Artificial Intelligence

From customer service to cybersecurity to transportation, artificial intelligence is reengineering decision-making across industries and allowing human workers and, in turn, their organizations, to operate more intelligently and competitively. In the technology sector, a key benefit of AI is its power to transform traditional approaches to software and application development and testing.

Artificial intelligence mac computer

Testing from the user perspective

Looking at testing specifically, AI can automate numerous functions, enabling companies to expand automation beyond test execution, reduce errors and predict the quantified impact of new product versions prior to release.

At Eggplant, we’re harnessing AI to deliver an even more crucial testing benefit—testing from the user perspective. This allows us to test the entire customer experience, including functionality, performance and usability, across any device, browser or operating system. In addition, our user-centric approach to testing enables us to monitor how customers are actually interacting with the technology—and how these interactions could be improved to drive positive business outcomes.

Eggplant’s AI-driven approach to testing creates a model of the system and user journeys and automatically generates test cases that provide robust coverage of the user experience, as well as of system performance and functionality. Through automated feedback loops, companies can quickly identify and address problems to ensure that their product is continuing to delight customers and align with business objectives. And because we ensure that any UI errors, bugs or performance issues are spotted and addressed before release, we eliminate the negative brand perception and customer impact typically associated with technology glitches.

Additional features of AI-driven testing include:

  • Regression packs: The definition of mission critical end-to-end tests that must pass prior to shipping the product. AI and machine learning can then be applied to the information gleaned from these fixed tests to identify other test cases to ensure the product delights in the field.
  • Bug hunting: Advanced machine learning can correlate common factors and attributes of historic defects to identify new attributes that indicate the highest likelihood of discovering new defects.
  • Coverage analysis: Analyzing where you have historically been in the model and also providing a balanced view to ensure as much test coverage as possible.

Other Needs Supported by Eggplant

Automation
Automation
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Continuous Testing
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Digital Transformation
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Monitoring
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