AI is Transforming DevOps, New Research Shows
Artificial intelligence (AI) is rapidly transforming software development and operations (DevOps), according to a new study by Techstrong Research. The study was commissioned by Tricentis, a leading testing services provider.
The research, which surveyed 504 IT professionals globally, reveals that AI-augmented DevOps is gaining significant traction, with nearly a quarter of organizations already using AI in one or more phases of their software development lifecycle (SDLC). This adoption is projected to skyrocket, with Techstrong Research predicting that 75% of organizations will be using AI-augmented DevOps tools by 2025.
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Generative AI Driving the Charge
“AI’s time has arrived in the world of DevOps,” says Mitchell Ashley, Chief Technology Advisor at Futurum Group and CTO at Techstrong Group. “Contributors across the SDLC are experiencing tangible benefits, particularly in terms of productivity and software quality.” He attributes much of this growth to the emergence of generative AI tools like Copilots, which are rapidly being embedded into developer tools. These AI assistants are changing the way developers work by:
- Accelerating Coding: Copilots help developers write code faster by offering real-time suggestions and automating repetitive tasks.
- Improving Code Quality: AI-powered code review tools can identify potential bugs and vulnerabilities, leading to more robust and secure software.
- Simplifying Debugging: Copilots can assist in identifying the root cause of errors, making debugging faster and more efficient.
One study participant commented on this shift, stating, “Code Generation and Language Server Protocols have automated the coding process. The continuous refinement of the released product through git commits is now painless.”
Productivity Gains Across the SDLC
The impact of these AI-powered tools is already being felt. The research shows that 60% of respondents report developers are more productive due to AI, exceeding the 43% anticipated in a 2022 study. These productivity gains extend beyond development:
- Testing and QA: 42% of respondents cite increased efficiency in testing and QA activities due to AI. This includes automating test case creation, analyzing test results, and identifying potential bottlenecks in the testing process.
- Security: AI is playing a growing role in enhancing security by proactively identifying vulnerabilities and automating responses to emerging threats. This has led to increased efficiency in security incident resolution, with 35% of respondents reporting faster resolution times.
AI’s Expanding Role in Software Testing
The report identifies software testing as a key area for AI adoption. With the rapid rise of AI-generated code, ensuring the quality and reliability of software becomes even more critical. AI is being used to:
- Optimize Test Planning: AI algorithms can analyze historical data and requirements to predict the most critical areas to focus testing efforts on.
- Automate Test Case Generation: AI tools can automatically create diverse test scenarios and data sets, expanding test coverage and depth.
- Analyze Test Results: AI can help identify patterns and anomalies in test results, allowing teams to prioritize and address issues more effectively.
Challenges and the Road Ahead
Despite the evident benefits, challenges remain in realizing the full potential of AI in DevOps. The research identifies key areas that need to be addressed:
- Skills Gap: Lack of AI skills within teams is a major obstacle to adoption. Organizations need to invest in training and upskilling programs to prepare their workforce for the AI-driven future of DevOps.
- Strategic Planning: Developing a comprehensive AI strategy is crucial for aligning AI capabilities with business goals. This requires careful planning and a deep understanding of the potential benefits and challenges of AI implementation.
- Building Trust: While trust in AI is growing, ensuring responsible and ethical use of AI remains a concern. Organizations need to establish clear guidelines and governance frameworks for AI systems, emphasizing human oversight and verification.
The Big Picture
The study didn’t address some key areas of concern. No information about how users were accessing AI services was documented. Nor were any concerns about selecting AI providers who provide adequate security for enterprise use. Nor were any of the usual ethical concerns addressed, such as managing halucinations and toxic responses.
Finally, a key concern among the DevOps workforce is the idea of job security and the possibility that AI many encroach on human job opportunities. I recently wrote about how we seem to be on a march towards full Virtual Employees who will be quite capable of taking over jobs, expecially in software engineering. Positive studies like this will foster the further development of more advanced AI applications, such as agents and coding automations. I believe this is something we need to approach with our eyes wide open.
A Transformative Future for DevOps
As AI continues to evolve, it is poised to revolutionize the DevOps landscape. “Many advances and surprises are expected as we are truly only at the beginning of this AI in DevOps journey,” says Ashley. By addressing the challenges and embracing the opportunities, organizations can unlock the full potential of AI-augmented DevOps, achieving greater efficiency, faster release cycles, and higher-quality software.
