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AIOps and Quality Assurance: AI and ML Drive Change

vladislavershov profile image Vladislav Originally published at qamadness.com ・4 min read

DevOps model facilitates the product delivery process. Still, this dynamic method might be not enough when both development and business teams participate in the decision-making process and the deadlines are tough. That’s where AIOps comes to rescue – a brand new way to optimize SDLC and business processes.

Definition of AIOps

AIOps (artificial intelligence in IT operations) means 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. These platforms consume and analyze the data generated by IT to better understand software behavior.

For example, traditional test automation services cover repetitive time-consuming tasks so people can focus on uncommon issues and cross-team collaboration. In AIOps, algorithms collect valuable insights and use data from performance monitoring, event analysis, IT service management, etc. for more efficient automation.

AIOps and DevOps: What’s the Difference?

DevOps automates the path from development to production – with auto tests and readiness checks, in particular. AIOps uses the data it receives 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.

AIOps Efficiency

What Makes AIOps Efficient?

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 platforms, the big scope of changes is not a challenge. 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.

Why Adopting AIOps Is a Good Decision?

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. It 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 systems analyze test traffic and logs automatically, show infrastructure changes and previous incidents. They find issues early by addressing outages and service degradations. These platforms also detect 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 and QA

AIOps and QA

AIOps can change the way a software testing company works. With the right tools, test data becomes priceless. AI together with ML build a predictive QA model that converts data into actionable insights that reduce test cycles and allow for faster product delivery.

Large applications imply equally large test suites that take a lot of time to run. There is no guarantee that these tests will cover all relevant elements. The predictive QA model can pick cases that are essential for a particular code modification. Moreover, an AIOps platform would alert the changes that could cause critical bugs.

Implementing 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. If there are too many, choose the most suitable.
  6. Use these insights to prepare an AIOps platform for real-time software monitoring.

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. With time, AI will mature and prevent both known and new issues.

Just don’t forget that 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.

Best AIOps Tools

Best AIOps Tools

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 – so you can choose one based on your priorities and business goals.

Speaking about AIOps companies, product reviews on G2 name these five software solutions as the best on the market today:

  • Dynatrace;
  • Splunk Enterprise;
  • AppDynamics;
  • Splunk Cloud;
  • Moogsoft.
  • Bottom Line

    Tech companies that apply AIOps to enhance dev and QA resources 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 only because they are having a moment.

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Vladislav

@vladislavershov

QA Automation Pro managing the team of QA engineers. I`ll share why automation is the future of tech and how it benefits web and mobile development.

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