2.7. Debugging

Debugging is a last resort when everything else has failed. Let’s take a step back and think about everything that comes before debugging.

2.7.1. Defenses against Bugs

According to Rob Miller, there are four defenses against bugs:

  1. The first defense against bugs is to make them impossible.

    Entire classes of bugs can be eradicated by choosing to program in languages that guarantee memory safety (that no part of memory can be accessed except through a pointer (or reference) that is valid for that region of memory) and type safety (that no value can be used in a way inconsistent with its type). The OCaml type system, for example, prevents programs from buffer overflows and meaningless operations (like adding a boolean to a float), whereas the C type system does not.

  2. The second defense against bugs is to use tools that find them.

    There are automated source-code analysis tools, like FindBugs, which can find many common kinds of bugs in Java programs, and SLAM, which is used to find bugs in device drivers. The subfield of CS known as formal methods studies how to use mathematics to specify and verify programs, that is, how to prove that programs have no bugs. We’ll study verification later in this course.

    Social methods such as code reviews and pair programming are also useful tools for finding bugs. Studies at IBM in the 1970s-1990s suggested that code reviews can be remarkably effective. In one study (Jones, 1991), code inspection found 65% of the known coding errors and 25% of the known documentation errors, whereas testing found only 20% of the coding errors and none of the documentation errors.

  3. The third defense against bugs is to make them immediately visible.

    The earlier a bug appears, the easier it is to diagnose and fix. If computation instead proceeds past the point of the bug, then that further computation might obscure where the failure really occurred. Assertions in the source code make programs “fail fast” and “fail loudly”, so that bugs appear immediately, and the programmer knows exactly where in the source code to look.

  4. The fourth defense against bugs is extensive testing.

    How can you know whether a piece of code has a particular bug? Write tests that would expose the bug, then confirm that your code doesn’t fail those tests. Unit tests for a relatively small piece of code, such as an individual function or module, are especially important to write at the same time as you develop that code. Running of those tests should be automated, so that if you ever break the code, you find out as soon as possible. (That’s really Defense 3 again.)

After all those defenses have failed, a programmer is forced to resort to debugging.

2.7.2. How to Debug

So you’ve discovered a bug. What next?

  1. Distill the bug into a small test case. Debugging is hard work, but the smaller the test case, the more likely you are to focus your attention on the piece of code where the bug lurks. Time spent on this distillation can therefore be time saved, because you won’t have to re-read lots of code. Don’t continue debugging until you have a small test case!

  2. Employ the scientific method. Formulate a hypothesis as to why the bug is occurring. You might even write down that hypothesis in a notebook, as if you were in a Chemistry lab, to clarify it in your own mind and keep track of what hypotheses you’ve already considered. Next, design an experiment to affirm or deny that hypothesis. Run your experiment and record the result. Based on what you’ve learned, reformulate your hypothesis. Continue until you have rationally, scientifically determined the cause of the bug.

  3. Fix the bug. The fix might be a simple correction of a typo. Or it might reveal a design flaw that causes you to make major changes. Consider whether you might need to apply the fix to other locations in your code base—for example, was it a copy and paste error? If so, do you need to refactor your code?

  4. Permanently add the small test case to your test suite. You wouldn’t want the bug to creep back into your code base. So keep track of that small test case by keeping it as part of your unit tests. That way, any time you make future changes, you will automatically be guarding against that same bug. Repeatedly running tests distilled from previous bugs is called regression testing.

2.7.3. Debugging in OCaml

Here are a couple tips on how to debug—if you are forced into it—in OCaml.

  • Print statements. Insert a print statement to ascertain the value of a variable. Suppose you want to know what the value of x is in the following function:

    let inc x = x + 1
    

    Just add the line below to print that value:

    let inc x =
      let () = print_int x in
      x + 1
    
  • Function traces. Suppose you want to see the trace of recursive calls and returns for a function. Use the #trace directive:

    # let rec fib x = if x <= 1 then 1 else fib (x - 1) + fib (x - 2);;
    # #trace fib;;
    

    If you evaluate fib 2, you will now see the following output:

    fib <-- 2
    fib <-- 0
    fib --> 1
    fib <-- 1
    fib --> 1
    fib --> 2
    

    To stop tracing, use the #untrace directive.

  • Debugger. OCaml has a debugging tool ocamldebug. You can find a tutorial on the OCaml website. Unless you are using Emacs as your editor, you will probably find this tool to be harder to use than just inserting print statements.

2.7.4. Defensive Programming

As we discussed earlier in the section on debugging, one defense against bugs is to make any bugs (or errors) immediately visible. That idea connects with idea of preconditions.

Consider this specification of random_int:

(** [random_int bound] is a random integer between 0 (inclusive)
    and [bound] (exclusive).  Requires: [bound] is greater than 0
    and less than 2^30. *)

If the client of random_int passes a value of bound that violates the “Requires” clause, such as -1, the implementation of random_int is free to do anything whatsoever. All bets are off when the client violates the precondition.

But the most helpful thing for random_int to do is to immediately expose the fact that the precondition was violated. After all, chances are that the client didn’t mean to violate it.

So the implementor of random_int would do well to check whether the precondition is violated, and if so, raise an exception. Here are three possibilities of that kind of defensive programming:

(* possibility 1 *)
let random_int bound =
  assert (bound > 0 && bound < 1 lsl 30);
  (* proceed with the implementation of the function *)

(* possibility 2 *)
let random_int bound =
  if not (bound > 0 && bound < 1 lsl 30)
  then invalid_arg "bound";
  (* proceed with the implementation of the function *)

(* possibility 3 *)
let random_int bound =
  if not (bound > 0 && bound < 1 lsl 30)
  then failwith "bound";
  (* proceed with the implementation of the function *)

The second possibility is probably the most informative to the client, because it uses the built-in function invalid_arg to raise the well-named exception Invalid_argument. In fact, that’s exactly what the standard library implementation of this function does.

The first possibility is probably most useful when you are trying to debug your own code, rather than choosing to expose a failed assertion to a client.

The third possibility differs from the second only in the name (Failure) of the exception that is raised. It might be useful in situations where the precondition involves more than just a single invalid argument.

In this example, checking the precondition is computationally cheap. In other cases, it might require a lot of computation, so the implementer of the function might prefer not to check the precondition, or only to check some inexpensive approximation to it.

Sometimes programmers worry unnecessarily that defensive programming will be too expensive—either in terms of the time it costs them to implement the checks initially, or in the run-time costs that will be paid in checking assertions. These concerns are far too often misplaced. The time and money it costs society to repair faults in software suggests that we could all afford to have programs that run a little more slowly.

Finally, the implementer might even choose to eliminate the precondition and restate it as a postcondition:

(** [random_int bound] is a random integer between 0 (inclusive)
    and [bound] (exclusive).  Raises: [Invalid_argument "bound"]
    unless [bound] is greater than 0 and less than 2^30. *)

Now instead of being free to do whatever when bound is too big or too small, random_int must raise an exception. For this function, that’s probably the best choice.

In this course, we’re not going to force you to program defensively. But if you’re savvy, you’ll start (or continue) doing it anyway. The small amount of time you spend coding up such defenses will save you hours of time in debugging, making you a more productive programmer.