Writing Solid Code in GEOS


Use Compiler Warnings

Enable all compiler warnings, and pay attention to them.
Don't install code that triggers warnings.

Compiler warnings exist to warn you of possible problems. You should pay attention to compiler warnings. If you get a warning, you should change your code so that the warning is not triggered.

The most common warnings are related to branch-out-of-range warnings, and unused label warnings.

If you get a branch-out-of-range warning, it probably means your routine is too long. Make it smaller by moving code into subroutines. If the routine is not too large, but you still have this sort of warning, you can use the 'LONG' macro in front of the branch to get rid of the warning.

If you have warnings about unreferenced variables, comment out the variables, or use the ForceRef macro to force a reference to the variable in question.

Use EC Code to do Parameter Checking

Use assertions to validate function arguments.

The kernel is rife with EC code. All of the graphics routines validate the graphics-state handle which is passed in di. The memory routines validate the handles passed in bx. This code allows bugs to be caught early, when the mistake is an obvious one. Without this sort of EC code, these bugs would be caught much later when some obscure side-effect shows up.

You should take the same approach with your own code. If you are writing an API that other developers will use you should provide EC code that validates the parameters which are passed to your API level routines.

If you are writing modules of an application, or are creating APIs for internal use, you should seriously consider scheduling time to write EC code to validate what is passed to these internal APIs.

Simplify and Solidify Your Code

Use assertions to detect "impossible" conditions.

EC code can be valuable internally, not only for situations where you wish to validate parameters to functions, but also internally to verify that the data you are using is correct. This sort of thing can help you locate bugs where bad pointers or corrupted data might cause problems:

This code example will catch the possibility of the instance data being uninitialized, or somehow corrupted. It will catch the case where the pointer (ds:di) is somehow not correct (perhaps the object or heap moved due to some earlier action). Finally, it will verify that actions within the loop do not cause cx to be corrupted.

Remember, not all bugs will cause a corruption of data or a crash. It is possible that a bad value in CX might only cause some delay, or possibly an attempt to interpret bad data.

Write EC Code to Verify Data-Structures

Don't wait for bugs to happen, use EC code to validate your internal data-structures.
Maintain debug information to allow for stronger error checking.
Create thorough subsystem checks, and use them often.

These are all extremely important if you are writing a subsystem that is responsible for managing data-structures. Currently our system includes code to validate an entire text object (and the various related data- structures), the entire system heap, an entire lmem heap, etc. These have proved invaluable in tracking down difficult problems. If you write this code at the same time that you are creating your data-management routines, it can help you from the very beginning, and it will take less effort to code and maintain the data-structure validation routines.

It is OK to keep around extra data in order to help you verify your own data-structures, as long as this data does not exist in the non-EC version. It is important to keep this data separate. For instance it is more appropriate to keep a separate array of extra EC data, than it is to add fields to a data-structure, but only in the EC code. This is especially true if you are dealing with data which will be saved to a file. You want to be able to use the same files in the EC and non-EC versions of your code.

Use second algorithms to verify the results of your primary algorithms.

Sometimes it may make sense to keep a totally separate "brain-dead" algorithm around to verify that your highly optimized approach is truly working correctly. For instance it may make sense to add EC code to a sorting algorithm that actually verifies that the entries end up sorted.

Design with Testing and Error Detection in Mind

Spend time to carefully design your tests.

Your test code is important. Spend as much time designing and documenting your test code as you would in designing and debugging the systems which this code will test.

Strive to implement transparent integrity checks.

Checking the integrity of your data-structures will give you (and the users of your code) a confirmation that your code is working as expected. In a database program I wrote, it was possible for a sequence of actions to corrupt a database item other than the one the user was looking at (a random scribbling bug). If I had used integrity checking code, I would have identified this bug at the time it happened rather than spending weeks tracking a problem that didn't actually make itself apparent until much later.

Don't apply normal constraints to EC code. Trade size and speed for error detection.

EC code is not a part of your product. If it makes the code larger and slower, that's fine. The only exception is when it makes the code so large and slow that it is completely unusable. A slowdown of 50% in the EC version may be perfectly acceptable.

Use well defined data-types.

Esp provides the ability to create meaningful enumerated types, structures, and records. Take advantage of these to produce code which is more readable, and which gives a clearer indication of what you mean.

Code with Testing and Error Detection in Mind

Destroy garbage, so that it can't be accidentally used later.

Currently the system will do this with the main heap and the lmem heap. If you are maintaining your own local data-structure heap, you should consider destroying data when it is freed so that it won't be mistaken for useful data later.

Write comments that emphasize potential hazards.

Once you have done a good job designing and documenting your code, you should make sure that you take the time to focus on what can go wrong. This should start in the design phase ("what happens if the file doesn't exist?") and should continue through coding. During the coding phase you should take care to document (in your code) the potential problems that the code is handling. This will allow other people who will be working in your code to understand the problems that they will need to deal with.

Always ask, "Can this variable or expression over or under-flow?"

In our system, over/underflow is rare, but there are frequently problems associated with signed versus unsigned values. In particular making the mistake of using jg instead of ja can result in your code failing miserably when an index becomes >=0x8000. It is very rare that you will want to used signed comparisons or branches, so pay close attention to your code in the cases where you do.

Implement "the task" just once. If you find yourself writing multiple copies of roughly the same code, you should be examining your algorithm more closely.
Handle your special cases just once.

If you find yourself implementing a routine that contains several special actions for a certain case, spend the time to examine your approach to see if you can get rid of the special case. The simpler the code (i.e. the fewer the special cases) the easier it will be to debug.

Get rid of extraneous "if" statements. Where ever possible, reduce the number of decisions your code must make.

Code with lots of branches is harder to debug. You must step through both paths to verify that it works, and even worse, it can create several new paths through your whole code body. Where possible, remove "if/then" structures. Table lookups are great for this, and there may be other approaches that work. Code that does not branch is incredibly easy to debug. Try to write as much of it as you can.


Last modified: Thu Jan 8 10:44:22 PST

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