Why QA Engineers Must Learn the Latest Technologies to Level Up

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Why QA Engineers Must Learn the Latest Technologies to Level Up

It's crucial that QA engineers stay up to date on the latest software trends.

· Performance Zone ·
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To stay ahead in the game, you need to evolve according to the changing times. This formula applies to everyone no matter what they do or where they are.

If we talk about QA engineers specifically, it is highly crucial for them to learn the newest technologies in the block: AI, IoT, Machine Learning, and blockchain to name a few.

In this day and age, the buzzword for every business’ success is “Customer." The happier the end-user is, the bigger are your chances of success. According to the recently published World Quality Report 2018-19 (WQR), enterprises are emphasizing more on enhancing customer experience, and for achieving the target goals, they are investing more in Artificial Intelligence (AI) and Machine Learning (ML) to optimize their testing efforts to the maximum degree.

If you study the report, you’ll notice that 99 percent of the participants in the study claimed to be using DevOps practices. DevOps brings in maximum value to your users in a minimum possible time, without compromising on the quality as well as security. DevOps involves automation to fire up the testing process.

Let’s study the noticeable findings in this year’s WQR and identify some ways that enterprises can adopt for paying close attention to why their QA teams need to enhance their skills while keeping customers satisfied.

Artificial Intelligence

Artificial Intelligence (AI) is an evolving technology that’s vastly technical in nature and, subsequently, requires a certain level of expertise to leverage properly.

Nonetheless, WQR indicates that many respondents are either in the process of adopting AI or are in the planning phase to apply it perhaps in the coming year for their internal processes (62 percent), QA purposes (57 percent), and their customer processes (64 percent).

However, it’s easier said than done. Even though they are completely aware of the significance of AI and do wish it apply it, the report shows that there are more than half of the respondents who are struggling with trying to understand where they can bring in AI, while there are many who experience trouble in integrating AI within their existing applications.

This clearly shows that there is a snowballing need for experts who have developed technical and mathematical skills and can help organizations execute these technologies within their work processes.

The Evolution of Roles

Searching for professionals with these skillsets isn’t as easy as it might seem, which is why adoption of AI techniques will take a while for the majority of organizations to adopt.

The World Quality Report reveals the evolution of new roles in QA and testing, which encompass:

•    AI QA strategists for understanding the method of applying AI to business processes using their business and technical know-how.

•    Data scientists who will scrutinize the test data and use predictive analytics, mathematics, as well as statistics for building models must have a thorough understanding of data analysis techniques and are completely aware of using them.

•    AI testing experts who will merely focus on testing of AI applications, apart from having traditional testing knowledge, should be aware of machine-learning algorithms and natural-language processing techniques.

Emphasis on Other QA Skills

The report also identifies that progressing trends including AI, Internet of Things (IoT), and blockchain all involve diverse skills and, hence, creates new roles for QA and testing professionals. The implementation of IoT technology is growing to the extent that approximately 97 percent of participants in the survey mentioned that they use IoT for several personal, as well as official business-related activities.

IoT devices can detain a large amount of data, which can either be shifted to the cloud for processing or processed partially or entirely on the device itself, in other words, edge computing.

Approximately 66 percent of participants agree to be using blockchain technology or have future plans to adopt the technology in the coming year or so. Since blockchain is not exclusively limited to Bitcoin or cryptocurrency, rather, it is becoming a part of many systems that entail a secure, distributed ledger for maintaining a transactions record.

However, implementations and executions of every new technology must focus on account security, data risks, as well as mitigate any threats associated with integration into other systems.

Hiring people with these skills cannot only be challenging but time-taking as well. Following what the report suggests, enterprises should focus on adding skills to their existing workforce. It presents a four-stage approach:

  • With automation being an essential skill for QA, look for ways to train your QA engineers to add agile testing with their automation skills.

  • Further work on enhancing the automation skillset by adding software development engineers in QA. This will assist in maintaining automation throughout the development pipeline using advanced skills and development techniques for more efficient testing.

  • Make sure that the team has abilities in handling security testing, functional testing, non-functional testing, various test environments, and can also handle data management.

  • Bring on board QA experts with AI skills for next level QA testing.

Prognosis for Testing and QA

The future is bright for QA engineers. Customers are a top priority, and to be able to conquer new work dimensions, QA professionals need to master new technologies such as AI, IoT, and blockchain for the best possible customer experience.

AI, IoT, blockchain, performance, qa, qa engineers, qa testing, quality assurance, testing, trends

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