Software engineering limited by lack of full automation | Computer Weekly

"Almost two-thirds of respondents said they are using AI for code generation, 60% have used AI for documentation, and 57% are using AI for quality assurance and testing. Other areas of software development currently supported by AI were found to include error remediation (55%), security compliance (54%), and performance and cost optimisation (53%). Areas where the software developers polled are seeing improvements from using AI include the speed of code creation (51%), faster testing and quality assurance (45%), and developer onboarding time (43%)."

"The survey found that, on average, organisations use eight to 10 distinct AI tools for software development. Some are using far larger numbers of tools, which suggests there is a risk of AI tool sprawl, introducing complexity that could lengthen the time it takes to get new members of the software development engineering team fully onboard. Harness noted that tool sprawl and vibe coding can amplify operational risk."

"It warned that fragmented toolchains and inexperienced developers utilising AI assistants are creating governance challenges, increasing incidents and incurring hidden costs. It recommended that IT leaders consolidate tools into a unified platform and establish AI-powered guardrails to reduce complexity and keep teams focused on innovation. According to the Harness study, organisations have high AI use for coding, but immature testing, deployment and governance."

AI use in software development is rising, with most development teams expecting AI agents to work alongside human engineers within five years. Almost two-thirds of developers use AI for code generation, 60% for documentation, 57% for quality assurance and testing, 55% for error remediation, 54% for security compliance and 53% for performance and cost optimisation. Reported benefits include faster code creation (51%), accelerated testing and QA (45%), and reduced developer onboarding time (43%). Organisations typically deploy eight to 10 distinct AI tools, raising risks of tool sprawl, governance challenges, increased incidents and hidden costs. Leaders are urged to consolidate tools and establish AI-powered guardrails and pair coding assistants with automated testing.

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