AI in Testing

AI Testing
10 Apr 2020

AI in Testing

AI is a broad category of tech, including machine learning, where software is ‘trained’ to do basic tasks with or without human instruction. AI even includes work on Artificial General Intelligence (AGI) — the work to construct conscious — even superintelligent — machines

AI and testers will be soon best friend forever. Potential of applications of AI for testing are beyond imaginations

AI is a way to build software that can replicate human-style judgment. The explosion of AI as a field, combined with affordable computing, means that AI can be applied to some of the most challenging aspects of automated testing and deliver AI-assisted testing for humans.

If generating test cases isn’t enough to commit to BFF status with AI, Infosys now has an offering for “artificial intelligence-led quality assurance.” The idea is that the Infosys system uses data in your existing QA systems (defects, resolutions, source code repo, test cases, logging, etc.) to help identify problem areas in the product.

AI and Automation Testing

evolution of testing

The rise of test automation has coincided with the adoption of Agile methodologies in software development. This enables teams to deliver robust and bug-free software in small batches. Manual testing is limited to business acceptance testing only. Test Automation along with DevOps helps Agile teams to ship a fail-safe product for SaaS/ cloud deployment via a CI/ CD pipeline

Advantages of AI based Testing

  1. Automating Visual Validation
    • There are many ML-based visual validation tools that can detect minor UI anomalies that human eyes are likely to miss
  2. Automatically Writing Test Cases
    • While learning the application, they automatically crawl and collect useful data like screenshots, HTML pages and page loading time. Over time they collect enough data from the application so that they can train the ML model for expected patterns of the app
  3. Improving Reliability
    • AI/ ML tools can read the changes made to the application and understand the relationship between them. Such self-healing scripts observe changes in the application and start learning the pattern of changes and then can identify a change at runtime without you having to do anything. As the app evolves the ML scripts adjust automatically, reducing flakiness and fragility of test automation.
  4. Reduced UI-based Testing
    • Another change brought by AI/ML to automation testing is automation without the user interface. Non-functional tests like Unit Integration, performance, security, and vulnerability are also no exception. AI/ML-based techniques can be applied for generating tests in these layers

How will be testers and BFF do

Maybe there is hope in the length of the runway between here and where AI takes off. However Tester and AI [BFF] will have a great deal to soon to work together to achieve greater results.

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