While artificial intelligence (AI) and machine learning (ML) are emerging technologies, we know they can help an organization parse large data sets and glean actionable insights. But do AI-infused processes actually make a difference in organizations that employ DevOps? The answer is explored in a recent survey by Tricentis. Working in partnership with Techstrong Group, Tricentis surveyed more than 2,600 DevOps practitioners across the globe about their AI-augmented DevOps processes. The results pointed to a bright future in DevOps.
In this survey, the majority of respondents cited they’re already getting value from AI-powered DevOps processes. A third of survey respondents found AI-augmented DevOps extremely useful today and nearly half found it very useful. Although the technology is still relatively young, a 2021 report by IT infrastructure automation company Puppet Labs found that 83% of organizations are implementing DevOps processes. This rapid adoption speaks to the promise of the technology.
Interestingly, nearly two-thirds of respondents cited that testing is the area within DevOps where AI will have the most impact. Testing is a major pain point for DevOps organizations because a scaled DevOps environment calls for complex testing scenarios and large amounts of data, and many organizations struggle to scale their automation to the level DevOps requires. Without automation, manual testing can significantly delay releases. In AI-infused DevOps processes, AI can help accelerate the authoring of tests, help testers understand where the risks are, and repair broken tests.
We typically think of software testing as either functional, where the program enables users to properly execute tasks, or non-functional, where the application is secure, fast and stable. Respondents were asked which disciplines of testing stood to gain most from AI and 65% of them indicated functional testing. Unit testing and UI testing were the sub-categories most often selected.
The complexity of the functional testing process is where AI shines. Functional testing is open-ended and many permutations are required to ensure proper test coverage. AI steps in and manages those permutations, parsing large amounts of data and providing insights. Additionally, AI can help wrangle production signals that aren’t discovered in a test environment.
Throughout the survey, respondents specifically noted AI’s promise in regard to UI testing. UI testing is perhaps the most time-intensive and manual type of testing in most organizations. With the many variations of the user experience paths that exist, AI can help mimic the actions of a real end-user, adjusting for application nuances or variations in experience. AI and ML can also boost your build process, increasing the efficiency of repetitive tasks. The ability to quickly identify and resolve issues can improve the effectiveness of your automation.
While survey respondents see the most potential in testing, they acknowledge that AI-powered DevOps can address other business challenges. More than 40% claim that these processes can reduce the skills gap and empower junior staff to perform complex tasks, improve the customer experience, cut costs, boost innovation and increase efficiency among developers. Nearly 40% also believe that AI-powered DevOps delivers leadership the insights they need for continuous improvement.
It’s worth noting that among the DevOps organizations that see benefits from AI and ML, most of them are mature organizations; they employ DevOps workflow pipelines, toolchains, automation and cloud technologies, and they’ve automated more than half of their testing. Only 21% of practitioners who are just dipping their toes into DevOps have achieved this level of automation. And even in mature organizations, testing is still the main bottleneck; only 40% of them have automated more than half of their testing.
The key to scaling DevOps practices is developing test automation that can keep up with an increasing volume and speed of releases. But the road to DevOps isn’t always easy. Practitioners cited obstacles like a lack of tech skills (44%), insufficient budget (25%) and tool selection (19%). Organizations that want to implement DevOps need to rely on external vendors and internal resources to address these challenges and effectively scale.
So where does an organization that wants to implement DevOps start? With the big questions. It’s crucial to identify the specific business problem that you’d like to solve. What’s the gain you’re looking for? Where in the software life cycle do you expect to find a benefit? AI and ML can add business value, but they don’t solve every problem across the life cycle.
After an organization has identified its DevOps goals, its focus should shift to automation. To reap the benefits of AI-powered testing, an organization should automate repetitive functional testing, especially unit and regression testing. Most organizations start by implementing continuous integration and continuous delivery. Once an organization has taken these steps, it will start to amass a large cache of results and artifacts generated from running the test cases, which AI can mine to increase test stability and identify recurring problems. The more you automate, the more you’ll get out of it and the faster you’ll accelerate your delivery.
Organizations just starting out with DevOps should also understand the distinction between artificial intelligence and ML. Most people refer to ML as AI, but they’re not interchangeable, and most organizations haven’t advanced to the use of AI. A true AI is broadly powerful, while ML technologies are narrow and solve a specific domain problem that a model was trained for.
When you’re evaluating potential vendors, there are a number of key questions to ask, including:
• What specific AI technology is the vendor using? Is it an off-the-shelf library or something they built themselves?
• How are they using AI? Which algorithms are in play and what approach do they take?
• How do they train their models? Different types of patterns emerge in data sets depending on the industry, the frequency with which data is updated and the application. The data population that the vendor is training on must look like your production data, or the model could take you in the wrong direction.
• How do they ensure that bias doesn’t come into play? If they train models in a different domain or vertical, for example, you could be an outlier for that vendor.
• How do they handle failure? How do they monitor for miscategorization and misapplication of the AI in a way that lets you trust the data?
• Will your data be used to train and improve your learning models? If so, does that raise any privacy concerns? Expectations around data privacy should be explicit and leave no questions.
• Are there other customers in your vertical with a similar business profile that are getting the benefits you’re looking for? Can the vendor provide examples?
Consider a financial services organization that has more than 20 applications to manage. With so many applications, it’s necessary to conduct significant UI testing and the tests must be stable. One of the biggest challenges to UI testing lies in locating various web elements; most people start with manual script-based automation, but it can be challenging and time-consuming to create and maintain. Employing AI and smart locators to identify these elements can save time and drastically cut down on UI testing maintenance.
Survey respondents were asked how they’re currently using AI to augment their testing processes. Thirty-seven percent say they’re accelerating the creation of their automated test cases, while 44% focus their testing on the areas of highest risk. Almost half are able to reduce test case maintenance with self-healing and 43% use AI to identify the root cause of failed tests. Thirty-one percent claim that AI offers insights into test process improvements and 34% use AI to help identify tests to run based on changes within the application.
Going forward, AI will become more and more embedded in testing processes. Great progress with off-the-shelf vendors is being made, and their products are a lot more accessible than they used to be. As the tooling continues to mature, these products will reduce the need for specialized skills. Internal initiatives like data lakes or data warehouses will persist, but there will be less need to build large data science teams to get the advantages of AI.
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