In the digital economy, a software failure is a business failure. Software development is no longer just the concern of developers and IT.
The business world is a-changing. Digital technologies are now at the core of many businesses. In the digital economy, data is the fuel and applications are the engines.
What does this mean for software development in an organization? Why has software testing become critical for business, and how best can it be done as organizations churn out cloud and mobile applications to meet business demands?
DigiconAsia sought out some insights from Damien Wong, Senior Vice President, APAC, Tricentis.
What are some reasons organizations have begun seeing software testing as critical to their business?
Damien Wong (DW): Today, organizations operate in a highly challenging environment where customer demands and expectations are rapidly changing. To stay ahead, these businesses are constantly under pressure to roll out new and innovative digital capabilities quickly to maintain a competitive edge. The key metrics for software application excellence are functionality, performance, security, and usability. It is critical to incorporate testing strategies into mobile and web development processes to fulfill these metrics and create superior user experiences.
A software failure is a business failure – imagine a glitchy mobile application that denies user access and transactions, or a crashing website during the busiest shopping season. Hence, even non-technical business leaders are increasingly prioritizing application-related risks.
To avoid making the headlines on software failures, organizations are applying robust and comprehensive testing practices to maintain customer satisfaction.
Additionally, there is an exponential increase in cyber threats, with 28% of organizations in Southeast Asia reporting a rise in exposure to cyberattacks due to increased digitalization of their operations. By adopting security testing in the development lifecycle, organizations are able to identify any potential vulnerabilities and proactively correct and improve the security of their software products, building resistance to attacks.
With cloud and mobile apps becoming more prevalent in the digital economy, what are some key areas of change that organizations should take note of for efficiencies in their business operations and continuity?
DW: Organizations and consumers are accustomed to using mobile apps for everything – from work collaboration and enterprise resource planning to banking, entertainment, food delivery and ride hailing. To keep up with rapid change, software engineers and testing teams need to quality-assure new mobile apps and features quickly.
The challenge they face is that testing mobile apps and real devices has traditionally been time consuming and can delay releases. They struggle to pinpoint failures and promptly fix errors after each build, thereby risking high-quality mobile experiences.
To enhance operational efficiencies, organizations must change their software testing strategy to ensure it is continuous and automated throughout the development process, so as to gain instant feedback for improvement.
They should also invest in capabilities that offer the ability to test software across both physical and virtual devices, where real user experiences can be simulated across different operating systems and devices. This can ensure that, despite code changes and wider application updates, quality is not compromised.
For organizations looking to migrate legacy, enterprise applications to the cloud, they should conduct careful planning and incorporate automated cloud-centric testing to minimize business disruption risks. This can critically assess an application’s readiness for the cloud environment thereby avoiding performance issues and ensuring that data integrity and accessibility for applications is assured during the migration.
How is AI being increasingly leveraged to augment and enhance DevOps?
DW: As AI continues to gain traction in the region and globally, DevOps teams will devote more resources to incorporate this disruptive innovation to automate business processes. In fact, IDC predicts that by 2026, 75% of APAC organizations will depend on AI to optimize efficiency and enhance product quality in diverse and distributed environments.
Test automation driven by AI not only enhances human capabilities by relieving teams from error-prone manual processes, it also enables exploratory testers to identify more bugs and issues early in the process. This allows teams to focus their efforts on more strategic and complex priorities, leading to faster development cycles and higher-quality products.
Moreover, AI-powered test automation provides valuable metrics that help assess risk levels to allow for more streamlined decision-making processes. This is not merely about identifying defects, but also involves a sophisticated impact analysis of potential risks associated with both business and technical changes.
With a clear understanding of the risks involved, DevOps teams can then make informed decisions about the prioritization of tasks, resource allocation, and the deployment of mitigation strategies. This way, teams can optimise their workflows and enhance the delivery of more reliable software products.
What are some other trends in DevOps and software testing that you foresee in 2024 and beyond, especially in APAC?
To supercharge their testing capabilities, we expect organizations to integrate generative AI capabilities in their software development cycle. This will bring great benefits for DevOps and ensure continuous delivery. Not only can generative AI inform the team on what needs to be tested and optimize test case designs, we are excited about its potential to support the auto-creation and management of test data intelligently. Furthermore, it can execute tests at scale and provide intelligent insights and analytics to debug applications more rapidly.
However, AI is a double-edged sword: it harnesses exceptional potential to enhance the daily lives of individuals yet poses a disruption to various aspects of society including education, workforces, and interpersonal interactions.
The duality of this technology means that organizations need to take on more responsibility to test the safety of new AI models to ensure their validity and accuracy, and failure to do so can negatively impact businesses and user experiences.
At a national level, we can also expect governments to play a more substantial role in scaling AI successfully – just like how the Singapore government has recently updated the national AI strategy to contribute to valuable breakthroughs globally – as well as establishing guidelines for the ethical and beneficial use of AI within their jurisdictions.
