Better Bug Hunting for Improved Team Productivity: Announcing Eggplant AI 2.0

By JB Brockman | 2/13/18

We recently co-hosted a webinar with Bloor Research about the Future of Testing, and in it, we conducted an informal poll about artificial intelligence (AI) and testing. When we asked what everyone thought the biggest advantage was to incorporating AI into a test automation strategy, attendees overwhelmingly selected team productivity and efficiency.

Poll - What do you see as the biggest advantge to incorporating AI into a test automation strategy.jpeg

Intelligent test automation represents a huge opportunity to boost team productivity. The latest release of the brains behind our Digital Automation Intelligence Suite, Eggplant AI 2.0, empowers teams to test effectively within the shrinking test windows associated with continuous delivery, and to consistently create amazing digital experiences. Using enhanced AI, machine learning, and analytics capabilities, Eggplant AI 2.0 helps teams intelligently navigate applications, predict where quality issues are most likely to pop up, and correlate data to quickly identify and resolve issues.

Improvements include:

  • Advanced bug-hunting, machine-learning algorithms. By analyzing failure patterns across test runs, Eggplant AI 2.0 can help teams refine tests to actually find defects. For example, maybe defects are happening when users select submenu items on screens that also show interactive maps. Or, maybe users experience issues when entering date of birth information on a search screen when it’s in the MMYY form.
  • Support for user-defined directed tests. Through the Eggplant AI 2.0 GUI, teams can define explicit test paths simply and quickly — for both manually defined regression tests and advanced, intelligent, AI-based exploratory tests — from the same model. For instance, teams can test core functionality, like adding an item to a basket, checking out, and paying for the item before conducting algorithmic-derived tests to remove the item, rotate the screen, select a new item, etc.
  • Ability to annotate the model with customer-defined properties. Fuel the analytics within the software by adding user-configurable properties and values to your Eggplant AI model. For example, you can attribute properties such as complexity (high, medium, low), GDPR relevancy (none, undefined, relevant), development team (offshore, local), and technology used (D3, Spring, C#, JS) to any action or state.

Efficiency, productivity, and increased test coverage equals a better user experience and increased business value. The latest enhancements to Eggplant AI 2.0 are based on discussions with and feedback from more than 400 enterprise companies about driving business success. For healthcare companies, success translates to better care and patient outcomes. For defense organizations, success equates to lives protected. For retailers, success means satisfied customers and high NPS scores for every app release.

“Eggplant AI 2.0 is a huge leap forward in the evolution of test automation. This is the future of testing and is the only way software and app vendors are going to keep up with the demands of users and the pace of DevOps.” — Antony Edwards, CTO, Testplant

To learn more about Eggplant AI 2.0, read the release.

Watch our Future of Testing webinar to explore the importance of AI in a test automation strategy.

Share your thoughts about AI and testing in the comments. We’d love to hear from you.

Topics: Digital Experience, Test automation, User Experience, Software test automation, testing strategy, artificial intelligence, user journey, analytics, continuous delivery, digital automation intelligence, test automation strategy

JB Brockman

Written by JB Brockman

JB writes stuff as the content marketing lead at Eggplant.

Stay up-to-date with the latest in test automation

Lists by Topic

see all