Ironically, to alleviate the DevOps skills issues, we need more AI skills

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According to reports, artificial intelligence increases intelligence in companies and is doing the same for IT businesses. For example, AIOps (Artificial Intelligence for IT Operations) applies AI and machine learning to streams of data from IT processes, sifting through the noise to detect, highlight, and mitigate problems.

AI and machine learning are also finding a home in another emerging area of ​​IT: helping DevOps teams ensure the health and quality of software moving through the system and to users at ever-increasing speeds.

As noted in a recent survey by GitHub, development and operations teams are turning to AI at scale to smooth code flow through the software review and testing phase, with 31% of teams actively using AI and ML algorithms for code review use – more than twice as many as in the previous year. The survey also found that 37% of teams are using AI/ML in software testing (vs. 25%), and another 20% plan to adopt it this year.

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An additional survey by Techstrong Research and Tricentis confirms this trend. The survey of 2,600 DevOps practitioners and leaders found that 90% are positive about introducing more AI into the testing phase of DevOps flows and see it as a way to address the skills shortage they also face. (Tricentis is a software testing vendor that is obviously involved in the findings, but the data is significant as it reflects an increasing shift toward more autonomous DevOps approaches.)

The Techstrong and Tricentis study even reveals a paradox: companies need specialized skills to reduce the need for specialized skills. At least 47% of respondents say a major benefit of AI-enabled DevOps is closing the skills gap and “making it easier for employees to perform more complicated tasks.”

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At the same time, a lack of skills required to develop and execute AI-powered software testing was cited by managers at 44% as one of the top barriers to AI-infused DevOps. This is a vicious cycle that will hopefully be broken as more professionals enroll in training and education programs focused on AI and machine learning.

Once AI is deployed at IT premises, it will help improve process-intensive DevOps workflows. Nearly two-thirds of managers surveyed (65%) say functional software testing is well suited to AI-enabled DevOps and would benefit greatly from it. “The success of DevOps requires large-scale test automation that generates massive amounts of complex test data and requires frequent changes to test cases,” the survey authors emphasize. “This aligns perfectly with the AI’s ability to recognize patterns in large data sets and provide insights that can be used to improve and speed up the testing process.”

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In addition to the potential reduction in skill requirements, the survey also identified the following benefits of bringing more AI to DevOps:

  • Improvement in customer experience: 48%
  • Reduce costs: 45%
  • Increase in the efficiency of development teams: 43%
  • Increase code quality: 35%
  • Diagnose problems: 25%
  • Increase Release Rate: 22%
  • Codify knowledge: 22%
  • Avoid mistakes: 19%

Early adopters of AI-enhanced DevOps typically come from larger organizations. This is not surprising as larger companies would have better developed DevOps teams and better access to advanced solutions like AI.

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“In terms of DevOps, these mature companies are characterized by the strides they have made over the past five to seven years in streamlining their software development capabilities, as well as their mature and refined pipelines and processes,” the Techstrong and Tricentis authors point out . “These DevOps organizations are cloud-native and use DevOps workflow pipelines, toolchains, automation and cloud technologies.”

In the long run, using AI to support important aspects of DevOps is a smart idea. The DevOps process, for all its collaboration and automation, only gets harder as software is expected to fly out the door at an ever-increasing rate. Let the machines handle many of the tedious aspects like testing and monitoring.

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