I'm trying to do some algorithm comparison for plagiarism. I've found many TEXT comparison for plagiarism.
But in an algorithm it's very different. Let's say that some algorithm uses an huge number of variables, functions and user defined structures. If some guy copy the source code from someone, he'll at least, change the variables and functions names. With an simple text comparison algorithm this difference in functions and variables letters will count as an "difference" making the algorithm gives an "false" for plagiarism.
What I want to do is "generalize" (I don't know if that's the right word) all the variables, functions and user-defined structures names in an C++ source code. So the varibles will be named like "a", "b", the same for functions "... fa(...)", "... fb(...)".
I have the c++ source algorithms in strings variables in PHP to be compared.
I know that many other things should be analysed for an accurate source code comparison, but that will be enough to me.
It's an interesting question. Depending on how complex the algorithm, however, it might be that variable names are what gives the plagiarism away. How many ways can you really code up a tree traversal for example?
I think there was a paper a few years ago on identifying coders through their style - looking at all the little things like whitespace, where {}s are placed, etc. Who knows but maybe that is the way to go, look for a negative match to the student's previous style rather than positive match to the known sources. Saying that, students aren't likely to have developed a very personal coding style at an early stage of learning.
One thought - what language are the examples written in? Can it be compiled? If you compile C and then do a binary comparison on the executables, then will identical programs with different local variable names have the exact same binary? (Global vars and functions wouldn't, though).
I've used MOSS in the past: http://theory.stanford.edu/~aiken/moss/ to detect plagiarized code. Since it works on a semantic level, it will detect the situations you presented above. The tool is language-aware, so comments are not considered in the analysis, and it goes a long way in detecting code that has been modified through simple search-and-replace of variable and/or function names.
Note: I used the tool a few years ago when I taught computer science in grad school, and it worked wonderfully in detecting code that had been yanked from the internet. Here is a well-documented account of similar application: http://fie2012.org/sites/fie2012.org/history/fie99/papers/1110.pdf
If you google "measure software similarity", you should find a few more useful hits: http://www.ics.heacademy.ac.uk/resources/assessment/plagiarism/detectiontools_sourcecode.html
I have unstructured geneally unclean data in a database field. There are common structures which are consistent in the data
namely:
field:
name:value
fieldset:
nombre <FieldSet>
field,
.
.
.
field(n)
table
nombre <table>
head(1)... head(n)
val(1)... val(n)
.
.
.
I was wondering if there was a tool (preferably in Java) that could extract learn/understand these data structures, parse the file and convert to a Map or object which I could run validation checks on?
I am aware of Antlr but understand this is more geared towards tree construction, an not independent bits of data (am I wrong about this?)
Does anyone have any suggestions for the problem as a whole?
I recommend Talend. It is very versatile, open source data integration tool. It is based on java. You can use build in tools/components to extract data from unstructured data sources. You can also write complex custom java code to do what you want.
I used Talend in couple of scientific proof of concept projects of mine. It worked for me. Good part is, it is free!
We ended up using antlr for this, it required us to make multiple lexers where one lexer would manipulated the input for the next lexer.
Another project is pads - wrote in C
You should use "bnflite"
https://github.com/r35382/bnflite
Using this template library you need to develop BNF like gramma for your text by means of classes and overloaded operators directly in C++ code.
The benefit is that such gramma is easily adjustable to your source
Parsec is designed to parse textual information, but it occurs to me that Parsec could also be suitable to do binary file format parsing for complex formats that involve conditional segments, out-of-order segments, etc.
Is there an ability to do this or a similar, alternative package that does this? If not, what is the best way in Haskell to parse binary file formats?
The key tools for parsing binary files are:
Data.Binary
cereal
attoparsec
Binary is the most general solution, Cereal can be great for limited data sizes, and attoparsec is perfectly fine for e.g. packet parsing. All of these are aimed at very high performance, unlike Parsec. There are many examples on hackage as well.
You might be interested in AttoParsec, which was designed for this purpose, I think.
I've used Data Binary successfully.
It works fine, though you might want to use Parsec 3, Attoparsec, or Iteratees. Parsec's reliance on String as its intermediate representation may bloat your memory footprint quite a bit, whereas the others can be configured to use ByteStrings.
Iteratees are particularly attractive because it is easier to ensure they won't hold onto the beginning of your input and can be fed chunks of data incrementally a they come available. This prevents you from having to read the entire input into memory in advance and lets you avoid other nasty workarounds like lazy IO.
The best approach depends on the format of the binary file.
Many binary formats are designed to make parsing easy (unlike text formats that are primarily to be read by humans). So any union data type will be preceded by a discriminator that tells you what type to expect, all fields are either fixed length or preceded by a length field, and so on. For this kind of data I would recommend Data.Binary; typically you create a matching Haskell data type for each type in the file, and then make each of those types an instance of Binary. Define the "get" method for reading; it returns a "Get" monad action which is basically a very simple parser. You will also need to define a "put" method.
On the other hand if your binary data doesn't fit into this kind of world then you will need attoparsec. I've never used that, so I can't comment further, but this blog post is very positive.
Is it possible for a person working with statistic to replace his specialized programs by F#? I'm thinking about SAS/SPSS mainly?
Any native support for it in F#?
I am not talking about the trivial things as standard deviation and the likes, but for example item-response modeling.
UPDATE : Dont't let the item-response modeling put you of! I don't even know it, just an example of things I know they do with SPSS to clarify it's about more advanced features.
Short : is there a way to use F# as your main statistical tool and replace SPSS all together?
Sadly, nothing comporable to combination of
R + PostgreSQL + Python/Java/Groovy/Scala/... + VisAD
Of course, there is nice http://www.codeplex.com/vslab instead of gnuplot
and some c# statistics code packaged in http://ta-lib.org/ http://www.alglib.net/
You can use R within F# with the type providers for R
see
http://blogs.msdn.com/b/dsyme/archive/2013/01/30/twelve-type-providers-in-pictures.aspx
and see
http://techblog.bluemountaincapital.com/2012/08/01/announcing-the-f-r-type-provider/
Here at BlueMountain we like to perform statistical analysis of data. The stats package R is great for doing that. We also like to use the data retrieval and processing capabilities of F#. F#’s interactive environment lends itself pretty well to data exploration, and we can also easily access our existing .NET-based libraries. Once we are done, we can build and release production-supportable applications.
Here is something maybe-useful:
http://fsmathtools.codeplex.com/
or
http://mathnetnumerics.codeplex.com/
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I'm building an application that receives source code as input and analyzes several aspects of the code. It can accept code from many common languages, e.g. C/C++, C#, Java, Python, PHP, Pascal, SQL, and more (however many languages are unsupported, e.g. Ada, Cobol, Fortran). Once the language is known, my application knows what to do (I have different handlers for different languages).
Currently I'm asking the user to input the programming language the code is written in, and this is error-prone: although users know the programming languages, a small percentage of them (on rare occasions) click the wrong option just due to recklessness, and that breaks the system (i.e. my analysis fails).
It seems to me like there should be a way to figure out (in most cases) what the language is, from the input text itself. Several notes:
I'm receiving pure text and not file names, so I can't use the extension as a hint.
The user is not required to input complete source codes, and can also input code snippets (i.e. the include/import part may not be included).
it's clear to me that any algorithm I choose will not be 100% proof, certainly for very short input codes (e.g. that could be accepted by both Python and Ruby), in which cases I will still need the user's assistance, however I would like to minimize user involvement in the process to minimize mistakes.
Examples:
If the text contains "x->y()", I may know for sure it's C++ (?)
If the text contains "public static void main", I may know for sure it's Java (?)
If the text contains "for x := y to z do begin", I may know for sure it's Pascal (?)
My question:
Are you familiar with any standard library/method for figuring out automatically what the language of an input source code is?
What are the unique code "tokens" with which I could certainly differentiate one language from another?
I'm writing my code in Python but I believe the question to be language agnostic.
Thanks
Vim has a autodetect filetype feature. If you download vim sourcecode you will find a /vim/runtime/filetype.vim file.
For each language it checks the extension of the file and also, for some of them (most common), it has a function that can get the filetype from the source code. You can check that out. The code is pretty easy to understand and there are some very useful comments there.
build a generic tokenizer and then use a Bayesian filter on them. Use the existing "user checks a box" system to train it.
Here is a simple way to do it. Just run the parser on every language. Whatever language gets the farthest without encountering any errors (or has the fewest errors) wins.
This technique has the following advantages:
You already have most of the code necessary to do this.
The analysis can be done in parallel on multi-core machines.
Most languages can be eliminated very quickly.
This technique is very robust. Languages that might appear very similar when using a fuzzy analysis (baysian for example), would likely have many errors when the actual parser is run.
If a program is parsed correctly in two different languages, then there was never any hope of distinguishing them in the first place.
I think the problem is impossible. The best you can do is to come up with some probability that a program is in a particular language, and even then I would guess producing a solid probability is very hard. Problems that come to mind at once:
use of features like the C pre-processor can effectively mask the underlyuing language altogether
looking for keywords is not sufficient as the keywords can be used in other languages as identifiers
looking for actual language constructs requires you to parse the code, but to do that you need to know the language
what do you do about malformed code?
Those seem enough problems to solve to be going on with.
One program I know which even can distinguish several different languages within the same file is ohcount. You might get some ideas there, although I don't really know how they do it.
In general you can look for distinctive patterns:
Operators might be an indicator, such as := for Pascal/Modula/Oberon, => or the whole of LINQ in C#
Keywords would be another one as probably no two languages have the same set of keywords
Casing rules for identifiers, assuming the piece of code was writting conforming to best practices. Probably a very weak rule
Standard library functions or types. Especially for languages that usually rely heavily on them, such as PHP you might just use a long list of standard library functions.
You may create a set of rules, each of which indicates a possible set of languages if it matches. Intersecting the resulting lists will hopefully get you only one language.
The problem with this approach however, is that you need to do tokenizing and compare tokens (otherwise you can't really know what operators are or whether something you found was inside a comment or string). Tokenizing rules are different for each language as well, though; just splitting everything at whitespace and punctuation will probably not yield a very useful sequence of tokens. You can try several different tokenizing rules (each of which would indicate a certain set of languages as well) and have your rules match to a specified tokenization. For example, trying to find a single-quoted string (for trying out Pascal) in a VB snippet with one comment will probably fail, but another tokenizer might have more luck.
But since you want to perform analysis anyway you probably have parsers for the languages you support, so you can just try running the snippet through each parser and take that as indicator which language it would be (as suggested by OregonGhost as well).
Some thoughts:
$x->y() would be valid in PHP, so ensure that there's no $ symbol if you think C++ (though I think you can store function pointers in a C struct, so this could also be C).
public static void main is Java if it is cased properly - write Main and it's C#. This gets complicated if you take case-insensitive languages like many scripting languages or Pascal into account. The [] attribute syntax in C# on the other hand seems to be rather unique.
You can also try to use the keywords of a language - for example, Option Strict or End Sub are typical for VB and the like, while yield is likely C# and initialization/implementation are Object Pascal / Delphi.
If your application is analyzing the source code anyway, you code try to throw your analysis code at it for every language and if it fails really bad, it was the wrong language :)
My approach would be:
Create a list of strings or regexes (with and without case sensitivity), where each element has assigned a list of languages that the element is an indicator for:
class => C++, C#, Java
interface => C#, Java
implements => Java
[attribute] => C#
procedure => Pascal, Modula
create table / insert / ... => SQL
etc. Then parse the file line-by-line, match each element of the list, and count the hits.
The language with the most hits wins ;)
How about word frequency analysis (with a twist)? Parse the source code and categorise it much like a spam filter does. This way when a code snippet is entered into your app which cannot be 100% identified you can have it show the closest matches which the user can pick from - this can then be fed into your database.
Here's an idea for you. For each of your N languages, find some files in the language, something like 10-20 per language would be enough, each one not too short. Concatenate all files in one language together. Call this lang1.txt. GZip it to lang1.txt.gz. You will have a set of N langX.txt and langX.txt.gz files.
Now, take the file in question and append to each of he langX.txt files, producing langXapp.txt, and corresponding gzipped langXapp.txt.gz. For each X, find the difference between the size of langXapp.gz and langX.gz. The smallest difference will correspond to the language of your file.
Disclaimer: this will work reasonably well only for longer files. Also, it's not very efficient. But on the plus side you don't need to know anything about the language, it's completely automatic. And it can detect natural languages and tell between French or Chinese as well. Just in case you need it :) But the main reason, I just think it's interesting thing to try :)
The most bulletproof but also most work intensive way is to write a parser for each language and just run them in sequence to see which one would accept the code. This won't work well if code has syntax errors though and you most probably would have to deal with code like that, people do make mistakes. One of the fast ways to implement this is to get common compilers for every language you support and just run them and check how many errors they produce.
Heuristics works up to a certain point and the more languages you will support the less help you would get from them. But for first few versions it's a good start, mostly because it's fast to implement and works good enough in most cases. You could check for specific keywords, function/class names in API that is used often, some language constructions etc. Best way is to check how many of these specific stuff a file have for each possible language, this will help with some syntax errors, user defined functions with names like this() in languages that doesn't have such keywords, stuff written in comments and string literals.
Anyhow you most likely would fail sometimes so some mechanism for user to override language choice is still necessary.
I think you never should rely on one single feature, since the absence in a fragment (e.g. somebody systematically using WHILE instead of for) might confuse you.
Also try to stay away from global identifiers like "IMPORT" or "MODULE" or "UNIT" or INITIALIZATION/FINALIZATION, since they might not always exist, be optional in complete sources, and totally absent in fragments.
Dialects and similar languages (e.g. Modula2 and Pascal) are dangerous too.
I would create simple lexers for a bunch of languages that keep track of key tokens, and then simply calculate a key tokens to "other" identifiers ratio. Give each token a weight, since some might be a key indicator to disambiguate between dialects or versions.
Note that this is also a convenient way to allow users to plugin "known" keywords to increase the detection ratio, by e.g. providing identifiers of runtime library routines or types.
Very interesting question, I don't know if it is possible to be able to distinguish languages by code snippets, but here are some ideas:
One simple way is to watch out for single-quotes: In some languages, it is used as character wrapper, whereas in the others it can contain a whole string
A unary asterisk or a unary ampersand operator is a certain indication that it's either of C/C++/C#.
Pascal is the only language (of the ones given) to use two characters for assignments :=. Pascal has many unique keywords, too (begin, sub, end, ...)
The class initialization with a function could be a nice hint for Java.
Functions that do not belong to a class eliminates java (there is no max(), for example)
Naming of basic types (bool vs boolean)
Which reminds me: C++ can look very differently across projects (#define boolean int) So you can never guarantee, that you found the correct language.
If you run the source code through a hashing algorithm and it looks the same, you're most likely analyzing Perl
Indentation is a good hint for Python
You could use functions provided by the languages themselves - like token_get_all() for PHP - or third-party tools - like pychecker for python - to check the syntax
Summing it up: This project would make an interesting research paper (IMHO) and if you want it to work well, be prepared to put a lot of effort into it.
There is no way of making this foolproof, but I would personally start with operators, since they are in most cases "set in stone" (I can't say this holds true to every language since I know only a limited set). This would narrow it down quite considerably, but not nearly enough. For instance "->" is used in many languages (at least C, C++ and Perl).
I would go for something like this:
Create a list of features for each language, these could be operators, commenting style (since most use some sort of easily detectable character or character combination).
For instance:
Some languages have lines that start with the character "#", these include C, C++ and Perl. Do others than the first two use #include and #define in their vocabulary? If you detect this character at the beginning of line, the language is probably one of those. If the character is in the middle of the line, the language is most likely Perl.
Also, if you find the pattern := this would narrow it down to some likely languages.
Etc.
I would have a two-dimensional table with languages and patterns found and after analysis I would simply count which language had most "hits". If I wanted it to be really clever I would give each feature a weight which would signify how likely or unlikely it is that this feature is included in a snippet of this language. For instance if you can find a snippet that starts with /* and ends with */ it is more than likely that this is either C or C++.
The problem with keywords is someone might use it as a normal variable or even inside comments. They can be used as a decider (e.g. the word "class" is much more likely in C++ than C if everything else is equal), but you can't rely on them.
After the analysis I would offer the most likely language as the choice for the user with the rest ordered which would also be selectable. So the user would accept your guess by simply clicking a button, or he can switch it easily.
In answer to 2: if there's a "#!" and the name of an interpreter at the very beginning, then you definitely know which language it is. (Can't believe this wasn't mentioned by anyone else.)