Scope of AI in DevOps

With everything in the technical world is going towards improvement, DevOps has a huge potential to transform itself by integrating AI in the process as DevOps has the majority of repetitive work. Automation supported by AI addresses today’s complex requirements easily with more clarity. In DevOps, more than a third of four tasks can be done with AI and the remaining work can be monitored and done by the experts to fill the hue of excellence to the performance. With development teams leading towards Continuous Testing, the Test Automation technique needs to get smarter to be more effective in the delivery pipeline. DevOps has a yet lot more ground to embrace AI in various ways. AI has the capability to develop, execute and perform automated tests under human supervision without any kind of interruption. Combining AI with DevOps has the potential to get developed with excellence improvisation. In this blog, we have discussed the scope of AI in DevOps.

In order to make DevOps, AI-driven, the following steps need to be taken to leverage the maximum potential.

1. Test Runs and Executions

Developing a test run is a continuous and repetitive task that requires a great number of efforts. It should be noted that your conventional way of working is doing its job while you are finding a new one, eliminating the risk of dropped productivity. It requires a plethora of resources and collaboration efforts to involve each corner in the test run with an expectation of optimization. It is difficult to match the rate of effective test runs with the increasing demand of software applications, only with human staff as not everyone may know the application end to end to identify the affected test cases correctly. And to meet this demand, AI supports the DevOps in a way that; with the relevant information in a specific format on what is developed and what is to be developed, machines can easily generate plans automatically, minimizing human errors. AI automation makes test runs and execution quicker and authentic to inspect the application quality continuously.

2. Test Case Maintenance

Same as Test runs and execution, in Test Automation, maintenance is an essential and continuous effort. With each change in the application, the requirement to retrace the associated automated test cases to modify the test arises. Sometimes there are numerous directly and indirectly associated tests that need an update. So, each connected test need attention to check for maintenance. With higher numbers of changes in the application, more efforts are needed! Even in GUI automated Testing with the centralized repository, maintenance efforts can be minimized to some extent only. But with evolved development practice, sophisticated and smart approaches are needed to modify directly affected tests along with fewer maintenance efforts. Rather than static locators to identify an element in UI, a dynamic locator strategy could be used to identify elements that make the tests more powerful and authentic with reduced maintenance efforts. AI application in Test Automation allows tests to be autocorrected to some extent, maintaining all the affected tests automatically.

3. Examining Test Failures

While examining a failed test, experts check whether a failure has been occurred because of system infrastructure or application formulas, which consumes a lot of time and manual labor. Even if they ran all their tests in such cases, they would still need to debug all failed tests manually one at a time before filtering similar failures. However, a machine too can look for similar failures in the remaining test, with all the prior information available, side-lining the task of debugging each file manually. Moreover, with AI-based Test Automation, a failed test can help to identify another affected test along with analysis of cause, resulting in solution for a various failed test. With existing data of test failure reasons, AI can execute upcoming tests with improved stability.

4. Test Development

While developing a test automation project, a huge volume of experts, time, and efforts are required. However, AI tools can weigh this down in many ways. Creating automated test scripts based on required documents using NLP techniques can ease the task. Secondly, with UI based application, it is possible to navigate through each page and generate automated tests. Additionally, Test Bots can be trained to perform specific actions with installed data. Another way to access AI is leveraging visual recognition for GUI-based applications.

5. Embracing Latest APIs:

To make DevOps embedded with AI, it is best to motivate development experts to work on advanced APIs like Azure and AWS which allows AI deployment into their software without creating self-developed models. Additionally, experts can invest their valuable experience in the integration of add-ons like voice-to-text and various other advanced trends.

6. Pre-trained Model:

A well-scripted, pre-trained model supports identifying the user behavior or inputs in a particular search. Add-ons to this model can generate improvised results which can match with users’ behavior pattern completely. The pre-trained model acts as an experimental trial towards AI-based adoption.

7. Using Free Data

Public data sets are highly useful in the initial training phase of AI adaption. Using public data may not fulfill all the requirements of the project but it can certainly help you fill the gaps to enhance the viability.

8. Continuous Exploration

Development experts should keep looking for the latest updates related to AI. Companies can encourage staff to engage in research that could be useful for their next project or giving access to AI sandboxes and general-purpose APIs without any extra formalities of the corporate procurement process.

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