A Way of Continuous Improvement: Alexander Motylev on the Growing Role of AI and ML in Testing and Quality Assurance

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The recent advancements in AI and ML technologies proliferate into multiple aspects of software development, so testing and quality assurance are not excluded. According to a survey, whose results were recently published by DevOps.com, 49% of respondents reported their organization is applying AI to application testing in one or another form, and another 21% plan to do so in the next six months. They expect AI-based automation tools to increase productivity and end revenue while reducing the number of vulnerabilities and bugs in their applications. Alexander Motylev is currently a Data Test Engineering Director at Paramount Global/Pluto TV and an IEEE and ACM member with extensive experience in implementing pioneering automated solutions in testing and QA. He shares some insights into his projects and his view on the growing impact of AI and ML in the industry.

Throughout his career, Alexander Motylev has worked on several projects taking positions within the field of QA management, QA engineering, and data test engineering. His most significant work includes his roles at Paymentus, an online payment processing system, and, since 2022, at Paramount Global. He notes that his ability to adapt to rapid technological changes played an important role in advancing his career. “When new solutions based on AI and ML emerge in rapid succession, one needs to learn constantly while keeping up with their day-to-day tasks and responsibilities at the same time,” he adds. This commitment to continuous learning is a cornerstone of his approach to achieving continuous improvement in his work. 

Alexander’s master’s degree in Mechatronic Engineering laid a solid foundation for his career, but he didn’t stop there. He continues to actively pursue learning, always striving to expand his knowledge and skills, particularly in recent technological advancements. Over the past few years, he has completed certifications from IBM and Google.  He also plays an active role in educating the next generation of QA and data analytics professionals, providing training sessions and workshops for teams within various organizations, and demonstrating the most productive ways to implement AI/ML in practice. 

Alexander Motylev emphasizes that the role of testing and QA becomes crucial when sensitive data is involved, as the cost of failure is extremely high. At Paymentus, he led the development of a tool for automating data testing for processing online payment data, which inspired him to automate a routine data testing process prone to human errors. “The project of creating a tool for automating data testing for processing online payment data inspired me to automate a routine data testing process, which was prone to human errors,” he explains. AI technology can provide immense benefits in such cases, allowing us to prevent errors and quickly detect suspicious behaviors. The development of the test automation framework enhanced the effectiveness of software testing and helped to ensure the reliability of processing payments, demonstrating continuous improvement in both security and efficiency. 

There are multiple instances of applying such an approach in practice in his career, with the most prominent one being the development of the AI/ML- Based Data Processing, Analytics, and Quality Assurance Tool to Boost Operational Efficiency for Video-Streaming Companies Across the United States. This initiative leverages Alexander’s extensive expertise in software quality assurance management and data test engineering to significantly enhance industry standards and operational efficiency in the U.S. video-streaming sector. The tool, developed during his tenure at Paramount Global/PlutoTV, captures data about online viewership, generates and analyzes reports, and detects data anomalies with the eventual result of improved production quality and preventing production incidents. Currently, Alexander Motylev continues to enhance the tool, integrating AI/ML forecasting and predictive analytics into the framework, exemplifying continuous improvement in action. 

However, to apply advanced technologies efficiently, a company or a team needs a deep understanding both of company operations and the current state of AI/ML. “There are several ways AI and ML technologies get integrated into the testing and QA processes,” notes Alexander Motylev. “They are used for data collection, analytics, and creating predictive models. However, in any case, they have to be tailored to the needs of a particular company.” Alexander’s experience leading onshore and offshore QA teams and implementing QA best practices and methodologies has given him unique insights into how these technologies can be best utilized. While it may seem tempting for a business to pursue innovation for its own sake, one needs to clearly understand, for what purpose new methods and technologies are being implemented, especially in the processes that have a high impact on the quality of the final product. 

Moreover, it is crucial not to concentrate on the most obvious solutions to unleash the full potential of an innovative technology. “On a current level, AI technologies can be applied in multiple ways, so it is important not to concentrate on one already working idea, but try out multiple ones,” highlights Alexander Motylev. “ This philosophy has guided his participation in innovative projects such as the 2024 Innovation Fest, an event aimed to recognize innovations in Paramount Video Engineering, where he and his team developed Paramount Buddy, an AI chatbot assistant. This AI chatbot integrates directly with daily tools, providing real-time, precise answers to complex queries, and navigating through extensive documentation effortlessly. The breakthrough starts with Confluence documents and expands to platforms like Jira and GitHub, leveraging the latest in AI and cloud technologies for scalable and robust performance. This example illustrates how an AI chatbot can be adjusted to the needs of a particular business and used to improve performance. The same approach can be applied to integrating AI and ML into testing and QA processes to get the most benefits from the technology. 

The impact of the recent advancements in AI and ML technology on QA and testing continues to grow. To remain competitive, companies must learn to apply novel methods most efficiently. Alexander Motylev’s extensive experience in managing Software QA, Testing, and Release Management activities using an Agile approach has positioned him uniquely to lead the development of cutting-edge AI/ML-based tools that address key operational challenges in the industry. While there is no universal solution on how to integrate new solutions, the trends and ideas described above provide a good starting point.