To alleviate DevOps skill issues, we need more AI skills, ironically

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It is said that AI enhances intelligence within companies and does the same for IT stores. for example, AIOps (Artificial Intelligence for Information Technology Operations) It applies artificial intelligence and machine learning to the flow of data from IT operations, filtering noise to detect, highlight, and avoid problems.

Artificial intelligence and machine learning are also finding a place in another emerging area of ​​information technology: helping DevOps teams ensure the feasibility and quality of software that moves at ever faster speeds through the system and out to users.

As found in a recent study out of place github, development and operational teams are turning to AI in a big way to smooth the flow of code through the software review and testing phase, with 31% of teams using AI and machine learning algorithms to review code — more than double last year’s figure. The survey also found that 37% of teams are using AI/machine learning in software testing (up from 25%), and another 20% plan to introduce it this year.

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In addition exploratory study from Techstrong Research and Tricentis confirms this trend. The survey of 2,600 DevOps practitioners and leaders found that 90% would prefer injecting more AI into the testing phase of DevOps streams, and see it as a way to solve the skill shortage they also face. (Tricentis is a software testing vendor, and has a clear interest in the results. But the data is important because it reflects an increasing shift toward a more independent DevOps approach.)

There is even a paradox that emerged from the Techstrong and Tricentis study: companies need specialized skills in order to mitigate the need for specialized skills. At least 47% of respondents stated that the main advantage of DevOps being infused with AI is to reduce the skills gap, and “make it easier for employees to perform more complex tasks.”

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At the same time, managers cited a lack of skills to develop and run test AI-powered software as one of the main barriers to AI-saturated DevOps, at 44%. This is a vicious cycle that we hope to address as more professionals participate in training and educational programs focused on artificial intelligence and machine learning.

Once AI starts putting it into practice with IT sites, it will help make an impact in process-intensive DevOps workflows. Nearly two-thirds of managers in the survey (65%) say functional software testing is well suited and would benefit greatly from AI-enhanced development. The survey authors note that “Success for DevOps requires test automation at scale, which results in massive amounts of complex test data and requires frequent changes to test cases.” “This is perfectly aligned with AI’s capabilities to identify patterns in large data sets and provide insights that can be used to improve and speed up the testing process.”

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Besides potentially reducing skill requirements, the survey also identified the following benefits of infusing more AI into DevOps:

  • Customer Experience Improvement: 48%
  • Cost Reduction: 45%
  • Increased efficiency of developer teams: 43%
  • Code quality increase: 35%
  • Diagnosed problems: 25%
  • Launch speed increase: 22%
  • Legalization of knowledge: 22%
  • Prevent defects: 19%

Early users of AI-powered DevOps tend to be from larger organizations. This is not surprising, given that greater interests may have more sophisticated DevOps teams and greater access to advanced solutions such as artificial intelligence.

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Techstrong and Tricentis note that, “With respect to DevOps, these mature companies are distinguished by the progress they have made in streamlining their software development capabilities over the past five to seven years and mature and refined pipelines and processes.” “These DevOps organizations are cloud-native and use DevOps workflows, tool chains, automation, and cloud technologies.”

In the long run, infusing AI to help with the vital aspects of DevOps is a smart idea. The DevOps process, for all its collaboration and automation, is getting more and more stressful as software is expected to go out the door at an accelerated pace. Leave it to the machines to deal with a lot of the cumbersome aspects, like testing and monitoring.

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