The Challenge of QA Engineering in the 2020s

The first quarter of the 20^th century witnessed advancements in the field of industrial engineering that facilitated the rise of America’s industrial economy. That era saw manufacturing advancements for emerging industries such as the automobile and the telephone. However, in the first quarter of the 21^st century, it is the service sector that is most dominant in the economy and, within that broad category, the digital and technology sector is of paramount influence. But whereas industrial engineering was the revolutionary technical discipline of a century past, quality assurance (QA) engineering is now of critical influence in today’s digital era. And the profession faces some key challenges through the balance of the decade.

According to the technology research firm AI Multiple, “the number of software developers is expected to exceed 28 million by 2024.” Additionally, their research suggests that, in 2021 alone, more than 2 million mobile apps were released. As the aforementioned market data indicates, the digital economy has witnessed an explosion of software applications for education, entertainment, healthcare and other uses. This is the backdrop for the mission of QA. And to ensure that software releases perform adequately in terms of functionality and security, they must be tested rigorously.

For the design and development of digital products such as apps, QA offers a variety of benefits such as improved product delivery, customer satisfaction, and improved user experience. The successful development and marketing of useful apps can be the foundation of new startup ventures with the potential of growing into billion dollar enterprises or new product lines of existing companies. Yet, the QA profession faces various challenges as it provides its mission of key support in app development.

One such challenge is ensuring that apps are compatible with multiple devices. PCs, Apple notebooks, tablets, smartphones, and even smartwatches—there is no shortage of devices which consumers may wish to see host a valued app. As application development is migrating from native app development to hybrid development, this challenge is accelerating. The term native app development refers to building a mobile app exclusively for a single platform. That means an app is built with programming languages and tools that are specific to a single platform. For example, you can develop a native Android app with certain programming languages or a native iOS app with other languages. This explains why QA engineers need to be familiar with multiple programming languages. But it is time-consuming as well as costly.

Contrast this with cross-platform development which involves the process of creating an app that works on several platforms. This is done by using tools like React Native, Xamarin, and Flutter, where the apps created can be deployed on both Android and iOS.

Cross-platform development does save time and cost. However, it risks sacrificing quality. It is difficult to tailor an app that runs optimally on multiple platforms, and the app will need an additional abstraction layer when running, thus resulting in lower performance. The drawbacks of QA for cross-platform development include slower app performance and limited functionality-limitations that the QA process must address.

Perhaps an even more arcane challenge is the role of artificial intelligence (AI) in the QA process. To make app testing smarter, more effective and more efficient, organizations are adopting artificial intelligence-based tooling and processes. This is consistent with the general growth of AI systems in a wide variety of devices and applications such as modern cars, corporate recruiting and the military.

AI-based testing is a software testing technique in which AI and Machine Learning (ML) algorithms are used to effectively test a software product. The objective of AI-based testing is to make the testing process smarter and highly effective. With the inclusion of AI and ML in testing, logical reasoning and problem-solving methods can be applied to improve the overall testing process.  Further, in this testing method, AI testing tools are used to execute the tests that use data and algorithms to design and perform the tests without any human intervention. That does not eliminate the need for QA engineers; it simply complements the tools available to those professionals.

Testing is time-consuming, and AI can be used in testing tools to increase the efficiency of testing. However, the effective use of AI entails some challenges. AI models require a substantial amount of high-quality data before they can be used. Otherwise, we face the classic risk in system design of “garbage in, garbage out.” There are other issues associated with the use of AI in a QA environment including its expense, which is a tradeoff against its various benefits including speed in test execution.

QA engineering operates behind the scene in areas that are invisible to the consumer. Whereas a factory worker in a 20^th-century auto manufacturing plant could visibly detect the defects in a component moving on the assembly line, the QA engineer operates in the digital world that is opaque to the untrained eye. And as digital products and services expand in how they impact our daily lives, the field of quality assurance must be ready to ensure that those services function as intended.

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