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Debugging Memory Corruption in Game Development


October 16, 2008 Article Start Previous Page 3 of 6 Next
 

Try tracing through the code, stepping over functions, when you find one that does the corruption, and then next time around, dig into that function.

If you don't have this debugger functionality, then you can still roll your own by writing a little function that checks that memory location. You can then sprinkle calls to this function through your code, narrowing down the region of code that causes the error. If the memory location's address is dependent on the code, you may need to compile the code, note the new address, and then re-compile with the new address wired into the code.

Another manual method is to keep track of allocations that include that particular location. Memory corruption is often due to a dangling pointer that was once legal. So if you know the location of corruption in advance, then having a list of the callstacks of all the allocations that once owned that location can help quickly identify the culprit.

Identifying Hex Droppings

A memory location has been corrupted. Assuming you cannot quickly find what bit of code is responsible for the corruption, you can learn a lot about what that piece of code might be by examining the nature of the corruption.

Once you have identified the location that has been corrupted, then look at a hex dump of it in the debugger (or print out your own if a debugger is not available). A hex dump looks something like this.


The memory address is on the left, then comes the contents of memory, here listed eight bytes to a row, and then those eight bytes are repeated as ASCII characters

Single Bit Corruption

Few pieces of code will cause only a single bit to be flipped. The most likely candidate is a bit-field of flags.

Single Byte Corruption

If only one byte was modified, then that can narrow down the fields considerably. If the corrupt value is 0 or 1, then perhaps it is a byte flag.

Single 32-bit Word Corruption

A 32 bit word is often the fastest and most convenient way of storing data. It is the only way for certain data types such as floats or pointers (depending on your platform). Looking at the contents of the 32 bits will tell you something about the code that inserted that value there.

If you know that a 32 bit value is being corrupted, then you should view the memory location as a single 32 bit word, rather than as a sequence of four bytes. This removes any confusion with endianness, and makes the type of data much easier to recognize.

That said, it is also a useful skill to be able to recognize certain types of data as a byte stream, since the data may be intermingled (in a class) with other data of varying types. In the examples below we give the values both as a 32-bit integer, and as a four byte little-endian format, which harder to recognize than big-endian, since that is just the word with the bytes spread out.

Zero

Example:

00000000 or 00 00 00 00

Zero is easy to recognize. At first, you might not think there is much information in a zero, but consider the limited number of reasons a piece of code could be writing a single zero to a location in memory, and it may give some clue as to what piece of code might be responsible.

Zero is:

NULL - Perhaps the errant code is clearing a pointer? Some programmers make a habit of cleaning any pointer that is a member variable after they have deleted whatever it was pointing to (a reasonable practice to help prevent dangling pointers). However they might be doing it at the wrong time.

Zero. Both as an integer (0) and as a floating point (0.0f). Where in the code are individual values set to zero?

FALSE. Perhaps the code is treating the location as a flag, and simply setting it to FALSE.

The first value in an enum - Perhaps a type field, of a status field. What kinds of enumerations do you have in your suspect code? What does the first entry mean? What causes the code to write out the first value?

Clear and empty - Often data structures are initialized to zero. Does this happen anywhere in the code? Does the size of the data being cleared match the zeros in the corruption?

One

Example

00000001 or 01 00 00 00

One is also easy to recognize. Less common that zero, it can still tell you something about the code that wrote it there.

One is

TRUE - Perhaps it is being used as a flag. What could be set to TRUE?

An integer - Hence it's not a floating point number. You can discount code that stores floats.

Not a pointer - Odds are that the code causing the corruption is not thinking that it is storing a pointer, unless it is a secondary bug.

The first value of an enum - like any small number, it's possible it is an enumerated value, possibly a type number.

Floating Point Numbers

Example

3F800000 or 00 00 00 80 3F

Many floating point numbers have an easily recognizable format. A very common floating point value is the one shown above, 3F800000 is the hex representation of the 32-bit floating point value of 1.0. See Table 24.X for additional values.


Table 24.X

Notice how the small values start with a 3. A floating point number has the first bit being the sign, the next eights bit being the exponent, and the following 23 bits being the fractional part. Since numbers in the same range tend to have similar exponents, you can often recognize a group of floating point numbers of similar magnitude.

In games, a very common range for floating point values is from -1.0 to +1.0. These numbers are used extensively in unit vectors, transformation matrices, UV coordinates and scaling factors. Numbers in this range usually start with a 3 (for positive numbers), or a B (for negative numbers).

If you suspect it is a floating point number, you can then sometimes tell if it is an original (hard wired in the code) value, or a value arrived at by calculation. Consider the numbers above. The values 1.0, 2.0, 0.5, 100.0 all have trailing zeros in their hex representation. The value 3.3333334 also a sequence of AAAAAA in it.

By contrast, the less rational number 3.14159274 has what seems to be a random string of hex digits. We can see the degree of entropy in the hex number matches that in the floating point number.

So, a floating point number that has been the subject of some computations is much more likely to have random looking hex digits. Hence, you can tell if you are looking for code more like from an update function:

p->m_speed = sqrtf(p->m_speed*p_m_speed - 2.0 * g * h);

or from an initialization function

p->m_speed = 2.5f;

Article Start Previous Page 3 of 6 Next

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