How AI Learns to Test Software in the Cloud

This patent describes a cloud system where a single AI model learns to test many live software applications at once by trying actions, getting feedback, and improving itself.

What it covers

This technology provides a way for a single artificial intelligence (AI) model, specifically a reinforcement learning model, to automatically test multiple versions of a software application running live in a cloud environment. The AI model first selects an action to take in the user interface of a software instance, such as clicking a button (Claim 1). It then causes that action to be performed and receives a 'reward' based on the outcome, like whether a task was completed (Claim 1, Claim 8). This process is repeated for other software instances, potentially running on different devices (Claim 5, Claim 10). The AI model then uses these actions and rewards from all instances to update and improve its testing strategy (Claim 1). For example, the AI could be testing a new mobile app, learning which sequence of taps and swipes leads to a successful login or exposes a bug.

What it doesn't cover

• —Does not cover software testing performed solely by human users.
• —Does not cover testing in simulated or offline environments, as it specifies 'live environments'.
• —Does not cover testing where the AI does not interact with a graphical 'user interface' or 'interface element'.
• —Does not cover using a separate reinforcement learning model for each individual software instance being tested.
• —Does not cover testing methods that do not involve determining a 'reward' to train the learning model.

The clever bit

The novelty lies in using a *single* reinforcement learning model to interact with and learn from *multiple live software environments* simultaneously in a distributed cloud setting. This allows the AI to gather diverse experiences and improve its testing capabilities much faster than if it were testing one instance at a time.

Why it matters

Automated software testing is crucial for quickly releasing high-quality applications. This patent describes a system that allows a single AI to efficiently test many versions of software simultaneously, which can significantly speed up the development cycle. By leveraging cloud computing, it enables large-scale, continuous testing that would be impractical or impossible with traditional methods.

Real-world examples

• 1.Automated UI testing for web applications
• 2.AI-driven quality assurance for mobile apps
• 3.Testing new features in enterprise software across many user configurations
• 4.Continuous integration/continuous deployment (CI/CD) pipelines