Software Testing 101

Intelligent Test Automation

November 19, 2018

Intelligent test automation is oft-repeated buzzword in the world of software testing and test automation. But what comprises intelligent testing? Essentially it is the combination of machine learning and AI-enabled analytics, and continuous feedback that helps testing teams to predict outcomes, minimizes risks, and provides incrementally high quality software with every cycle.

The World Quality Report 2018-19 talks about the correlation between quality assurance and customer satisfaction. One of the key recommendations of this report is to “Increase the level of basic and smart test automation, but do so in a smart, phased manner.” The report talks about the double-edged sword that is automation. “Automatoin’s key role as an enabler of successful agile and DevOps transformation.” But also, “automation is the biggest bottleneck holding back the evolution of QA and testing today.”

Intelligent Test Automation is the evolutionary approach that extends automation’s capabilities to cover more ground now and faster. Thanks to automation, there is availability of large volumes of test data – historical and real time. All we need to do is to gain the right perspective from this information.

Intelligent testing can sharpen up every aspect of your business from achieving sales targets to enhanced time to market. The idea is not only to gain intelligence, but actionable insights based on software quality metrics. This proactive and prescriptive approach to anticipating defects and resolving them before they happen is what makes this approach a game-changer.

Using the vast amounts of data already in your test automation suites, Intelligent Test Automation can help you focus on high-risk error categories. So that you can set priorities defined by business needs, providing direction to focus on and execution results by platform.

Furthermore, it provides a reusable framework with data-driven testing and the adequate amount of test coverage and test depth for leaner management. With service layer and GUI layer automation you get access to end-to-end stability. This is cognitive QA at its best that leverages Machine Learning and Artificial Intelligence to continuously learn from your data and improve your automation cycle with prescriptive analytics.

Let’s look at how AI and ML-led intelligent test automation transforms processes and makes software development smarter, faster and more efficient:

1. Optimizing your test suite

One of the key problems facing software development and testing is over-engineering that results in a considerable loss of time, effort and resources. As businesses release faster and use automation to move forth, they often struggle with large amounts of backlog. AI BOTS can help clear the backlog and enable focusing on testing right. BOTs can identify similar/redundant and unique test cases, thereby removing the duplicates and enhancing traceability.

2. Instant feedback

One of the main benefits of intelligent test result analysis is the instant feedback and enhanced response time. The smarter analytics identifies hotspots easily and executes test cases automatically. An obvious use for AI is to take over the highly repetitive tasks that call for repeated inputs and report the outputs. AI can mimic these input-based scenarios very easily be it user actions for mobile app testing or more complex combinations. Based on historical information,

3. Predictive and Prescriptive Analytics

BOTS thrive on data and luckily most businesses have vast amounts of production and test data generated by automation suites. AI and ML can be used to analyze test results, identify defects and predict the quality. It uses this data to predict the key parameters of processes and prescribe the best course of actions. An intelligent approach to identify usage and failure trends to spot the most critical and less obvious faults.

4. Defect analysis

An intelligent approach to identify defects early and provide suggestions to avoid these failures and speed up cycle time. AI can accurately detect usage and failure trends to spot the most critical and less obvious faults. This allows teams to prioritize regression test cases based on risks identified.

QMetry’s intelligent platform dives deep into your automation suite to gain insight in to failures, root causes and offers actionable insights and suggestive test action models. Our integrated command and control center connects the dots from multiple automation tools, CI/CD jobs and other tools in your toolchain. Not only do you get more visibility and traceability, you also get a sense of direction and focus on what’s necessary and important.

  • Consolidate information from multiple tools and thousands of test cases
  • Eliminate redundant test cases and avoidable scenarios with predictive insights
  • Achieve ideal amount of coverage increasing your efficiency
  • Focus on high-risk and high-priority scenarios
  • Use real-time insights to optimize and refine the QA lifecycle incrementally and continuously, ultimately leading to defining the scope of your test activity

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