Modern quality engineering: Evolution and the road ahead

In today's rapidly evolving technological landscape, ensuring the quality of software and products has become a critical factor in achieving business success. Quality engineering has undergone significant transformations to keep pace with the demands of the digital age. In this article, I have put together some of my thoughts  about the evolution of modern-day quality engineering practices and the direction it is taking to meet the challenges and opportunities of the future. The driver behind these thoughts are the inputs from the multitude of customer projects I have handled at Nagarro along with thousands of customer discussions and hundreds of conferences I have attended over all these years.

The testing challenges are immense. Large organizations will and are already competing using advanced analytics and predictive algorithms with a lot of new investment going to predictive analytics and data science. Technology oriented business intelligence competency centers are being set up in many enterprises to provide AI driven solutions. Thousands of integration points between hyperconnected heterogenous systems creates uncertainty in test coverage. There is increased pressure to release apps more quickly with technology leaders exploring possibilities of using power of AI in test solutions. All of this requires testing approaches different than traditional software applications testing where the tester is expected to test probabilistic logic instead of deterministic and work more on algorithmic models.

While there are many testing challenges, they come with many opportunities as well. Evolutions of technologies have supported the creation of Generative AI (Gen AI) models. The quality engineering experts can learn the working of these models and use them to create new productive solutions for faster and efficient testing solutions. The efficiency of these models can be applied in test automation, performance testing and in many other flavors of testing. These models open a wide window of opportunity for learning and creativity. in innovative testing solutions. Beyond Gen AI, the AI based algorithms can support performance engineering, predictive user experience testing, defect prediction analysis, self-healing of automation frameworks and many more.  

Let’s take a quick look at how quality engineering has evolved over the years.

The early days: Manual and reactive

Traditionally, quality engineering has been associated with reactive practices, where testing and bug fixing occurred towards the end of the development process. Most of the quality engineering effort was manual. However, as software complexity increased and time-to-market pressures intensified, this approach proved to be inadequate. The need for a more proactive, holistic, and continuous quality assurance process became all too pervasive and evident.

The medieval days: Proactive, agile and DevOps influenced the way we work

The advent of Agile methodologies and the subsequent rise of DevOps brought about a paradigm shift in quality engineering. Agile practices emphasized iterative development, frequent feedback loops, and collaboration, allowing quality assurance to be integrated throughout the software development lifecycle. Quality engineers started working closely with development teams, implementing test-driven development and continuous integration strategies. DevOps further accelerated this evolution by emphasizing the integration of development and operations teams. Quality engineering became an integral part of the DevOps pipeline, ensuring that software quality was maintained consistently across all stages, from code commit to deployment. The focus shifted from manual testing to test automation, enabling faster and more reliable release cycles.

Modern days: Proactive, intelligent, and user-centric

The world today is high on demanding quality. Quality is no more a second thought, in fact business wants quality to be built in each step of product development with ability to release high quality software anytime. The tester must be more proactive in thinking of quality from day one and not just think about product functionality and requirements but also look at the application from end user perspective. End user is playing more central role in quality engineering approaches. Many quality solutions have already started to use power AI for better efficiency and optimization.

• Shift-Left and Shift-Right Testing: To improve the quality engineering process even more, the industry introduced the concepts of "shift-left" and "shift-right" testing. Shift-left testing involves bringing testing activities early in the development process, emphasizing early bug detection, and preventing issues from escalating. It includes techniques like unit testing, static code analysis, and continuous testing. This approach reduces rework, expedites development cycles, and enhances overall product quality. On the other hand, shift-right testing focuses on testing in production or real-world environments. It involves capturing and analyzing user feedback, monitoring performance, and conducting exploratory testing. Shift-right testing helps identify issues that may have been missed during development, gather valuable insights, and continuously improve the product based on user experiences.
• Shift to Cloud: The migration of software and infrastructure to the cloud has impacted quality engineering practices. Cloud-based testing environments enable scalability, rapid provisioning of resources, and on-demand infrastructure, making it easier to perform complex and large-scale testing. Cloud-based solutions also facilitate collaboration and ensure consistent testing across distributed teams. Automated environment provisioning became more mainstream.
• Focus on User Experience: Quality engineering is increasingly aligning itself with user-centric design principles. Usability testing, user feedback analysis, and performance monitoring play a crucial role in ensuring an exceptional user experience. Quality engineers collaborate closely with UX designers and product owners to deliver products that are not only functionally robust but also delight users.
• Focus of data security: With the world going remote in the new normal, it became vital for enterprises to secure the publicly exposed website and data. Security testing has gained a lot of traction in recent years.
• Internet of Things: Testing of intelligent digital mesh, with many devices connected to each other and communication together. HIL (Hardware-In-Loop) became more mainstream, and testing included both hardware and software together.

Futuristic days: Direction in Quality Engineering

While the above-mentioned modern-day trends will continue to drive business, there are some other significant trends as well, leading to re-skilling and learning. This is not exhaustive and could be more areas as the world is technologically changing very quickly with different experts interpreting the changes in their own intelligent way many times.

• Generative AI and Data Science: This is revolutionizing quality engineering. AI-powered test automation frameworks can autonomously generate test cases, perform predictive analysis, and identify areas of the application that require more testing coverage. Quality engineers will use more and more generative AI, LLM (Large Language Model) tools such as ChatGPT to become more productive and innovative. Quality engineers will learn more about data science.
• Prompt engineering: To avoid “AI hallucinations”, the quality engineering expert will learn more and more about Prompt Engineering. The industry will also have tools around prompt engineering. With accurate prompts, the engineers will be able to use LLMs (Large language Models) as “Recommendation Engines” for better solutioning.
• Need to think out of the box: The testers will still do manual but only in those areas which require out of the box and deep contextual thinking. They will have to be more innovative and bring out of the box ideas. All other tasks will most likely be taken care by AI models.
• Next gen test automation: Test automation in all areas, Web, APIs, IoT ecosystem and more, will continue for some more time till the AI based tools gain more maturity and traction, however next gen test automation and generative AI tools will see larger integration leading to higher productivity of quality engineers. We will see next gen automation working with unstructured data (not just structured data, as in most cases today), using more algorithmic models and be more and more fast.

Conclusion

The evolution of modern-day quality engineering is driven by the need for higher productivity, faster time-to-market, improved software quality, and enhanced user experience. At Nagarro, we have been getting so many requests from customers to use Gen AI in test automation solutions and a lot of progress has already been made. On the same lines, testing the application from the user’s perspective and not just from functionality perspective is becoming the key.

The engineering experts of Nagarro have also written a white paper about shaping the future of quality. For more inspiration, check out our Quality-portfolio.