The pace of innovation and digital transformation is unlikely to slow down anytime soon, driven by businesses that are either prioritising the integration of automated processes or trying to deliver exceptional customer experiences. Quality engineering underpins this change as it helps organisations modernise enterprise applications with less risk, innovate digitally with confidence and automate business processes at scale.
Artificial intelligence (AI) has also emerged as a cornerstone of progress, with its ability to optimise legacy processes, create new revenue streams, and speed up tasks that are traditionally tedious among others. Yet, as significant as the potential of AI is, integrating it with automation can create more effective processes for better-quality engineering.
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In a region like Asia Pacific (APAC) which is well-known to be an innovation hotbed, enterprises must operate at unprecedented speed and scale, all while delivering value to the business. 64% of businesses in APAC already believe that AI-assisted automation will be essential for increasing overall productivity. For businesses looking to maintain a competitive edge, it is especially crucial to understand how AI-assisted automation can be integrated into their workflows.
Driving quality with AI-driven automation
Rather than merely keeping pace, recent years have seen businesses in APAC striving to stay multiple steps ahead as the digital landscape evolves rapidly. As constituent application components evolve, AI-assisted automation will ensure that the quality of entire application suites remains intact. This way, engineers can also focus on creating better code and ensure that quality is prioritised across whole development pipelines without much incremental effort.
Additionally, automation, driven by AI, can conduct impact analysis to determine risk levels that come with both business and technical changes, allowing for a more streamlined decision-making process. With a clear understanding of potential consequences, development teams can better prioritise where to start looking for issues and fix test suites with the least amount of manual effort possible.
Improving quality assurance with enhanced testing
Given their ability to process and analyse large amounts of data in a faster and more efficient manner, intelligent automation is most useful in areas that require or deal with a lot of data and trends. Earlier detection of anomalies and software bugs in the software development lifecycle will ensure these issues are resolved in a timely manner resulting in better end-to-end quality.
For instance, regular automation testing would not be able to detect overlapping or off-screen text elements, hidden components, or complex colour combinations that are challenging for colour-blind users. While these issues usually go undetected and do not cause errors for most automated processes, they may later lead to problems that are harder for developers to fix. This is where visual AI testing can help identify such issues at scale, acting as a detective that uncovers even the most subtle aspects to ensure a positive and seamless user experience.
Enabling human expertise to drive quality engineering
With smaller teams and fewer resources in today’s challenging economic climate, software development teams are also under pressure to do more with less. But while speed is essential, quality should never be compromised.
AI-assisted automation is key for organisations to deploy higher-quality software and updates more quickly. However, there is widespread concern about how employees involved in software testing will be impacted. According to a survey conducted by Ipsos Global, employees in Asian markets were the most concerned about AI replacing their current jobs. The truth is just the opposite: AI will not replace humans, but those who know how to work with AI will have a clear advantage.
Rather than eliminate jobs, AI-powered automation should be seen as a tool to enhance human capabilities, relieving teams from tedious manual processes. This allows teams to focus their efforts on more strategic, complex and business-critical tasks instead. Ultimately, this will lead to faster development cycles and better products.
Without a doubt, AI in testing has evolved from a mere concept into reality, transforming the way we manage quality assurance. With quality engineering being such an integral and critical part of the software development lifecycle, AI-assisted automation will be the linchpin for organisations to elevate the most fundamental aspects of software engineering and deliver faster time-to-market and higher-quality solutions. AI’s broader significance lies in its ability to complement human capabilities, creating opportunities for new horizons of innovation.
The article titled “The perfect pair: How AI and automation propel quality engineering” was contributed by Damien Wong, Senior Vice President for APAC, Tricentis
About the author
Damien Wong is Senior Vice President for Asia Pacific and Japan (APAC), responsible for all aspects of the go-to-market strategy and for driving further expansion across the APAC regions.
In his role, Damien is responsible for shaping and developing Tricentis’ presence in the region. Spearheading the role as trusted advisor in software quality engineering, Damien consistently engages with key C-suites, senior stakeholders and strategic partners.
Damien is also a regular speaker at industry events and fervently believes in developing the next generation of industry leaders, through coaching and mentoring of budding executives across the Asia Pacific region.
With more than 27 years of experience in consulting, customer relationship and general management, Damien brings to Tricentis his tenacious leadership, and passion in building high-performance teams in enterprise technology companies.
Damien previously served as Vice President of APAC at Confluent, as well as Vice President and General Manager across Southeast Asia, Korea, Hong Kong, and Taiwan at Red Hat. He has also held business and technology leadership positions at companies such as Hewlett Packard, META Group and Accenture.