AIOps and QA: Machine Learning to Enhance Software Testing Services

When development and business teams participate in the decision-making process, both get a better understanding of the product as a wholesome system. This approach, however, requires more resources to compensate for the time of discussions. And most likely, there will be more code changes to make as different departments start to contribute.

DevOps model facilitates the product delivery process, but even this dynamic method isn’t always enough to meet the tough deadlines. That’s where AIOps comes to rescue – a brand new way to optimize SDLC and business processes.

What is AIOps?

“AIOps” stands for “artificial intelligence in IT operations.” It is the use of machine learning and data science for solving IT-related issues. An AIOps platform applies big data to enhance the functions of IT operations and minimize human input. Such platforms consume and analyze the data generated by IT to better understand software behavior.

IT operations and machine learning have existed separately for a long time, and AIOps is what brings them together. Using analytics for data-driven insights is the innovation that will help to cover a broader range of tasks by test automation in the future.

The Difference Between AIOps and DevOps

DevOps automates the path from development to production – with auto tests and readiness checks, in particular. AIOps uses data to predict the performance, suggest ways of optimization, and process root cause analysis.

With DevOps, we still rely on humans to look at logs, alerts, and metrics to find issues. AIOps is the next step on the path of automation. AI analyses data more accurately. It can correlate performance with code issues to recommend changes or even fix issues immediately based on past experiences.

Increasing the Efficiency of Development

In 2018, only 5 percent of large enterprises applied AIOps. Gartner, a research & consulting company that specializes in the internet technologies, predicts that 30 percent will use machine learning and big data analytics to automate IT processes by 2023.

The core advantage of AIOps is a fast-paced delivery of complex apps and distributed systems. Companies that employ DevOps still need to release new code monthly or weekly. It gets difficult for IT teams to keep up with the updates in the products they support, as well as for QA teams to run regular checks.

For AIOps, the big scope of changes isn’t a problem. It the future, the range of AI tasks will go further beyond automating regression testing. AI will be able to cover A/B tests, auto healing, automatic alerts, and much more.

Benefits of Using AIOps

AIOps helps organizations to dramatically improve service health and productivity. AI and ML can predict load patterns and schedule maintenance works (patches, upgrades, new releases) during low-impact periods. Here are a few more examples of how this technology enhances the development process:

• AIOps systems analyze test traffic and logs automatically, show infrastructure changes and previous incidents.
• They find issues early by addressing outages and service degradations.
• AIOps can test the code for performance and regression based on previous issues.
• It detects inconsistencies and proactively identifies potential issues before they cause problems.
• If a problem does happen, a platform presents only a few critical events that have affected the service.
• AIOps can roll back the previous build if the new one has failed, increase/decrease CPU based on memory usage, and take other actions to keep software stable.

“AIOps is really good at sifting through lots of data to find where to focus. Millions of log records and metrics can be processed to find the few that matter, something that humans just can handle.”
 Michael Procopio, Product Marketing Manager at Micro Focus

The Role of QA in AIOps

AIOps can change the way a software testing company works. With the right tools, test data is priceless. AI together with ML build a predictive QA model that converts data into actionable insights, like defect ranges and risk modules for future software versions. These insights reduce test cycles and allow for faster product delivery.

A QA team, whether it is QA outsource or an in-house squad, doesn’t always receive data that helps prioritize tests clearly. The previous experience becomes the main criterion for prioritization. QA specialists want to run as many tests as possible to have a wider coverage of functionality, while developers make an accent on the speed.

An AIOps system is objective, unlike humans. It takes a data-driven approach and allows reaching compromise. When AI analyzes real-user behavior, it can optimize test suites to take care of the functionalities people care about the most.

How to Get Started with AIOps

The scenario of AIOps adoption may differ depending on a project scale, complexity, and specifications. Still, this step-by-step guide will be helpful.

1. Get familiar with the AI and ML vocabulary. Do some research and team training.
2. Identify and understand data in your operations: logs, metrics, API outputs, device data, etc.
3. Think about how the data can solve your problems. For example, a system will review past failures and detect the root of the high-priority problems.
4. Analyze project feasibility. Make sure AIOps helps solve problems and its implementation is relevant.
5. Select test cases for ML. A brainstorming session and inputs by different teams is an optimal way to do it. Then choose and finalize test cases.
6. Use these insights to prepare an AIOps platform for real-time software monitoring. With time, AI will mature and prevent both known and new issues.

There is one thing to remember to avoid pitfalls: AIOps is not an alternative to manual software testing. AI cannot replace humans. It facilitates the work, augments the abilities of the QA specialists, but people still supervise AI and arrange ML. Also, the system should have enough data to learn from. AI needs to get the full picture to work with high accuracy and make valuable predictions.

Best AIOps Tools

AIOps tools are respectively new but already diverse. Users often look for ServiceNow, IMB, and CA AIOps solutions. We decided to go on G2 and check the unbiased reviews of AIOps companies. Here are five of the best ones, as rated by users.

• Dynatrace is an all-in-one platform that uses AI to simplify cloud operations. It provides data on app performance and suggestions on how to improve it. Dynatrace supports eight languages, including Japanese, Korean, and simplified Chinese.
• Splunk Enterprise is easy to use, with excellent data, visualization, descriptive documentation, multiple integrations options (Salesforce, Cisco, etc.), and support of four languages. A good solution for those, who need to access real-time Operational Intelligence.
• AppDynamics is a part of the Cisco software group, and it also focuses on product business performance in complex environments and end-user monitoring. Being easy to configure, it still monitors every line of code, proactively identifying issues.
• Splunk Cloud is another Splunk AIOps solution – a cloud service that provides a wide range of information about the infrastructure.
• Moogsoft integrates software tools that boost customer loyalty thanks to reducing noise, prioritizing incidents, and ensuring website uptime. It discards up to 90 percent of noisy alerts, identifies probable roots of incidents, and prescribes solutions without human interference.

If it is difficult to decide upon a platform for AIOps, Gartner Magic Quadrant may become helpful. This is a graph a company came up with to estimate technology providers. It distinguishes four types of companies on the market – Leaders, Visionaries, Niche Players, and Challengers. Thus, you can choose one based on your priorities and business goals.

Bottom Line

A company that delivers products faster than competitors gains a significant advantage in the market. Speed, however, shouldn’t be a priority if it affects quality. To ensure both, a tech company may need to apply additional dev and QA resources – to hire more people or find a tech solution that enhances the product delivery process.

Companies that apply AIOps are more likely to top the leaderboard, but only in case they apply it smartly. AIOps is not a panacea, so always keep in mind the specific nature of your project before rushing to adopt new technologies just because they are having a moment.