Make your code base healthier: the anti-cheater pattern

I gave a few years ago some courses to college students. They had to write some small C++ programs and send them to me before the end of the course, so I could correct them and give some grades.

They were massively cheating :)

I created a anti-cheat tool to try to find the cheaters, mainly for fun and curiosity. To make it efficient, I tried to understand how people where cheating. They were cutting and pasting pieces of code from various people, and where arranging them so they would look original. They were changing the comments of course, and moving functions around so I wouldn't recognize the class similarities when I was glancing through the code. But when they were cheating, 80% of the code would look similar to the programs they borrowed. When they were composing a new program out of several sources, the atomic bloc was roughly the function: it was most of the time coming for one source. In other words, there were a few students zero (like patient zero in epidemics), that where the providing a function help-yourself copy-n-paste catalog for 30 fellows.

The Levenshtein distance worked great there in finding the cheaters: given two strings, it computes the number of permutations needed to get from one string to the other. A ratio can therefore be calculated between the two strings. Roughly, when its value is equal or up to 0.7, we can consider that the two are quite similar. This can be applied to function code as well.

It was quit funny to use this tool at the end of the course, as I could point the cheaters immediatly. I hurd that some college use such programs now to detect if students are doing plagias in their homeworks.

In software, developers acts the same: in the very same code base, as long as there is more than one developer involved, you will always find the same functions duplicated again and again. Sometimes, the duplication is not done on purpose: it's a natural use case involved by the APIs. In other words, a refactoring is needed to remove duplicate code. This is done most of the time by agile developers that smell the need: why this function is not made generic and moved into the base class ?

So it's a good practice to hunt for duplicates, to make the code smaller, thus more robust. But it's hard to see all duplicates, as it takes a lot of code reviewing time.

That's where the anti-cheater pattern is useful

The pattern can be applied in two steps:
1. Parsing the code and applying a bit of filtering. 2. Calculating the distance, and reporting similarities.

Parsing the code

The first step is to read the code, using the compiler module. This is the cleanest way to extract the functions because the module parses the code and renders an Abstract Syntax Tree (AST), that is browsable without having to import, thus compile the code. Regular expression could work, but would be painful to create. In the AST, each node represents a piece of the program, with a specialized class. For example, a function is a node of the Function class and a list of children that represent the content of the function. compiler also provides a visitor pattern that allows us to set some hook everytime a function, a module, a class or anything else, gets traversed by the parser.

Below is the visitor used to parse the code for our duty:
registered_code = {}

class CleanNode(object):

    def __init__(self, node):

        code = [str(el) for el in node.getChildren()

                if el is not None and not isinstance(el, basestring)]

        # too small

        self.small = len(code) < 5

        self.code = ' '.join(code) =

        self.filename = node.filename

        self.key = node.key

        if hasattr(node, 'klass'):

            self.klass = node.klass

class CodeSeeker(object):

    def __init__(self, filename):

        """compiles the AST"""

        self.filename = os.path.realpath(filename)

        self.node = compiler.parseFile(self.filename)

        res = compiler.walk(self.node, self)

    def _key(self, node):

        """calculates an unique key for a node"""

        if hasattr(node, 'klass'):

            return '%s %s.%s:%s' % (node.filename, node.klass,

                          , node.lineno)


            return '%s %s:%s' % (node.filename,, node.lineno)

    def _clean(self, node):

        return CleanNode(node)

    def register(self, node):

        """register the node"""

        node.filename = self.filename

        node.key = self._key(node)

        node = self._clean(node)

        if not node.small:

            registered_code[node.key] = node


    # compiler walker APIs


    def visitFunction(self, t):


    def visitClass(self, t):

        for subnode in t.getChildren():

            if not subnode.__class__ in  (compiler.ast.Stmt, compiler.ast.Function):


            for f in subnode.getChildren():

                if f is None or isinstance(f, str):


                f.klass =


def register_module(filename):

    """registers a module"""


def register_folder(folder):

    """walk a folder and register python modules"""

    for root, dirs, files in os.walk(folder):

        if os.path.split(root)[-1] == 'tests':


        for file in files:

            if file.endswith('.py') and file != '':

                register_module(os.path.join(root, file))

Each function, inside and outside classes, are registered in registered_code with a few metadata. There's a few filters as you can see. Some are Plone specific (like the omission of tests folders, and files), and some removes very small functions (when there's less than 5 nodes in the function, including its name, parameters, etc). This is the playground for our Levenshtein algorithm.

Calculating the distance

Now we can compare each function to each other, and get a ratio. When the value is up to 0.7 we can consider that the code is pretty similar. I have used David Neca's package: for this, because it's fast and real simple to use:
from Levenshtein import ratio

def levenshtein(entry1, entry2):

    """returns the ratio"""

    return ratio(entry1, entry2)

Using it over our dictionnary will look like this:
def search_similarities():

    similar = []

    items = registered_code.items()

    done = []

    for key, value in items:


        code = str(value.code)

        for key2, value2 in items:

            if key2 == key or key2 in done:


            code2 = str(value2.code)

            ratio = levenshtein(code, code2)

            if ratio > 0.7:

                similar.append((ratio, value.key, value2.key))



    return similar

Of course we could do some caching, and use an iterator to optimize the code (the sorting is not really needed)

Demo in Plone and Zope

Let's run this pattern over Plone and Zope. I have tried it on Plone 3 lib and Zope 3 lib (within Zope 2.10).

For example, addOpenIdPlugin in plone.openid.plugins.oid:
def addOpenIdPlugin(self, id, title='', REQUEST=None):

    """Add a OpenID plugin to a Pluggable Authentication Service.


    p=OpenIdPlugin(id, title)

    self._setObject(p.getId(), p)

    if REQUEST is not None:


                "?manage_tabs_message=OpenID+plugin+added." %


was trapped to be similar to manage_addSessionPlugin in plone.session.plugins.session:
def manage_addSessionPlugin(dispatcher, id, title=None, path='/', REQUEST=None):

    """Add a session plugin."""

    sp=SessionPlugin(id, title=title, path=path)

    dispatcher._setObject(id, sp)

    if REQUEST is not None:


                               'manage_tabs_message=Session+plugin+created.' %


This tells us that a common API could be written that way (if it doesn't alreay exists):
def addPlugin(container, klass, id, REQUEST=None, **kw):

    """adds a plugin"""

    plugin = klass(id, title=title, **kw)

    container._setObject(plugin.getId(), plugin)

    if REQUEST is not None:


                                   '=Plugin+created') % container.absolute_url())

It would avoid having to write such boiler-plate code.

Another example, in Zope 2.10's folder. The class SimpleViewClass in looks very similar to the one found in Mmmm it's exactly the same in fact ! ;)

Last example. In, in PrincipalFolder class:
def search(self, query, start=None, batch_size=None):

    """Search through this principal provider."""

    search = query.get('search')

    if search is None:


    search = search.lower()

    n = 1

    for i, value in enumerate(self.values()):

        if (search in value.title.lower() or

            search in value.description.lower() or

            search in value.login.lower()):

            if not ((start is not None and i < start)

                    or (batch_size is not None and n > batch_size)):

                n += 1

                yield self.prefix + value.__name__

This is very similar to GroupFolder's one in
def search(self, query, start=None, batch_size=None):

    """ Search for groups"""

    search = query.get('search')

    if search is not None:

        n = 0

        search = search.lower()

        for i, (id, groupinfo) in enumerate(self.items()):

            if (search in groupinfo.title.lower() or

                (groupinfo.description and

                 search in groupinfo.description.lower())):

                if not ((start is not None and i < start)


                        (batch_size is not None and n >= batch_size)):

                    n += 1

                    yield self.prefix + id

This should be a common code as well, in the base class.


My example is not very clean though:
- the distance is calculated on the string representation of the tree of each function, and this is probably not optimal; - the ratio treshold was fixed by trying out the pattern on a few source code; - the whole thing is quite slow (I'm not Lundh).

So it's more likely to be a base for a better implementation, or maybe a CheeseCake addon ?

But this works for me at this stage, and let me find duplicate code. I'm thinking of hooking it in a buildbot, to analyze what developers commit and, when a similarity is found, send a mail with a few suggestions.

Comments !