5 QA Trends for 2021

In 2020, we watched the world change in ways that no one could predict. We’ve seen how quickly businesses can move into a new reality. Educational institutions and gyms have launched online classes in order to keep their businesses alive. Going to the museum or watching a play now happens almost entirely on a virtual platform. The new normal for families is to order dinner from dozens of restaurants that existed offline only yesterday; and now these restaurants are competing for our attention on every delivery food app on the market.

The future is now all innovation in both production and business processes, in order to adapt to the changes that Covid-19 has thrown at us.

The trend of hiring QA specialists (or QA specialists) early in the software development lifecycle, has worked for the IT strategies set forth by many companies. It has given many businesses an edge in helping maintain a competitive advantage and high revenue level.

So how does a business effectively organize the software testing team in 2021, in order to minimize the effects of the economic recession and maintain market leadership?

So, let’s talk about 5 QA trends for 2021, which can help anyone release a successful software in today’s market:

  • 1. Ensure a qualitative transition to Agile and DevOps methodologies.
  • 2. Optimize QA processes by introducing artificial intelligence.
  • 3. Apply test automation.
  • 4. Improve test data and environment management techniques.
  • 5. Revise QA budget allocation.


User behavior has changed over the past year. Users have become more careful in their choice of software products due to their diversity. This has largely determined the vector of software development and testing.

The shift from the traditional waterfall model, to Agile and DevOps methodologies continues to gain momentum. More and more companies are looking to reduce software go-live times and improve software quality. Despite the trend of merging QA processes and development, 42% of organizations surveyed in the World Quality Report (WQR) 2020-2021, noted that there is a competency gap in QA teams adopting agile approaches. 58% of IT business representatives have identified the selection of test automation tools as a key challenge as well.

To avoid such difficulties and ensure a painless transition to Agile and DevOps practices, it is necessary to:

  • Automate software testing to optimize QA activities.
  • Involve specialists with a certain skill set in both testing and development.
  • Conduct continuous monitoring of production logs to improve usability and identify defects early in the software development lifecycle.
  • Use automated quality dashboards to ensure high process visibility.


Keeping at the top of the list of trends over the past couple of years, artificial intelligence (or AI) may soon become a technology that will be prioritized in a number of industries. Forrester researchers note that in 2021, companies will see massive use of AI. One-third of businesses with adaptive methodologies will begin investing in AI to restore work ecosystems, set up internal processes, and seamlessly return employees to the office amid many other things.

Artificial Intelligence can play an important role in the QA sphere as well. About 90% of WQR respondents said that this is the kind of innovation they want to invest in.

Despite the current difficulties associated with the lack of experience and skills of QA teams to fully implement and maintain AI-based solutions, companies are already adopting new approaches to effectively adapt this technology in QA.

Also, in order to intelligently predict software quality levels and plan QA workloads, organizations are analyzing production incidents, identifying gaps, and generating test data to increase test coverage.

Testing of complex AI-based systems is slow but steady. For example, eHealth product manufacturers are developing standards that test AI-powered algorithms, and the automotive sector is using them to test advanced driver assistance systems.

The bottom line is that effective AI-enabled testing requires proper implementation of tools, as well as staff development, to achieve business goals in a short timeframe.


Despite frequent changes in application functionality during releases, companies are increasingly favoring test automation. Comparing last year’s and this year’s numbers, the number of IT representatives who have optimized their work on projects by implementing automation has increased. Test activities have become more transparent, while time to market has decreased, QA costs have also decreased, although the risk of cybersecurity issues have increased.

However, companies still face challenges; for example, below you can see the levels of adaptation of test automation in a number of aspects from a World Quality Report from 2020-2021.

Only 37% of companies get a return on their investment in this service. To make it easier to maintain voluminous automated test suites and achieve maximum scalability of checks, it is possible to move to script less automation tools.

Lack of expertise is another challenge that many companies face. It directly affects long-term strategy planning and the overall effectiveness of QA processes. This problem can be addressed by recruiting experienced professionals with advanced development and test automation skills, knowledge of AI, MO, APIs, and micro services.

By recruiting such talented specialists on a project, identifying the most appropriate and intuitive toolset, and relying on artificial intelligence and machine learning to solve technical problems… organizations can achieve the desired level of test automation, regardless of frequent application changes.


According to the World Quality Report, in 2020, IT reps were less likely to test software in on-premises environments. Instead, they were moving to the cloud and running Docker containers or using similar technologies. This trend came at the expense of the massive digital transformation brought about by the global situation.

Nevertheless, to get the maximum benefit by migrating to the cloud, it is necessary to organize effective management behind the QA teams. This will help avoid contingencies (such as high costs) due to untimely software releases.

When it comes to test data management, nearly 80% of WQR respondents use manually developed data for each launch. Which is 20% higher than the same parameters from previous years. It is likely that the creation of large data sets is related to meeting the needs of continuous automation.

To reduce the QA team’s time to create the right test data to help compile business requirements, companies can implement time division multiplexing.


The events of 2020 have impacted many companies’ IT budgets; reducing travel expenses due to remote work, investing in new technologies to set up internal processes outside the office, and saving money by moving to the cloud infrastructure.

So what areas are worth investing in in 2021? WQR states that organizations need to focus on the two most important ones:

  • Invest in technologies with great potential, such as artificial intelligence or test automation, to save costs in the future.
  •  Attract in-demand employees to QA teams, especially those with deployed software development and testing skills.


For many companies, the previous year was not an easy one; they had to adapt quickly to new conditions and change the focus of their QA strategy.

In the new year, in order to maintain a leading position among competitors, it is important to implement DevOps practices, test automation and AI, focus on improving test environment and data management, and pay attention to optimizing the QA budget, which has changed dramatically due to the im

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