Luke Lee

Software Engineer

Web + Desktop + Science

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A Software Developer's hidden truth

It's Friday afternoon, and your new release has been out for a few days. Your week began with a feeling of pride and relief, but your pride has slowly diminished as the week marched forward. It took a lot of effort and dedication to put out such a bug-free release. In fact, on the release date you were confident the next few weeks would be quiet as users didn't have anything else to need or want.

Of course, it was too good to be true and not too long after the release your first bug report came in. The first bug report was just something innocuous, a minor misspelling in a new dialog box. Then, a few more small bug tickets trickle in, which you quickly fixed and pushed to the repository.

Then it happened, every developer's worst nightmare, a bug reported in your most prized portion of the system. You frantically look through the code even though you know it by memory. How is it possible that branch of code was even executed in this scenario?! The code must be lying to you.

Fast-forward a few days into the bug hunt, and you still have no clue how this happened. You cannot even reproduce the scenario in your testing environment. If only you had more debugging information about the failure...

The truth will set you free

You'll recognize this scenario if you've been writing software for any non-trivial amount of time. It's upsetting that despite your best efforts you've shipped broken software, again. Don't worry, it happens.

This is the part of the story where I reveal the magic bullet to solve this for you once and for all. Unfortunately, I can't, and I don't think such a thing exists.

The hidden truth is all software has bugs. However, that doesn't mean we should give up and not strive for perfection. It just means we would be better served by slightly altering our perception of this reality. We should be writing software almost as if we are planning for defects. We should be writing software defensively, i.e calmly setting traps for the inevitable and unsuspecting bugs.

Defensive programming

The best term to describe this style is Defensive Programming. Wikipedia description doesn't quite capture what I have in mind, but it's a good starting point:

A form of defensive design intended to ensure the continuing function of a
piece of software in spite of unforeseeable usage of said software. The
idea can be viewed as reducing or eliminating the prospect of Murphy's Law
having effect. Defensive programming techniques are used especially when a
piece of software could be misused mischievously or inadvertently to
catastrophic effect.

What I'm really talking about is a combination of the following guidelines:


These guidelines are crucial to ensuring that we can protect our code and sanity from inevitable bugs. Remember, we're operating from the standpoint that we aren't going to write bug-free code.

We need to keep the guidelines in mind to help us find bugs quickly. A lot of times finding bugs is the hard part. So, let's optimize for finding, not the impossible task of preventing them entirely.

Python tools

Let's take a deeper look at some of the tools available to help in following the guidelines. We'll use Python as our language for demonstration purposes, but most languages have very similar tools.

Assume we have the following function which takes values from a user and will normalize the specified range of data into something between 0 and 1, which can be used by a new widget later down the road.

def normalize_ranges(colname):
    Normalize given data range to values in [0 - 1]

    Return dictionary new 'min' and 'max' keys in range [0 - 1]

    # 1-D numpy array of data we loaded application with
    original_range = get_base_range(colname)
    colspan = original_range['datamax'] - original_range['datamin']

    # User filtered data from GUI
    live_data = get_column_data(colname)
    live_min = numpy.min(live_data)
    live_max = numpy.max(live_data)

    ratio = {}
    ratio['min'] = (live_min - original_range['datamin']) / colspan
    ratio['max'] = (live_max - original_range['datamin']) / colspan

    return ratio

Now, assume we have the following 'columns' that are returned by the get_column_data() function:

age = numpy.array([10.0, 20.0, 30.0, 40.0, 50.0])
height = numpy.array([60.0, 66.0, 72.0, 63.0, 66.0])

Let's verify it does indeed turn our given range into something between [0 - 1]:

    >>> normalize_ranges('age')
    {'max': 1.0, 'min': 0.0}

OK, that's a pretty short test, but it seems to work. We passed in a range of 'real' numbers and normalized it to something in the space of [0 - 1].

Remember the guidelines mentioned above, and let's find the bugs that exist in this function. What assumptions are we making implicitly in the code?

I can see a few assumptions:

  1. original_range contains only positive numbers
  2. Ratio returned is between 0.0 and 1.0

After careful examination, there are quite a few assumptions in the above code. Unfortunately, these aren't immediately obvious when glancing at the code. If only we could make these assumptions more clear...


Asserts are very common in unit tests. In fact, Python has a large collection of customized asserts for unit tests. However, there is no reason this useful tool should be used simply in the testing world.

Assert statements within normal code are very useful as well. These statements take an expression and raise an AssertionError along with an optional message if the expression is False.

For example, our above function claimed to always return a value between [0 - 1]. Unfortunately, more stressing of our assumptions shows this isn't true:

    >>> age = numpy.array([-10.0, 20.0, 30.0, 40.0, 50.0])
    >>> normalize_ranges('age')
    {'max': 1.0, 'min': -0.5}

As you can imagine, this scenario could easily go unnoticed for a long time and this return value could be propagated all over the code base. This is precisely the type of bug that's impossible to find and leads to the sad story that started this post.

We could try to think of every possible value our users could pass in and handle it properly. In fact, this is the right thing to do, but there's no guarantee that we won't miss something. We've already conceded the fact that programmers are fallible.

Luckily, we can use assert statements to code against our future selves now that we've accepted that we make mistakes.

def normalize_ranges(colname):
    Normalize given data range to values in [0 - 1]

    Return dictionary new 'min' and 'max' keys in range [0 - 1]

    # 1-D numpy array of data we loaded application with
    original_range = get_base_range(colname)
    colspan = original_range['datamax'] - original_range['datamin']

    # User filtered data from GUI
    live_data = get_column_data(colname)
    live_min = numpy.min(live_data)
    live_max = numpy.max(live_data)

    ratio = {}
    ratio['min'] = (live_min - original_range['datamin']) / colspan
    ratio['max'] = (live_max - original_range['datamin']) / colspan

    assert 0.0 <= ratio['min'] <= 1.0, (
            '"%s" min (%f) not in [0-1] given (%f) colspan (%f)' % (
            colname, ratio['min'], original_range['datamin'], colspan))

    assert 0.0 <= ratio['max'] <= 1.0, (
            '"%s" max (%f) not in [0-1] given (%f) colspan (%f)' % (
            colname, ratio['max'], original_range['datamax'], colspan))

    return ratio

We added a few assert statements that will alert us if we don't return values within the expected range. Let's see how these assertions change our small test case:

    >>> age = numpy.array([-10.0, 20.0, 30.0, 40.0, 50.0])
    >>> normalize_ranges('age')
    AssertionError: "age" min (-0.500000) not in [0-1] given (10.000000) colspan(40.000000)

This small change has several benefits:

  1. Serves as a form of executable documentation

    Typically documentation comes in a few different flavors such as in-line or block comments, docstrings, and sphinx. Each of these serves a specific purpose and are almost essential to software development. Unfortunately, they all suffer from the same issue. They can quickly get out of sync with fast changing code and requirements. This leads to documentation that developer cannot trust.

    Asserts act as documentation with a different purpose. They clearly and concisely describe the expected state of the application at run time. In addition, the application will complain if we change our assumptions without modifying the asserts to match the new behavior.

    Assert statements are much more likely to be updated alongside other changes. Therefore asserts are more trustworthy than non-executable documentation. In addition, asserts still provide many of the benefits of comments, docstrings, etc.

    It's worth pointing out there's another form of executable documentation quite common in the Python ecosystem known as doctests. These tests/documentation can be somewhat ugly to look at, but their key feature is they are close to the code, just like asserts.

  2. Places warnings closer to the root problem

    We've all been there, you debug a problem for hours and realize the real bug wasn't even close to where you started (see 5 whys). Maybe the root cause of the bug was logically far away from where you first saw the symptoms.

    For example, you find a byte string deep in your system, but you assumed everything internally was Unicode strings. It could take a long time to find where the conversion was first broken. This is a frustrating situation to be in. It would be nice to have found the bug much sooner or at least had more debug information.

    Asserts aren't going to prevent this situation, but they do offer a chance to improve it. The above asserts will alert us the moment this function doesn't obey its' contract to return a value between [0 - 1]. This could give us a valuable clue later if we find other code that has an invalid range. We'll know this function didn't live up to its end of the contract. This one clue could literally save hours since it can avoid tracing back from the symptom all the way to the cause.

  3. Includes valuable debugging information about the "invalid" parameters

    Notice that our assert statements also included information about the input parameters. This information will be invaluable when a user encounters the bug using data we don't have access to. Also, the debug information will be especially useful when the user has trouble explaining the error scenario. So, these few assert statements could prevent you being the guy who disgracefully marks the bug with an "unreproducible" status.

    The input parameter information also has a few other subtle benefits:

    • Displays an invalid assumption about what type of data users are running
    • Explain oversight in our documentation about what type of data is expected
    • Expose potential new use cases that cannot be executed

Assert downsides

We've established that asserts can provide a ton of benefits, but it's not all fun and games. As usual, there are downsides.

  1. Debug mode

    Typically, for both technical and practical reasons, assert statements aren't meant for production code. Asserts are only enabled when the hidden debug constant is True. However, the default value for this constant is True, which means your code is most likely currently shipping in debug mode.

    This is something to consider if your application is in an environment where small amounts of additional logic is noticeable. The only way to turn off debug mode is to run the Python interpreter with the -O option.

  2. Increased code noise

    It's very easy to overuse asserts and quickly make your code difficult to read. This can make your code very noisy and bury the real functionality in a series of error checks and conditions. The following code is an example of overuse and how it's difficult to see what the code is meant to do.

def normalize_ranges(colname):
    Normalize given data range to values in [0 - 1]

    Return dictionary new 'min' and 'max' keys in range [0 - 1]

    assert isinstance(colname, str)

    original_range = get_base_range(colname)
    assert original_range['datamin'] >= 0
    assert original_range['datamax'] >= 0
    assert original_range['datamin'] <= original_range['datamax']

    colspan = original_range['datamax'] - original_range['datamin']
    assert colspan >= 0, 'Colspan (%f) is negative' % (colspan)

    live_data = get_column_data(colname)
    assert len(live_data), 'Empty live data'

    live_min = numpy.min(live_data)
    live_max = numpy.max(live_data)

    ratio = {}
    ratio['min'] = (live_min - original_range['datamin']) / colspan
    ratio['max'] = (live_max - original_range['datamin']) / colspan

    assert 0.0 <= ratio['min'] <= 1.0
    assert 0.0 <= ratio['max'] <= 1.0

    return ratio

Proper assert usage

Use asserts sparingly and for things you assume are never supposed to occur. Don't go overboard using assertions to check for invalid input.

There are no hard and fast rules for this and each developer might have a different tolerance for assert usage. Try to adopt some standards of your own and include them in your developer style guide.

You do have a style guide right?

Also, remember Python embraces duck-typing so don't ruin this by going overboard and using asserts to verify all your types.

One technique I've found useful is to catch all AssertionError exceptions at the top-level of my application and combine them with another useful technique.


Logging can be used similarly to assert statements. It can provide run time debugging information and potentially improve upon your executable documentation. Although, logging is not exactly like asserts. It has few extra benefits.

  1. Increased control granularity

    Python's logging module is very comprehensive and customizable. You can send messages to several different levels and each level can be turned on and off at will. So, you might consider some situations to be more severe than others and can codify this with log levels.

    Remember, this isn't the case with asserts because they rely on debug mode.

  2. More dynamic control

    Logging allows you to read your log level from pretty much anywhere. Some common places are a config file, environment variable, and database. This flexibility allows you to control how much log information you see without re-running or re-distributing your application.

    In contrast, Python doesn't allow you to dynamically assign to the debug constant for assert statements. So, you can't turn on and off asserts without re-running the application.

    This is worth considering when deciding on your defensive coding strategy. Dynamic logging control is especially important if you an "alternative" distribution mechanism like PyInstaller.

  3. Silently save tracebacks

    It's useful to have traceback and debugging information at the time of a failure and smart logging usage can do this for you almost automatically.

    This concept is demonstrated in a great exception handling post by Doug Hellman:

    def main():
        log = logging.getLogger('example')
            return 0
        except Exception, err:
            log.exception('Error from throws():')
            return 1

    The call to log.exception automatically adds the exception information for us. Then, we could configure the logger to put tracebacks and exceptions into a separate log file for later inspection without all the normal information and warnings.

    This exception file could contain excellent debug information if enabled in production code. This opens up a lot of exciting possibilities for mining this logging data:

    • Discover users trying different permutations of features we've never tested or even considered, which could lead to adding new features to make common use-cases easier.
    • Find common errors due to a misunderstanding that users have about how the application works, which could lead to writing better user documentation.

  4. Higher-level combination with assertions

    You can also use assertions in tandem with your logging. For example, you could run your application in the default debug mode then catch and log your AssertionError to a different file. This could lead to even more data mining possibilities such as finding out an environmental assumption you have about the platform your running in.

    These are just a few uses for logging in the context of Defensive Programming. In fact, you could use logging to create low-fidelity solutions for all sorts of problems, which is another post all in its own. [2]

Logging downsides

Logging suffers from many of the same downsides as asserts. However, logging's additional flexibility comes with additional baggage to consider.

  1. Difficulty managing consistent levels

    The most difficult thing with logging is using the available set of levels consistently throughout a code base. This boils down to the subjective problem of naming, which is one of only two problems in Computer Science. The best solution is to commit guidelines alongside your code in a style guide. Then, there is some project-specific documentation for newcomers to refer to when adding log messages.

  2. Design of multiple loggers

    The logging module is extremely flexible, but that flexibility comes at a cost. Logging configurations can get complicated. Consider a strategy like the following:

    • Debug level messages go to a hidden file called .debug
    • Info, warning, and error level messages go to stderr
    • Critical and exception level messages go into GUI pop up boxes

    This is a good starting point. Also, Python's documentation includes several good logging strategies that are definitely worth a read before deciding on your setup.

Remember, logging could be your saving grace when chasing a difficult bug. So, take the time to learn the logging system and be sure to design your configuration carefully. Even simple applications deserve a good, carefully designed logging strategy.

Unit tests

One of the last ways to protect ourselves from future bugs is to not forget past bugs. Old bugs have a tendency to creep back into long lived code bases This is usually a result of someone not understanding why something is written in a particular way and removing it to 'clean it up.'

This presents another scenario where it's useful to have another form of executable documentation. So, when an unsuspecting developer changes some code or removes something there's running code to alert them of their mistake.

Typically people encounter unit tests in the context of Test Driven Development. This is a great concept, but often in practice it's a bit too optimistic and difficult to follow (read: customer wants code now). However, that discussion is for another blog post.

What I want to discuss is how to use unit tests to protect you from future and past bugs. Think of this as somewhat reverses the TDD testing concept into something a bit more pragmatic with less up-front time costs.

I propose you write tests AFTER you fix a bug. (see discussion)

This serves a few purposes that might not be immediately obvious:

  1. Improves documentation of bug fix

    Surely you included a nice commit message indicating how you fixed the bug, but don't stop there. It's likely that your commit message and comments are lacking something like:

    • How did you test the fix?
    • What exact scenario caused the bug?

    This is where a good unit test comes into play. You already know how to test the bug. (You did test it before committing, right?) So, codify the scenario you tested with and let everyone benefit from your hard work.

    Unit tests are an excellent place to spell it all out and provide documentation for a bug fix. You can not only explain how and why you fixed it, but how you tested it. This information can be very valuable if the bug creeps up again.

    Don't forget, a passing test can be used as a clue to where the bug is not located.

  2. Future-proofing code against duplicate bugs

    It's not uncommon for a particular bug to sneak back into a code base. This can happen because of changing requirements, re-factoring errors, or any number of situations. You can catch regressions by writing a unit test for a specific bug fix and remembering to run your tests. Think of a unit test as giving yourself a get out of jail free card on a bug you might inject again later.

Unit test downsides

The main downside of unit tests is that it's easy to forget to run them. This is just a byproduct of the tests not be located directly with the code. This downside can be mitigated by practicing something like Continuous Integration using something like Travis CI and automating your testing.

Another downside is unit tests are typically only executed in their own testing environment, which is a simulated environment with no real users.


This style of development is tough to categorize, and unfortunately there aren't any solid rules to say when to use what. So, I encourage you to keep the guidelines in mind.

The guidelines will lead to a subtle change in mindset. The mindset change is important, not the tools and mechanisms themselves. Eventually you'll make some mistakes by overusing asserts or logging and start to form your own style. Also, the requirements for every project differ so it's important to learn all the tools and combine them in ways that make sense for your situation.


[1] Executable documentation is a term sometimes used to describe doctests. "Literate testing" is term used to describe this concept.

[2] You could even use logging to build your own analytics tool. Log to a network or Dropbox file each time a feature is used. Then, have a shell script come behind you every time and collect these files. Now you have a ton of usage information in a simple text-based format opening up tons of possibilities for data mining and helping your users.
Keep in mind that most users won't fill out surveys. So, this would be a way to collect information on the features they are trying to use or common workflows. Then, you could make these better in future releases.

Published: 10-24-2013 21:11:00