Software testing is often met with apprehension among developers, not due to its lack of importance, but because of the extensive effort and meticulous attention required.
Developers, whose primary drive is innovation, frequently view tasks like maintaining legacy code, scripting tests, and debugging as cumbersome diversions.
Yet the reality of software development has fundamentally evolved.
“25 years ago, testing meant writing scripts. You needed specialized coders just to create test automation,” commented Damien Wong, senior vice president for APAC at U.S.-based Tricentis.
“That was less of an issue then because there were fewer applications in the landscape, less integrations, and the changes happened less frequently,” he shared in a CDO Trends piece recently.
Today’s software environment is vastly more intricate, with applications proliferating rapidly and interacting through complex dependencies, including Infrastructure as Code (IaC).
Enterprises face amplified risks from ill-defined or inadequately tested code, compounded by real threats of software supply chain vulnerabilities.
GenAI initially promised substantial relief. “Today, with the help of GenAI tooling and large language models, you will see that the speed at which code is being developed has accelerated exponentially,” Wong explained.
Yet, these advancements introduced new complications, including code hallucinations and vulnerabilities. “Where we are seeing bottlenecks is in the validation of that code… code that is auto-generated by some of these GenAI tools introduce errors and all vulnerabilities,” Wong added.
Consequently, software testing has emerged as a critical bottleneck in software modernization efforts, a gap companies like Tricentis seek to address through AI-powered testing solutions.
“Metadata is very different from data. With metadata, you can’t reverse engineer your software.”
– Damien Wong
In today’s hyper-accelerated DevOps environment, tools like Tricentis Tosca and Testim have become indispensable to software quality assurance, Wong argued.
The company employs a GenAI-augmented, model-based, codeless testing approach that fundamentally redefines testing workflows.
“So, model-based test automation effectively abstracts away the business processes of the business model from the underlying technology,” Wong said.
“If there are changes to the application, [these are] automatically propagated to the hundreds of thousands of test cases that are automated.”
Such solutions resonate with developers historically frustrated by testing overhead. A recent Tricentis-commissioned Techstrong Research study revealed a striking trend: 60% of DevOps professionals perceive AI’s greatest value in testing rather than in coding itself.
Yet GenAI’s true disruption extends beyond development teams, democratizing the testing process. Non-technical stakeholders can now directly engage in testing activities.
“In the past, if a business user wanted test scripts, they’d speak with the teams that are responsible for engineering test automation,” Wong stressed. “Now, we remove that barrier entirely.”
This approach makes testing more pro-active. Wong shared a compelling example: “We had teams draw application prototypes on flip charts, photograph and scan them, and immediately generate test frameworks — before a single line of code was written.”
Further emphasising privacy and intellectual property protection, Tricentis utilizes metadata instead of raw data. Wong clarified.
“Now, metadata is very different from data; it’s kind of what we always differentiate on. With metadata, you can’t reverse engineer your software.”
Expanding into critical areas like mobile enterprise applications, Tricentis recently acquired Waldo, a SaaS-based, no-code mobile testing platform.
Reshaping QA
Wong highlighted how Tricentis Tosca Copilot, Testim Copilot, and qTest Copilot reshape software testing: “They allow QA and developer teams to greatly accelerate software delivery,” he added.
“Think about the intelligent heart of an autonomous car. So we have the same thing [with GenAI] but for test management.”
Additionally, the acquisition of SeaLights enhances Tricentis’ AI-driven quality intelligence, extending its reach beyond SAP environments into custom and packaged applications.
The tools support comprehensive test impact analysis, risk management, and root cause analysis across programming languages, enabling continuous testing automation.
This capability is especially critical for large enterprises burdened with legacy systems. Wong elaborated: “We often talk about GenAI and digital natives, but they have very little legacy technical debt.”
He added: “Whereas you talk to a bank or an airline, they have a lot of legacy. And even though they are building web front ends, mobile front ends, etc., they still have to deal with mainframes and legacy client-server systems.”
Despite these innovations, Wong underscored the inherent challenges of GenAI, particularly hallucinations in large language models.
“We don not use vanilla LLMs,” he continued. “We contextualize AI specifically for testing environments, ensuring privacy, reducing bias, and minimizing hallucinations.”
Wong stressed the supportive, rather than replacement-oriented, role of GenAI tools: “Our tools are co-pilots, not autonomous systems. We expect human review, but we’re reducing manual effort by 80-90%.”
So, as digital transformation accelerates, effective software testing becomes not just beneficial but essential. Rather than merely detecting bugs, the modern emphasis is on proactively preventing them, Wong concluded.
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