Artificial intelligence is reportedly boosting intelligence in business and is also doing the same for IT shops. For example, AIOps (Artificial Intelligence for IT Operations) applies artificial intelligence and machine learning to streaming data from IT processes, sifting through the noise to detect, address and avoid problems.
Artificial intelligence and machine learning are also finding a home in another emerging area of ​​IT: helping DevOps teams ensure the viability and quality of software moving across the system and to users at ever-increasing speeds.
As revealed in a a recent survey with GitHub, development and operations teams are heavily turning to AI to smooth the flow of code during the software validation and testing phase, with 31% of teams actively using AI and machine learning algorithms for code validation – more than double last year. The survey also shows that 37% of teams are using AI and ML in software testing (up from 25%), and another 20% plan to implement it this year.
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Additional survey from Techstrong Research and Tricentis confirms this trend. A survey of 2,600 DevOps practitioners and leaders found that 90% favor implementing more AI in the testing phase of DevOps workflows and see it as a way to address the skills shortage they too are facing. (Tricentis is a software testing vendor that clearly has a vested interest in the results. But the data is important because it reflects a growing shift toward more autonomous DevOps approaches.)
There is even a paradox that emerges from the Techstrong and Tricentis study: businesses need specialized skills to alleviate the need for specialized skills. At least 47% of respondents say the main benefit of AI-powered DevOps is closing the skills gap and “making it easier for employees to perform more complex tasks.”
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At the same time, the lack of skills needed to develop and test AI-based software was cited by managers as one of the main obstacles to AI DevOps (44%). This is a vicious cycle that will hopefully be rectified as more professionals participate in training and education programs focused on AI and machine learning.
Once AI starts to be applied to IT sites, it will help to disrupt DevOps-intensive workflows. Nearly two-thirds of managers in the survey (65%) say functional software testing is a good fit for and will greatly benefit from AI-augmented DevOps. “Successful DevOps requires large-scale test automation that generates huge amounts of complex test data and requires frequent changes to test cases,” the survey authors note. “This is perfectly in line with the capabilities of artificial intelligence to identify patterns in large data sets and offer insights that can be used to improve and speed up the testing process.”
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Along with potentially reducing skill requirements, the survey also identified the following benefits of introducing more artificial intelligence to DevOps:
- Improved customer experience: 48%
- Reduce costs: 45%
- Increased efficiency of development teams: 43%
- Code Quality Improvement: 35%
- Problem diagnosis: 25%
- Release Rate Increase: 22%
- Knowledge of codification: 22%
- Defect prevention: 19%
Early adopters of DevOps with AI tend to come from large organizations. This is not surprising, as larger concerns would have more developed DevOps teams and greater access to advanced solutions such as artificial intelligence.
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“From a DevOps perspective, these mature companies are marked by the progress they’ve made in optimizing their software development capabilities over the past five to seven years, as well as their mature and mature pipelines and processes,” Techstrong and Tricentis authors note. “These DevOps organizations operate in the cloud and use DevOps workflow pipelines, tool chains, automation and cloud technologies.”
In the long run, introducing artificial intelligence to help with vital aspects of DevOps is a smart idea. The DevOps process, for all its collaboration and automation, only becomes more exhausting as software is expected to fly out the door at a rapid pace. Leave it to the machines to handle many of the heavy lifting, such as testing and monitoring.
