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A heap overflow, heap overrun, or heap smashing is a type of buffer overflow that occurs in the heap data area. Heap overflows are exploitable in a different manner to that of stack-based overflows. Memory on the heap is dynamically allocated at runtime and typically contains program data.
A code sanitizer is a programming tool that detects bugs in the form of undefined or suspicious behavior by a compiler inserting instrumentation code at runtime. The class of tools was first introduced by Google's AddressSanitizer (or ASan) of 2012, which uses directly mapped shadow memory to detect memory corruption such as buffer overflows or accesses to a dangling pointer (use-after-free).
Example of a binary max-heap with node keys being integers between 1 and 100. In computer science, a heap is a tree-based data structure that satisfies the heap property: In a max heap, for any given node C, if P is the parent node of C, then the key (the value) of P is greater than or equal to the key of C.
Use after free – dereferencing a dangling pointer storing the address of an object that has been deleted. Double free – repeated calls to free may prematurely free a new object at the same address. If the exact address has not been reused, other corruption may occur, especially in allocators that use free lists.
The heapsort algorithm can be divided into two phases: heap construction, and heap extraction. The heap is an implicit data structure which takes no space beyond the array of objects to be sorted; the array is interpreted as a complete binary tree where each array element is a node and each node's parent and child links are defined by simple arithmetic on the array indexes.
Example of a complete binary max-heap Example of a complete binary min heap. A binary heap is a heap data structure that takes the form of a binary tree.Binary heaps are a common way of implementing priority queues.
Illustration of the table-heap compaction algorithm. Objects that the marking phase has determined to be reachable (live) are colored, free space is blank. A table-based algorithm was first described by Haddon and Waite in 1967. [1] It preserves the relative placement of the live objects in the heap, and requires only a constant amount of overhead.
The hit ratio of a cache describes how often a searched-for item is found. More efficient replacement policies track more usage information to improve the hit rate for a given cache size. The latency of a cache describes how long after requesting a desired item the cache can return that item when there is a hit.