A trace anomaly grouping and visualization technique logically groups traces with anomalies to cases to enable software developers to monitor, diagnose and visualize the anomalies, as well as to solve the anomalies during application development and production. A client library of an investigative platform collects signals from traces (trace signal information). The technique organizes (groups) related trace signals of methods with anomalies (e.g., exceptions, performance abnormalities such as slowness) into datasets (denominated as “cases”) based on common cause for an anomaly and correlates the signals to identify a case. The collected information may be used to differentiate between root causes of the anomalies using a comparative visualization of traces displayed on a standard user interface of the investigative platform. As such, the technique facilitates an understanding of differences among traces of executable code that resulted in the failure and traces without failure by providing the ability to comparatively examine views of those traces displayed on the standard UI. Signals of two or more traces may be selected and displayed side-by-side for comparison. The traces may be selected from a general notion of a healthy trace and a failed trace.