizations?


Despite the growing demand for AI and ML in testing, experts still believe that these technologies are only emerging in testing. However, very soon we will see their growth.

As AI places new demands on quality assurance teams, Agile teams need to start introducing AI skills, which include learning Data Science, statistics, and math. New skills will not replace, but complement basic skills in the field of automated testing and development (S-DET).

In addition, business acumen will be another important skill. A good tester should combine good AI skills and exceptional skills. Last year, new posts appeared, such as AI QA Analyst and Data Analysis and Testing Specialist.

As for the developers of automation tools, their task is to focus on creating the most practical tools.

Companies test concepts and revise the traditional way of working to best implement AI while taking budgets into account. A good tool with support for artificial intelligence should provide both economic business efficiency and technical aspects, such as viewing production logs, generating test scripts or responding to activity on production.

2. Test automation in Agile teams


Test automation is far from a novelty in the field of quality assurance. Indeed, 44% of IT companies planned to automate more than 50% in 2019 . According to the forecast, in 2020 the percentage of implementation of automated testing will only grow.

As more and more companies implement the latest Agile and DevOps processes to deliver a quality product as soon as possible, test automation is becoming an integral part of this system. Test automation is leading the way in helping teams complete repetitive tasks, quickly detect errors, provide continuous feedback and complete test coverage. Thus, organizations implementing automated testing in their quality assurance processes can save a significant amount of financial, time and human resources.

It is expected that test automation in 2020 will be especially welcomed by millennial entrepreneurs who use a combination of open and paid tools.

What does this mean for QA professionals?

One way or another, test automation does not preclude completely manual testing. In fact, good QA teams should properly combine manual and automated testing in order to maximize efficiency and ensure proper software quality. The importance of automated testing is hard to argue, but some types of testing, such as exploratory or usability testing, still need to be done manually.

In addition, QA professionals need to develop a smart overall and cross-cutting environment. Increasingly, there is a need to automate processes from assembly to deployment. Test automation is now considered not as a functional requirement, but as an integral part of the product life cycle.

Easier said than done. That is why many organizations could not get the most out of automation and get the desired return on investment. There is a recommendation in the Capgemini World Quality report that instead of seeing automation as an opportunity, testers should think of it as a large, intelligent, and connected platform.

What does this mean for test automation solution developers?

Test automation tool developers must constantly update and upgrade tools to ensure that their solutions meet the requirements of quality assurance teams. Automated testing solutions for the future must meet certain criteria, for example:


3.


Big data plays an important role in various market sectors, be it technology, healthcare, banks, trade, telecommunications, media, etc. More and more attention is paid to the use of data for segmentation and optimization of decision-making processes.

Testing big data allows industries to work productively with large volumes of data and their various types. It also helps to make better decisions through accurate data validation, and also improves the marketing strategy. Such testing is no longer new. Exponential growth is expected in this area, as many industries are moving to a data-driven approach.

The trend of Big Data testing is widespread, mainly due to the reliability of the processes that most companies follow to get the most out of their marketing strategies. Testing big data is not uncommon and its popularity is growing. We predict that the need for testing big data applications will only grow in 2020.

4. QAOps: Quality Assurance and DevOps Transformation


If you have not heard about the term QAOps, then this is only a matter of time.

You may already be familiar with DevOps, a set of methods for software development that combines development (Dev) and operations in the field of information technology services (Ops). DevOps' main goal is to simplify the System Development Life Cycle (SDLC), while teams can focus on creating new features, fixing bugs, and frequent updates that meet business goals. DevOps streamlines collaboration between developers and business representatives.

Similarly, QAOps helps to increase the flow of direct communication between testers and developers, by integrating testing into the CI / CD pipeline, as well as ensuring the work of testers in a team with others. Simply put, QAOps is based on two main principles:

  1. CI/CD.
  2. CI/CD.



Facebook is one of the best examples of implementing QAOps. In 2014, the Facebook team decided to switch to Facebook Graph API 2.0 and force a Login Review in all applications. To ensure smooth migration, the team wanted to test the new version on the 5000 largest applications. In-house testing turned out to be impossible, so they decided to use outsourced QAOps. In the end, the team was able to test more than 5,000 applications in one month and managed to solve critical problems, which would not have been possible if only Facebook’s own team had been involved in this process.

QAOps can be used not only in global technology companies, but also in medium and small teams. This practice scales well to the size of any business.

As more and more teams gravitate toward DevOps, we expect to see an uptrend in QAOps in 2020.

5. IoT Testing


The growth of testing the Internet of Things (IoT) devices was noted back in 2019 . According to Gartner, in 2020 the number of Internet of Things devices should reach 20.5 billion.

IoT testing is testing of Internet of Things devices for security, ease of use, reliability, compatibility of device versions and protocols, versatility of software elements, monitoring of connection delay scalability, data integrity assessment, device authenticity, etc.

IoT testers often face tremendous amounts of work in this area, especially monitoring communication protocols and operating systems, as well as numerous combinations of various elements of the Internet of Things system. For this reason, the team of testers must continuously expand their knowledge, improve the level of skills in the field of usability, security and performance of IoT testing.

Another issue that IoT testers will face in the coming years is strategies. Despite the fact that the number of Internet of Things devices and applications is growing exponentially, 34% of respondents said that despite the presence of Internet of things modules in their products, their quality assurance teams still do not have an established testing strategy, as noted in the World Quality Report .

6. Cybersecurity and risk control requirements


The digital revolution is driving up security threats. CIO and CTO of almost all companies in many industries recognize the importance of testing the security of their software, applications, networks and systems. Software development teams work with partner companies to help provide their product with the right level of security.

Security testing provides not only transaction security (regardless of whether it is money or data), but also the protection of users' personal data. Since cyber threats can occur at any time and in any form, security testing will remain a popular topic in the future.

Conclusion


Here is a list of our forecasts regarding the main trends in software testing in 2020. No matter how the digital transformation takes place, there is no doubt that testers and software companies will continue to make new changes and adjustments. As a result, quality teams, leaders and professionals must constantly evolve in order to remain flexible in this ever-changing industry.


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