Kobiton, a leading mobile app testing company, has announced plans to introduce an AI-powered issue aggregation engine early next year. This new feature is designed to enhance the debugging process by automatically grouping related errors and identifying patterns across multiple test sessions. By leveraging artificial intelligence, the engine aims to streamline issue detection and resolution, ultimately improving the efficiency of mobile app testing.
According to Kobiton, the AI engine will help uncover hidden connections between issues that might appear unrelated at first glance. For example, a recurring problem such as button occlusion on various devices may actually stem from a common factor like screen resolution. By consolidating such errors into a single, overarching bug, the AI engine reduces manual troubleshooting efforts, allowing developers to focus on more critical aspects of app performance.
Currently, Kobiton enables users to integrate their own AI algorithms, along with those from third-party providers, to analyze mobile device test data. This data, often referred to as “exhaust,” includes test steps, screenshots, full video capture, system logs, network activity, and other key performance metrics. The AI-driven insights derived from this rich dataset help testers identify and address app issues more effectively.
Kobiton’s AI capabilities are further enhanced through strategic partnerships with AI-focused companies like Applitools. When these advanced tools detect potential issues, the findings are fed into the Kobiton Session Explorer, a platform that provides a detailed timeline of test sessions. This real-time visibility allows developers to pinpoint exactly when and where an issue occurred, facilitating faster resolutions and more seamless mobile app experiences.