To overcome DevOps problems, more AI skills are needed

Artificial intelligence would strengthen intelligence within companies, and would do the same for IT workshops. For example, AIOps (artificial intelligence for IT operations) applies AI and machine learning to data from IT processes, sifting through noise to detect, highlight and prevent problems.

AI and machine learning also find their place in another emerging area of ​​IT: helping DevOps teams ensure the viability and quality of software that moves at ever-increasing speeds through the system and to users. .

As a recent survey by GitHub indicates, development and operations teams are massively turning to AI to streamline code flow in the software review and testing phase. 31% of teams are actively using AI and ML algorithms for code review, more than double from last year. The survey also reveals that 37% of teams are using AI/ML in software testing (up from 25% previously), and another 20% plan to use it this year.

Looking for specialized skills

Another one investigation carried out by Techstrong Research and Tricentis confirms this trend. Survey of 2,600 DevOps practitioners and leaders reveals that 90% of them are in favor of injecting more AI into the testing phase of DevOps flows, and see it as a way to solve the skills shortages they also face. (Tricentis is a software testing vendor, which explains the obvious interest in the results. But the data is significant, as it reflects a growing shift toward more autonomous DevOps approaches.)

A paradox even appeared in the study by Techstrong and Tricentis: companies need specialized skills in order to mitigate the need for specialized skills. At least 47% of respondents say a key benefit of AI-infused DevOps is reducing the skills gap and “making it easier for employees to perform more complex tasks.”

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

When AI begins to be implemented in IT sites, it will help reduce process-intensive DevOps workflows. Nearly two-thirds of managers surveyed (65%) say functional software testing is well suited for AI-augmented DevOps and would benefit greatly from it. “Success in DevOps requires test automation at scale, which generates massive amounts of complex test data and requires frequent changes to test cases,” the survey authors point out. “This aligns perfectly with AI’s abilities to identify patterns in large datasets and offer insights that can be used to improve and speed up the testing process. »

Many concrete advantages

In addition to the potential reduction in required skills, the survey also identified the following benefits of introducing AI into DevOps:

  • improving the customer experience: 48%;
  • reduce costs: 45%;
  • increase the efficiency of developer teams: 43%;
  • improve code quality: 35%;
  • diagnose problems: 25%;
  • increase release speed: 22%;
  • codify knowledge: 22%;
  • preventing defects: 19%.

Early adopters of AI-enhanced DevOps tend to be large enterprises. This is not surprising, as larger enterprises have more developed DevOps teams and greater access to advanced solutions such as AI.

“When it comes to DevOps, these mature companies are marked by the progress they’ve made in streamlining their software development capabilities over the past five to seven years, and by their mature and refined pipelines and processes,” the authors point out. authors of Techstrong and Tricentis. “These DevOps organizations are cloud native and use DevOps workflow pipelines, toolchains, automation and cloud technologies. »

In the long run, infusing AI to help vital aspects of DevOps is a smart idea. The DevOps process, for all its collaboration and automation, is becoming increasingly exhausting as software is expected to be released at an ever faster rate. Leave much of the expensive stuff, such as testing and monitoring, to the machines.

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To overcome DevOps problems, more AI skills are needed

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