The QA space may underestimate the transformative impact artificial intelligence is having on the industry, according to an industry insider.
In fact, testing, quality assurance, and security vulnerability detection are by far the most widely adopted use cases for AI in software development, according to Paulo Rosado, CEO and founder at OutSystems.
“AI is redefining the impossible,” Rosado stated, as he said he is helping teams compress multi-year legacy modernization projects “into just a few months.”
“The latest AI disruptions have brought us the potential to compress these development timelines into even shorter and faster projects,” he added.
“With AI, historically impossible transformation projects are not only possible but easier, cheaper, and faster to accomplish,” Rosado said.
Fast-changing landscape
Rosado said AI is rapidly changing the QA landscape as we know it. His firm recently ordered KPMG to carry out a study into the future of software development, which found that AI can reduce development time by up to 50%.
The survey, which highlighted AI’s transformative role in testing, quality assurance, and code generation while noting ongoing challenges with data privacy and integration, found that AI is coming in faster and with a bigger impact than most executives may realise.
As the reach of AI expands, QA leaders are exploring new use cases for technology used throughout the software development lifecycle. The research surveyed 555 software executives around the world whose companies span IT consultancy services, banking, financial services and insurance.
The vast majority, more than eight of ten executives, reported that their organisations first began to incorporate AI technologies in their SDLCs between six months to five years ago, with the earliest adopters primarily being IT services companies.
Across regions, EMEA and North America remain roughly on equal footing, while APAC is steadily catching up.
Rosado said the findings show that testing, quality assurance, and security vulnerability detection are by far the most widely adopted use cases for AI in software development.
Nonetheless, “generative AI is set to transform the industry by significantly enhancing these processes and introducing unprecedented capabilities.”
He also stressed that 75% of software executives have seen up to a 50% reduction in development time by implementing AI and automation.
“Early adopters are planning to increase their use of AI in other stages of the SDLC, such as user interface design, code generation, DevOps optimization, and application maintenance,” Rosado explained.
“Nearly everyone is planning to increase their investment in AI-augmented SDLC management over the next two years, indicating that AI will play a central role in driving innovation and competitive advantage in the software industry.”
Shifting roles
One trend the QA space is seeing currently is that the developer’s role is rapidly shifting from code writer to code reviewer.
“Large language models are a big help, but they still make mistakes. But as these models evolve, and trust in the resulting code improves, the developer’s role will be more akin to that of an orchestrator and acceptance tester of AI-generated outputs,” said Rosado’s colleague, Rodrigo Coutinho, co-founder and AI Project Manager at OutSystems.
Despite being a nascent technology a couple of years ago, Coutinho said that confidence in the quality of AI-generated code has risen substantially, with half of respondents said that the implementation of AI has improved software quality, enhanced decision-making, and increased efficiency in software testing and quality assurance.
“But confidence is also paired with risk awareness surrounding tech debt in the form of orphan code and hallucinations, a lack of context for an organization’s specific coding needs, and scalability concerns,” Coutinho noted.
With strategy baked into AI in SDLC processes, many firms have experienced or expected to experience a higher quality of applications, with fewer bugs and improved performance.
Concerns
Data privacy and security concerns remain the primary barriers to broader adoption, Coutinho continued.
“The AI opportunity is undeniably huge, but its wider adoption in other areas of the SDLC beyond software testing and vulnerability detection still face some barriers.”
Chief among these are data privacy and security concerns and regulatory and compliance challenges.
Moreover, more than a third of executives face difficulties integrating generative AI into existing workflows as the primary barrier to adoption. One-third of respondents said they had a backlog of between 150 and 800 use cases for GenAI.
“The speed and sprawl of AI, namely GenAI, is paving the way for an increase in investments for nearly all respondents.”
But risks concerning the reliability of AI-generated code persist, though they can be mitigated with existing approaches, such as user acceptance testing, unit testing, and regression testing.
“It’s up to the developer working with AI to guarantee the quality of the deliverables, but this becomes way more efficient with AI. AI is, in fact, a great partner in creating tests in synthetic data,” Coutinho concluded.
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