A survey of 509 software engineers finds nearly three-quarters (72%) are leveraging generative artificial intelligence (AI) capabilities somewhere in a software development process, with nearly half (48%) reporting they use these tools every day.

Conducted by BairesDev, a provider of application development outsourcing services, the survey finds the most widely used tools are ChatGPT (54%), GitHub Copilot (30%), Microsoft Copilot (17%) and Google Gemini (12%).

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Which of the following best describes how you currently monitor and manage systems across your hybrid cloud environment?(Required)

Multiple separate tools for different environments

Multiple separate tools for different monitoring and management functions

Single consolidated dashboard

Custom built monitoring and management solution

No full monitoring and management solution

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How valuable would it be for your monitoring and management solution to incorporate predictive analytics to help you identify and address system issues that are critical and not waste time or effort working through all the data and “noise” that is not critical?(Required)

Extremely valuable

Moderately valuable

Somewhat valuable

Slightly valuable

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How do you most typically address potential system issues BEFORE they impact your operations?(Required)

Automated workflows and playbooks to remediate potential issues as soon as they are detected

An orchestrated response process that leverages automation, AI, and human expertise to rapidly respond to potential issues

Advanced monitoring tools such as predictive analytics and anomaly detection to proactively identify and address potential issues

Respond to issues as they arise, using manual processes and troubleshooting techniques

A combination of automated remediation, orchestrated response, and/or proactive monitoring to address potential system issues

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Other

What methods does your organization use to control and monitor cloud/IT spending?(Required)

Centralized procurement processes

Pre-approved budget thresholds

Automated cost monitoring tools

Department-level spending controls

Regular cost audits and reviews

Cloud cost optimization platform

Approval workflows for resource provisioning

Native financial operations tools provided by the cloud provider

No formal controls in place

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Other

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Which methods do you rely on most to detect security vulnerabilities in your systems?(Required)

Automated scanning tools

Manual assessments

Vendor notifications

Threat modeling (for early detection)

None of these

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What are the TWO most common ways you prioritize vulnerabilities to address within your environment?(Required)

Based on potential impact

Based on exploitability

Regulatory compliance requirements

Based on CVSS scores

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First-in-first-out approach

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How do you typically handle system updates and security patches?(Required)

Automated updates and patches

Manually review and apply updates and patches on a regular schedule

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Apply updates and patches on an as-needed basis.

Outsourced update and patch management

No defined process

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While 81% are using generative AI tools to write code and 67% to create documentation, the survey finds only a total of 40% said they do not believe these tools and capabilities have freed up time for them to accomplish other tasks.

However, 71% said they believe their productivity has increased between 10%-25%, with just under a quarter (23%) reporting productivity increases of 50% or more. Only 6% reported having no change in their productivity since they started using GenAI. The roles experiencing the highest productivity boosts from GenAI at a minimum of 40% are site reliability engineers (SREs), DevOps, geographic information system (GIS) developers and project managers/scrum masters.

In terms of quality, 74% of respondents said generative AI has increased their quality of work to some extent, with half (53%) reporting the quality of their work has improved between 10%-25%. Just under a quarter (24%) said there has been no change.

Overall, 47% of respondents report discovering errors every time they use generative AI, although typically minor. Another 16% say there are errors every time, but they are typically significant. Among software engineers with eight or more years of experience, 49% have seen minor errors every time they use AI-generated code, while only 39% of engineers with less experience have seen these errors. A total of 20% said generative AI tools are not good at code generation, the survey finds.

BairesDev CTO Justice Erolin regardless of the quality of the code being generated software engineers are using these tools to provide a head start on a project. Instead of having to write every line of code themselves, these tools generate enough code to justify the return on the investment (ROI), he added.

It’s not likely software engineers are going to cut and paste code generated by an AI platform without first reviewing it, noted Erolin.

Raising the Bar

In the long term, it remains to be seen how generative AI will impact the demand for software engineering expertise. Many senior software engineers are now automating tasks that they previously might have handed off to a more junior member of the team. That creates a challenge because over time the need for junior software engineers might be diminished, noted Erolin. In effect, the bar for becoming a software engineer is being raised, he added.

In theory, junior engineers will have been exposed to generative AI tools in college so, hopefully, junior engineers will, in effect, become senior engineers much faster once they join the workforce. Generative AI should also make it easier to onboard software engineers to any project thanks, in part, to the increased availability of documentation.

Ultimately, software engineering is never going to be the same again. The challenge and the opportunity now is determining how best to use generative AI tools, not only today but as they continue to advance and evolve in the months and years ahead.