Software Testing Trends to Follow

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SoftwareSuggest

Senior editor

Parul Saxena

Chief editor

Last updated: May 2, 2021

Before hitting the market, any software product must pass through the hands of a test engineer. These unsung heroes of the software development industry are the ones to study the requirements for the product, perform numerous checks, localize and describe all kinds of defects. In other words, they bear the responsibility for the product’s quality.

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However, the information technology is subject to constant changes, and so do the roles of various participants in the software development cycle. As more and more tools and approaches appear, there is a need to acquire new skills in order to stay professional in this particular field. This way, let’s discuss some trends that have emerged in the testing world in 2018. Technical details of the article are contributed by Elinext testing department. We won’t touch in detail much-hyped topics like artificial intelligence (AI), but we’ll consider some other areas of testing worth your attention.

Latest Software Testing Trends to Watch in 

1. Big Data

Big Data has been around for years. Surprisingly, testers know little about it, although they should. After all, the process of testing is directly related to data. So what is big data? Definitions may vary, but they all boil down to the fact that Big Data is associated with volume and processing. Previously, test engineers used to deal with megabytes and gigabytes, and now it’s all about terabytes and petabytes. However, the number of zeros isn’t a crucial factor in understanding its principles.

Big data is associated with the so-called four “v’s”:

  • volume;
  • velocity;
  • variety;
  • veracity;

The problem with big data is that it is poorly structured in 90% of cases. Most of the data is derived from social networks: calls, messages, search requests, pictures, PDF-files, and other information. This way, testing large data involves both functional and non-functional checking. Functional testing consists in analyzing the quality of data for further processing. Non-functional types of testing include load and volumetric testing. However, each type of testing requires the tester to know the system and the ability to research the product. Besides, the testing of large data often requires testing automation.

2. The growth of DevOps

It would be fair to say that DevOps isn’t a new concept for business but its emergence in the IT sector is quite fresh. It has gained an enormous amount of support from various businesses in the past five years.

DevOps is a set of practices which allows saving loads of time in the development process. According to a study by cloud-management provider RightScale, the ratio of enterprises that have adopted some aspect of DevOps principles reached 84% in 2017.  It’s fair to say that in 2018, more and more organizations will implement the practices of DevOps. More and more practitioners are used to support automated testing and continuous integration (CI).

Robert Stroud from Forrester Research states that “although many organizations are in the experimentation stage with single or multiple pilots, they all are transitioning toward DevOps across their entire enterprise.”

3. The share of automated testing will increase

Although testing automation is an integral part of DevOps, at the moment it covers no more than 20% of all testing activities according to the World Quality Report 2017-2018. The majority of organizations still focus on functional UI and regression testing.

The automation testing is considered as the primary approach to reducing testing and layout time. Its ability to integrate with DevOps tools becomes mandatory for autotest tools. Most large open source and free tools like Selenium and Katalon and commercial tools Ranorex and TestComplete are now supported by integration with Jenkins, Git, and Jira.

4. Use of manual and automated testing

Although automation is regarded as a key area nowadays, manual testing is still dominant in the testing industry, and this situation makes it difficult to solve problems associated with continually cutting cycles of calculations and complex test environments. As for now, correct testing strategies combine both manual and automated testing. So it’s safe to predict that these two types of testing will continue to co-exist for the next few years.

The variety of artificial intelligence and machine learning practices serves to increase the productivity of software teams and the quality of programs themselves. Taking into account the latest achievements in the field of AI and ML, there will appear more intelligent testing automation technologies and tools for drawing test cases, test data and reusing test scenarios. They will also help in developing test scenarios, predict application behavior, areas and test levels. Intelligent testing tools should offer intelligent analytics to better diagnose errors and visualize testing results through a variety of sources.

In general, automation is now centered around the user interface. And testing of APIs and services remains with developers and is performed manually. The trend is to extend the use of API testing automation and services, rather than testing UI. Typical testers equipped with smart and easy-to-use tools will be responsible for testing APIs and services, helping to reduce product delivery time and improve quality.

5. Increased automation for mobile platforms

As we’ve already stated, the current use of test automation is relatively low. The share of mobile automation is even lower. The fact is there is a shortage of correct methods, tools, and devices to automate mobile applications in full measure. However, the necessity is growing as the gradual shift from the desktop and web applications to mobile ones requires organizations to increase the use of test automation for mobile applications. New testing platforms and tools like Kobiton and Sauce Labs offer excellent opportunities for these purposes.

6. Shift Left testing with TDD & BDD

Thanks to the automation of configuration management to improve performance there is a chance that the shift left trend of testing will also be one of the best solutions. Shift left testing is the beginning of the testing process at an early stage of development without waiting for the development processes to be completed to identify and report errors.

An example can be the launch of test processes directly from the requirements definition phase to prevent bugs from getting into the code and to prevent the occurrence of problems related to defects. With shift left testing, companies are more likely to benefit from a cost and time perspective, since the more time it takes to detect errors, the bigger the repair price is.

Companies are also more inclined to use TDD and BDD to deliver applications according to customer expectations without any defects or delays. Accepting the methodology of TDD and BDD, the team can get a real idea of what is required, and whether there is a misunderstanding from the requirements stage.

7. Integration

To support smart testing and analytics, you need to collect data from different sources at different stages of development, such as requirements management systems, change management systems, task management systems, and test environments. In the nearest future, we will see testing and management automation tools that offer functions for integration with various sets of ALM tools and test environments. Such integration allows you to make more informed decisions about testing and software quality.

Bottom Line

The technological evolution in the software testing domain makes both the QA and testing engineers revisit their skills and expertise. However, these advancements and technological updates are not solely about testing specialists – it is a challenge for a company’s entire software development team.

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