The DevOps Guide to Improving Test Automation with Machine Learning

AI and machine learning (ML) are crucial to scaling test automation. Without them DevOps teams must contend with vast quantities of test data riddled with bugs and unstable test cases. With AI/ML it’s possible to conduct more comprehensive and detailed test data analysis to spot defects and recurring patterns in test failures. DevOps teams can use AI/ML to improve test stability, identify quality and security issues, and provide fast feedback.

DevOps processes involve a wide range of practitioners, including product managers, product owners, developers, test automation engineers, business testers and operation engineers. This means the data originates from different tools and personas, and needs to be normalized. To succeed in a complex DevOps digital journey, teams must adopt automated continuous testing that is reliable, self-maintained (as much as possible) and brings value with each test execution cycle.

Organizations that implement continuous testing within Agile and DevOps execute a large variety of testing types multiple times a day. With each test execution, the amount of test data that’s being created grows significantly, making the decision-making process harder.

With AI and ML, executives should be able to better slice and dice test data, understand trends and patterns, quantify business risks, and make decisions faster and continuously. Without the help of AI or ML, the work is error-prone, manual and sometimes impossible.

Machine Learning for Trends and Patterns Identification

The whole purpose of investing time and resources into test automation creation is to be able to answer on-demand quality and business risk-related questions.

Scaling test automation and managing it over time remains a challenge for DevOps teams. Development teams can utilize ML both in the platform’s test automation authoring and execution phases, as well as in the post-execution test analysis that includes looking at trends, patterns and impact on the business.

Before diving deeper into how ML can help during both phases of the test automation process, it is important to understand the root causes to why test automation is so unstable when not utilizing these technologies:

• The testing stability of both mobile and web apps are often impacted by elements within them that are either dynamic by definition (e.g. react native apps), or that were changed by the developers.
• Testing stability can also be impacted when changes are made to the data that the test is dependent on, or more commonly, changes are made directly to the app (i.e. new screens, buttons, user flows or user inputs are added).
• Non-ML test scripts are static, so they cannot automatically adapt and overcome the above changes. This inability to adapt results in test failures, flaky/brittle tests, build failures, inconsistent test data and more.

There a few different ways ML can help your DevOps organization with test automation. They are as follows:

1. Make sense of high volumes of test data

 Organizations that implement continuous testing within Agile and DevOps execute a large variety of testing types multiple times a day. This includes unit, API, functional, accessibility, integration and other testing types.

With each test execution, the amount of test data that’s being created grows significantly, making the decision-making process harder. From understanding where the key issues in the product are, through visualizing the most unstable test cases and other areas to focus on, ML in test reporting and analysis makes life easier for executives.

With AI/ML systems, executives should be able to better slice and dice test data, understand trends and patterns, quantify business risks, and make decisions faster and continuously. For example, learning which CI jobs are more valuable or lengthy, or which platforms under test (mobile, web, desktop) are faultier than others.

Without the help or AI or ML, the work is error prone, manual and sometimes impossible. With AI/ML, practitioners of test data analysis can add features around such things as test impact analysis, test environment instabilities and recurring patterns in test failures.

2. Make informed and actionable decisions

 With DevOps, feature teams or squads are delivering new pieces of code and value to customers almost daily. Understanding the level of quality, usability and other aspects of code quality on each feature is a huge benefit to the developers.

By utilizing AI/ML to automatically scan the new code, analyze security issues and identify test coverage gaps, teams can advance their maturity and deliver better code faster. As an example, code-climate are able to automatically review any code changes upon a pull request and spot quality issues, and optimize the entire pipeline. In addition, many DevOps teams today leverage the feature flags technique to gradually expose new features, and hide them in cases of issues.

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With AI/ML algorithms, such decision making could be made easier by automatically validating and comparing between specific releases based on predefined datasets and acceptance criteria.

3. Enhance test stability over time through self-healing and other test impact analysis (TIA) abilities

 In traditional test automation projects, the test engineers often struggle to continuously maintain the scripts each time a new build is being delivered for testing, or new functionality is added to the app under test.

In most cases, these events break the test automation scripts — either due to a new element ID that was introduced or changed since the previous app, or a new platform-specific capability or popup was added that interferes with the test execution flow. In the mobile landscape specifically, new OS versions typically change the UI and add new alerts or security popups on top of the app. These kinds of unexpected events would break a standard test automation script.

With AI/ML and self-healing abilities, a test automation framework can automatically identify the change made to an element locator (ID), or a screen/flow that were added between predefined test automation steps, and either quickly fix them on the fly, or alert and suggest the quick fix to the developers. Obviously, with such capabilities, test scripts that are embedded into CI/CD schedulers will run much smoother and require less intervention by developers.

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 Machine Learning is Vital for DevOps 

As digital test data continuously grows along with the cadence of test cycles, ML systems can help identify patterns in the quality of web and mobile applications, and advise DevOps leaders where to focus their attention in order to optimize ongoing processes. Only at the test reporting and analysis phases can DevOps teams gain value from the overall testing activities.

When thinking about ML within the DevOps pipeline, it is also critical to consider how ML is able to analyze and monitor ongoing CI builds, and point out trends within build-acceptance testing, unit or API testing, and other testing areas. An ML algorithm can look into the entire CI pipeline and highlight builds that are consistently broken, lengthy or inefficient. In today’s reality, CI builds are often flaky, repeatedly failing without proper attention. With ML entering this process, the immediate value is a shorter cycle and more stable builds, which translates into faster feedback to developers and cost savings to the business.

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