GitLab’s 2023 Global DevSecOps Report results show increasing adoption of DevSecOps methodologies. AI tools are rapidly being adopted by developers to check and test their code, but security practitioners say AI threatens developers’ jobs or makes them more difficult by introducing hard-to-find errors. I am afraid that
GitLab collected a total of 5,010 responses to our 2023 survey of DevSecOps professionals across industries and company sizes around the world. Of his three components of DevSecOps, software development was the most common (39%), followed by him in IT operations (32%) and IT security (29%).
Half of the respondents were under the age of 35, and although the majority of respondents were men, nearly a quarter were women, not as disproportionately as some other surveys have seen.
Two-thirds of the respondents were from the US, 14% from India, 3% from the UK and 6% from other European countries.
The study found that organizations are adopting DevOps or DevSecOps practices more and more each year. This year, that percentage is 56%, up from 47% in 2022, when multiple methodologies were used.
Less than half of respondents already use a DevOps/DevSecOps platform, but the same percentage are considering evaluating or purchasing this year, and only 3% have no plans to do so.
Over the past few years, GitLab’s ongoing research has tracked the progress of the shift-left approach that has been put in place to introduce software testing early in the software development lifecycle. This approach aims to reduce errors later in the pipeline by moving testing to earlier stages of development and creating a faster development process. For more information, see What is a shift-left approach in DevOps?
This year it states:
Left shift is becoming a reality
According to the report, 74% of security professionals have made a left shift or plan to do so in the next three years.
According to the report:
Shifting left brings many benefits throughout the software development lifecycle. Most notably, development, security, and operations teams are coming together rather than working in silos. No one group feels alone when it comes to application security.less than one-third this year
Survey respondents (30%) said they are “completely” responsible for application security (down from 48% last year). A majority of respondents (53%) say they are responsible for the security of their applications as part of a larger team, up from 44% last year.
The study also explored the adoption of AI and ML in software development workflows for security testing and code checking, with 65% of developers already using artificial intelligence and machine learning in their testing work or It turns out that we plan to use it for the next three years.
This is because 44% of professional developers are currently using AI tools, their main use case is writing code, and a further 25% plan to use such tools soon, and their primary use case is writing code. A good use case is consistent with a recent stack overflow investigation that found that: For code testing, see Developers positive about using AI tools.
Of the GitHub survey respondents who use AI-based methods, 62% use them to check their code, up from 51% last year, and the use of bots in the testing process is down from the previous year. ratio increased from 39% to 53%.
Respondents in development are embracing AI, but two-thirds of respondents in security say they are concerned about the impact AI/ML capabilities will have on their work, with 28% saying they are “very ” or “I am concerned”.
I am “very” concerned.
Of those who expressed concern, three-quarters were concerned about the impact on jobs, with 29% worrying about fewer jobs, 23% saying AI/ML would be more cost-effective, 23 % worried about their skills. will become obsolete. For the rest of the quarter, I’m concerned that AI/ML may introduce errors that make the job more difficult.
For more information, download the full report here.
For more information
2023 GitLab Global DevSecOps Report: Security Without Sacrifices
Related article
DevSecOps is growing, but there is room for improvement
Developers willing to use AI tools
What developers think about AI
What is the shift left approach in DevOps?
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