The Scientific Method in Debugging.

How to know when it works.

Now, we’ve all experienced some kind of problems when using computers and understand quite how fustrating it can be when the solution doesn’t appear to be in plain sight. This can often be quite a fustrating experience for both the user, or the person whose function it is to fix the problem.

Of course, when one is under pressure, or when something has been a problem for a while, then it’s all too easy to regard a problem as being intractable, or even impossible to solve. We might even begin to imagine that the computer (or other device) has a will of it’s own, and intends to thwart our good intensions, asserting that the machine contains “Gremlins” or other malevolent entities. We might then attempt to exorcize the system by perhaps, rebooting the machine, or reinstalling the offending item of software. We might even resort to hurling insults at people who maintain other components of the system in use. By and large, this is complete and utter hogwash, borne from our own fustrations. In fact these techniques are often entirely ineffective, and may serve to impede our diagnoses.

The fact is, that when analysed with a cool mind, we can realise that the system is simply a set of components, each of which is composed of smaller components, and so-on. For example, a service (say, an online application) may be composed of a set of front-end webservers, middleware servers, and backend relational database servers. The database servers are composed of a network stack, the RDBMS instance (say, an installation of Oracle or PostgreSQL, &c), and the storage stack. The storage stack might be composed of a filesystem, the block device layer, and the physical storage, so on and so on, until we’re worrying about how many electrons there are in a bit of silicon or germanium compared to another part (although most people, understandably, don’t need to go that far). Of course, there’s not always a clean seperation between components, but accounting for that is left as an exercise for the reader.

An example of the stacks given in the paragraph above

Each of these components can usually be considered to be a black box, which takes certain inputs, and produces certain results. So, as long as we have an understanding of what should happen at each stage, then we’ve got a firm basis to start our debugging.


First of all, we need to observe what’s occurring, and note how it differs from our model of the inputs and outputs of the system as a whole. For example, we might have a web application which is mangling internationalized text, such that non-english characters (such as accented characters) come out as two or more bogus characters. With some knowledge of Unicode and character sets, we can hypothesize that the application is encoding what it thinks is ISO-8859-1 text as UTF-8 (as demonstrated in Iñtërnâlizætiøn by Sam Ruby). All one really needs to know at this point is to have an idea of how it should look, under various circumstances.

For example, we know what the french word “vélo” looks like, and we can do a test to see how it should be encoded on the wire:

: cez@apsu; echo vélo | sed -ne l
: cez@apsu; 

Here, we use the “l” command in sed(1) prints out the current line in a “visually unambiguous” form, and handily, displays octets outside of the ASCII range as their escaped octal equivalents.

So, from this we see that “é” is represented in UTF-8 as two octets. For the time being, we needn’t worry about the exact value of those octets, although those impatient to know should read RFC 3629.

You’ll also need to know enough about the protocols used between each of components to account for any re-encoding of the data. For example in the vast majority of XML-based protocols (eg: the Atom Publishing Protocol), the encoding is specified either as a parameter to the Content-Type; or within the XML declaration (if it’s specified in both places, this may lead to inconsistencies, and problems), so we need to inspect those to account for any changes in encoding. However, a change in the declared encoding may signify a problem in itself, of course.

So, armed with this information, we can work though the stack incrementally, inspecting the traffic at each point in the highest level of the stack to check that the data is being transmitted correctly.

So, in our example, you would inspect traffic:

And vica versa:

For example, you could use tcpdump(8) to inspect the traffic as it flows over the wire between each component. (Admittedly, I’m yet to meet a user who could be inspected using a network packet capture tool).

Now this implies that you must work your way through the stack one level at a time; not so. Personally, I find that this approach would work for systems composed of different components, each with a differing function. However, if you’re dealing with a system which is composed of quite homogenous components (for example IP routers, or more generally parts of the system which shuffle a bit of data along to the next part without altering it much), then it can be useful to take a binary search model, whereby you start in the middle, and eliminate the halves of the model which appear to be operating correctly.

Of course, whilst this can safe some amount of work, this can miss problems when a component will be symetrically broken. This might be the case where you’re sending UTF-8 data to a database which is expecting ISO-8859-1 text, but stores it again in UTF-8. The data may be stored doubly encoded, but it’ll be decoded when it’s sent back out again. In some cases, this may not be a problem at present, but it’ll serve to trip us up in the future, should we ever need to go around the back of the component.

Hypothesis and Experimentation

Now, armed with this information, we might find that the data going in one side of the middleware is correctly encoded, but it’s producing the wrong output. In this case, we need to be able to extract that component from the system, and test it as a single unit. Sometimes, a software package will provide a suite of software tests that you use to start from, and it can be useful to integrate your failing example into these tests (if possible) to aid in the diagnosis. This helps to ensure that our tests are repeatable.

But we needn’t always be quite so formal about it. Sometimes, all that’s required are a few quick invocations of a command line interface in order to test a hypothesis. But in general, we should endevor to document the tests we’ve done (for example, in a trouble ticket) to ensure that our tests are repeatable, and that we don’t do the same test twice. On the other hand, we may need to go to the trouble of setting up a duplicate environment to test against (although many shops will have one of these in place already).

So once we’ve established that we have the faulty component, we must use our knowledge of the component’s internals to subdivide it into it’s parts, and then do appropriate tests on these parts. For example, you might add code to the suspected code-paths to log the state of the data at strateigic points. Although, we need to be wary of how this data may be inplicitly transformed when it’s either taken into, or output from, the system. Languages such as Perl which attempt to automagically treat strings as unicode where appropriate can be dangerous, because it’s not always obvious where this is occurring, how, or why, so this may lead to inaccurate, or just plain wrong results.

Now, the line between what constitutes experimentation and observation can be somewhat fuzzy, mostly because in practice, the steps of “trying an experiment” and “observing the results” get conflated into one. However, proper practice of experimentation is vitally important when testing a hypothesis, because if you don’t check that you’re actually looking at the correct component, you’re liable to be wasting large amounts of time. If you’re not able to reproduce the problem in an isolated environment, then it’s possible that you’ve either neglected something which affects the results, or you’re testing the wrong component.

For example, some time ago, the company I was working for had a provisioning application which was largely based around Perl DBI and PostgreSQL. Then one day, we suddenly found that some transactions were hitting a deadlock. At this point, two methodologies were applied by two seperate parties:

After a few hours of experimentation, and investigation by both parties (and a few upgrades of PostgreSQL) later, we traced the problem to a long running application, and an unforseen consequence of DBI’s behavior when AutoCommit was turned off. DBI will implicitly begin a transaction when you first issue a query in this mode, and so the transaction will only be over (and held resources released) once an explicit COMMIT or ROLLBACK instruction is sent to the database. The application concerned was caching datahase connections (as creating a connection to PostgreSQL is relatively expensive, as each connection is served by a seperate Unix process), but it wasn’t rolling back the current transaction when some part of the process failed.

It wasn’t entirely clear to either party exactly why this behaviour was causing problems, but the point is that one party lept to an assumption, whereas the other decided to observe the behaviour of the system as a whole before jumping to conclusions. The point is, that we need to be precisely sure of why we’re doing each test, otherwise our tests will be inefficient, and may not actually help solve the problem.

Applying the fix

Then, of course, once you’ve discovered the faulty component, you need to discover why exactly it’s at fault. It may be tempting to apply a band aid, but over time these build up cruft in the system, as well as making it more fragile, as the band-aid may interact unexpectedly with something else, perhaps because it changes the interface of that component. For example, if we were to “fix” the internationalisation problem above by stripping out all non-ASCII characters, then it’s likely that there’d still be complaints from customers in France, for example.

“Rebooting the computer”

One technique that seems to be commonly used in debugging practice is that of rebooting the computer at fault, or re-installing a faulty component, which, more often than not, seems to have the desired effect. However, it’s likely that you’ve only temporarily removed a symptom, but not corrected the underlying cause. An example of this is when a machine experiences a kernel crash, or complete and utter hang. Yes, rebooting the machine helps fix the problem temporarily, but it’s usually best to investigate the underlying problem, and attempt to rectify this.

The reason that rebooting, or re-installing a component can seem to alleviate a problem, is because it re-sets that component to a (at least partially) known state. In some cases, this is simply a retrograde step, because you may be unwittingly destroying information which may prove to be vital in the diagnosis. For example, some operating systems may display a kernel stack backtrace on the console of the machine, which may seem like random gibberish to the uninitiated. However, this can be vitally important when to a systems developer.

Another example might be to reboot a machine which has been subject to unwanted intrusion, and is causing a denial of service against either your, or someone else’s network. In this case, the state of the running processes on the machine, and network connections can be of great help. For example, there may be a process on the machine which is permitting remote control of the machine by a malicious third party. A common method of controlling these bots is to connect to an IRC server, and join a channel which distributes commands. If you just turn the machine off, then you won’t be able to discover where the commands are coming from (this is probably going to be quite difficult anyway, because an attacker with an ounce wouldn’t normally be connected directly from say, their home ADSL connection).

Of course, that’s not to say that we shouldn’t leave the machine in an unrecoverable state. In the case of a break in, it’s often best to disconnect the machine as soon as you can, and you may not be able to move the machine onto a quarantine area for examination, we need to compromise by taking a snapshot of the state of the machine (filesystem snapshots and / or virtual machine snapshots are useful here). Or in the case of a kernel crash, you might take a photograph of the console screen, or just configure a logged serial terminal for the next time it occurs.

In real life

Now, by and large, you won’t need to think about the steps that you undergo when you start to debug a problem; once you’ve been working on something for a while, you internalise the structure, and you no longer have to think about it. But of course, we sometimes get stuck, and find a problem that’s seemingly impossible to solve. In these cases, stepping back from the system, and working through it methodically is the answer.

It’s also often the case that multiple teams of people are involved with fixing a particular problem, which can mean that it’ll take far longer to arrive at a diagnosis, especially when they don’t keep a detailed record of what has been tested, and the evidence that’s been gathered already. It can be easy to descend into irrationality and start blaming other teams for problems, when some methodical diagnosis would help solve the problem. One answer to this is to state that all investigation must be recorded in a form that’s accessiable to all concerned (for example, a trouble ticket system).

For example, some of the lower levels of support teams aren’t always entirely forthcoming with any diagnostic information they’ve gathered (if any) when escalating an issue to higher level teams. One answer to this problem is to standardize on what level each team is expected, what tests they should perform at each step, and how these should be recorded. Of course, this needs to be enforced, perhaps by having one member of each team who is dedicated to doing triage, and ensuring that all the required information is present. If not, the request should be returned to the original department (of course, this draconian method isn’t always appropriate).