\textit{Heisenbugs}, notorious for their ability to change behavior and elude reproducibility under observation, are among the toughest challenges in debugging programs. They often evade static analysis tools, making them especially prevalent in cyber-physical edge systems characterized by complex dynamics and unpredictable interactions with physical environments. This paper broadly evaluates the identification of heisenbugs through the lens of \textit{observation-oriented} dynamic data race detection, where true measure of effectiveness against such ``difficult bugs'' is poorly explored primarily due to the lack of reliable and objective metrics of the bugs’ difficulties. Application timing constraints for low overhead and jitter further cripple the usability of most tools against such bugs in practice, impeding their adoption during deployment.

We emphasize the critical impact of \textit{execution diversity} across both instrumentation density and hardware platforms for observation-oriented heisenbug detection; the benefits of which outweigh any reduction in efficiency from limited instrumentation or weaker devices. We develop an experimental WebAssembly-backed data-race detection framework, Beanstalk, which exploits this diversity to show superior effectiveness compared to \textit{any} homogeneous instrumentation strategy on a fixed compute budget. Beanstalk’s approach also gains power with \textit{scale}, making it suitable even for low-overhead deployments across numerous compute nodes. Finally, based on a rigorous statistical treatment of bugs observed by Beanstalk, we propose a novel metric, the \textit{heisen factor}, that similar detectors can utilize to categorize heisenbugs and measure effectiveness. We use this to provide insights on effective debugging both in-house and at deployment, and explore the generalizability of the heisen factor to other quantifiable bug classes beyond data-races.