Communications of the ACM
Technical Perspective: BLeak: Semantics-Aware Leak Detection in the Web
Web applications are at least as likely to leak memory as regular applications. Web leaks can significantly increase a browser's memory footprint, reducing application responsiveness and even crashing browser tabs. Such leaks exist everywhere, on websites that people use on a daily basis—Google Maps, Firefox, Google Analytics, or Airbnb, just to name a few. They are notoriously difficult to diagnose: developers see the growth of memory usage, but where exactly are the statements that cause the growth?
Despite a rich literature of leak detection for regular (Java, C++, Python, and so on) applications, prior techniques do not work well for Web applications where leaks exhibit very different characteristics. For example, the developer may forget to remove certain event listeners and hence these listener objects are still reachable in the heap. While they are no longer used by the application, they still respond to events (for example, when the user uses the mouse on the editor), keeping their states "fresh." As a result, existing techniques that identify suspicious objects based on their staleness (that is, time since their last access)—which have worked effectively on a wide range of traditional applications—would miss these leaks in Web applications entirely.
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