CSC/ECE 517 Fall 2013/oss E816 cyy

Introduction to Refactoring plagiarism_check.rb and sentence_state.rb

Expertiza is a web application, which allows students to submit assignments and do peer review of each other's work<ref> Expertiza github</ref>. Expertiza also supports team projects and any document type of submission is acceptable<ref> Expertiza wiki</ref>. Expertiza has been deployed for years to help professors and students engaging in the learning process. Expertiza is an open source project, for each year, students in the course of CSC517-Object Oriented Programmning of North Carolina State University will contributes to this project along with teaching assistant and professor.

For this year, we are responsible for refactoring plagiarism_check.rb and sentence_state.rb of the Expertiza project. Expertiza is built using Ruby on Rails with MVC design pattern. plagiarism_check.rb and sentence_state.rb are parts of the automated_metareview functionality inside models. The responsibility of sentence_state.rb is to determine the state of each clause of a sentence, and the responsibility of plagiarism_check.rb is to determine whether the reviews are just copied from other sources.

Project description

Classes

Classes we are going to refactor are plagiarism_check.rb (155 lines). sentence_state.rb (293 lines)

What it Does

The two class files performs functions which are needed in NLP analysis of reviews in the research. plagiarism_check.rb and sentence_state.rb are used to check whether the reviews are copied from other places. To check whether plagiarism happens is important because the reviewers are tends to game with the automated review system to get a high score instead of writing a high quality review. The classes compare the review text with text from Internet to determine whether and copy-paste happens<ref> Automated Assessment of Reviews</ref>.

Our Job

The code has many code smells. First, the methods are long and complex, and the codes are not structured well, which makes it not readable and understandable. Second, extremely long if-else branch and loop exists everywhere. Third, the responsibility of sentence_state.rb is heavy, some functions of sentence_state should be given to others. Finally, there are some duplicate codes.

Our job is to refactor the two classes and make it more O-O style and have a clear structure. To eliminate the code smells, we need removing the duplicate code, reconstruct long if-else branch and loop, generating new methods and classes to encapsulate functionality of other complex methods, clear the responsibility of classes and methods. After refactoring, we also need to test the two classes throughoutly without error.

Design

sentence_state.rb

To see the original code please go to this link: sentence_state.rb

Main Responsibility

The responsibility of SentenceState is to determine the state of each clause of a sentence. The possible states include positive, negative, and suggestive. In order to find the state of the sentence, SentenceState first splits the sentence into sentence clauses, then splits each clause into sentence tokens (words). Then it iterates through the tokens, and determines the new state of the sentence dependent on the previous state and the token state.

Take for example the sentence_state_test Identify State 8:

sentence = “We are not not musicians.”

First token: We => Positive, state = > positive

Second token: are => Positive and prev_state = >positive, state => positive

Third token: not => Negative and prev_state => positive, state => negative

Fourth token: not => Negative and prev_state => negative, state => positive (double negative!)

Fifth token: musicians => positive and prev_state => positive, state => positive

Therefore the sentence state is positive.

Design Smells

The original code had several design smells, mostly deeply-nested if-else statements and duplicated code. Another design smell was that SentenceState had too many responsibilities. It had to first parse the sentence into separate sentence clauses and then separate the sentence clauses into tokens before iterating through the tokens to determine the state of the sentence. These responsibility of parsing the sentence and sentence tokens can be split into another class because this functionality could be useful elsewhere in the future and should be kept decoupled from SentenceState. The worst problem was the a deeply nested if-else statement which determined the next state of the sentence clause based on the previous state and the next sentence token. Instead of the SentenceState class being responsible for all of these relationships, it is better to have subclasses of SentenceState which each know the relationship between there own state and any sentence token type.

Refactor Steps

The first step in refactoring is to get the tests to pass. This required some debugging to find that some constants were defined in two different files, constants.rb and negations.rb, and the NEGATIVE_DESCRIPTORS definition was incomplete in negations.rb file. After updating this, all 18 original tests in sentence_state_test.rb passed.

The first place to refactor was the longest method in SentenceState, the method sentence_state(str_with_pos_tags). There were three for loops, each with deeply nested if-else statements inside of them. To make this method more readable, I extracted three for or if-else statements into their own method to clean up the code. This changed the code from 164 lines of if-else and for statements to:

def sentence_state(str_with_pos_tags)
   state = POSITIVE
   prev_negative_word =""  
   tagged_tokens, tokens = parse_sentence_tokens(str_with_pos_tags)
   for j  in (0..tokens.length-1)
     current_token_type = get_token_type(tokens[j..tokens.length-1)
     state = next_state(state, current_token_type, prev_negative_word, tagged_tokens)
     if(tokens[j].casecmp("NO") == 0 or tokens[j].casecmp("NEVER") == 0 or tokens[j].casecmp("NONE") == 0)
       prev_negative_word = tokens[j]
     end
   end #end of for loop
   if(state == NEGATIVE_DESCRIPTOR or state == NEGATIVE_WORD or state == NEGATIVE_PHRASE)
     state = NEGATED
   end
   return state
end

This was much easier to read and it revealed that the class SentenceState had too many responsibilities. The class now had two methods which parse the sentence: break_at_coordinating_conjunctions and parse_sentence_tokens. However, parsing a sentence into its sentence clauses and individual tokens (words) could potentially be useful elsewhere, so it is better to decouple this responsibility from SentenceState into a new class. So these two methods were refactored into a TaggedSentence class with the methods break_at_coordinating_conjunctions and parse_sentence_tokens. Now when SentenceState is called, it makes a new TaggedSentence and then calls break_at_coordinating_conjunctions which returns the sentence clauses as arrays of sentence tokens. See the new TaggedSentence class here: tagged_sentence.rb

Once this change was done, the next step was to refactor each of the three new methods that were created earlier to clean up the sentence_state method, because these still contain deeply nested if statements. The first method created was parse_sentence_tokens(str_with_pos_tags). Originally this method was as shown below:

def parse_sentence_tokens(str_with_pos_tags)
   tokens = Array.new
   tagged_tokens = Array.new
   i = 0
   interim_noun_verb  = false #0 indicates no interim nouns or verbs
       
   #fetching all the tokens
   for k in (0..st.length-1)
     ps = st[k]
     if(ps.include?("/"))
       ps = ps[0..ps.index("/")-1] 
     end
     #removing punctuations 
     if(ps.include?("."))
       tokens[i] = ps[0..ps.index(".")-1]
     elsif(ps.include?(","))
       tokens[i] = ps.gsub(",", "")
     elsif(ps.include?("!"))
       tokens[i] = ps.gsub("!", "")
     elsif(ps.include?(";"))
       tokens[i] = ps.gsub(";", "")
     else
       tokens[i] = ps
       i+=1
     end     
   return tokens
end

One of the problems of this code is the duplication of ps[0..ps.index(punctuation)-1] and ps.gsub(punctuation, "") because of the use of an if else statement. To remove this duplication, the strategy design pattern can be used to make each of the duplicated functions into lambda blocks, or commands, and iterate over a punctuation array to remove undesired punctuation. Also, after inspecting the code it was seen that tokens[i] is only truly updated when there is not a punctuation in the string because i is not increased unless it gets to the end of the if-else statement. To fix this we can set a valid_token boolean if no punctuation is found, and then save that value into tokens. This refactored code is shown below.

def parse_sentence_tokens(str_with_pos_tags)
   sentence_pieces = str_with_pos_tags.split(' ')
   num_tokens = 0
   tokens = Array.new
   tag = '/'
   punctuation = %w(. , ! ;)
   sentence_pieces.each do |sp|
     #remove tag from sentence word
     if sp.include?(tag)
       sp = sp[0..sp.index(tag)-1]
     end
     valid_token = true
     punctuation.each do |p|
       if sp.include?(p)
         valid_token = false
         break
       end
     end
     if valid_token
       tokens[num_tokens] = sp
       num_tokens+=1
     end
   end
   #end of the for loop
   tokens
 end

This does not shorten the code but it makes it more readable and extendable. If anyone wants to check for an additional punctuation, they do not have to update the if-else statement which could easily make a bug, instead they only have to update the punctuation array with the new punctuation. Also, the variables are defined so that the reader can understand their functionality. Future refactoring could include moving the line sp = sp[0..sp.index(tag)-1] into a lambda called remove_tag_from_sp[sp, tag] so that the code is even more readable.

The next method to refactor was get_token_type(current_token) in the SentenceState class as shown below.

     if(is_negative_word(tokens[j]) == NEGATED)  
       returned_type = NEGATIVE_WORD
     #checking for a negative descriptor (indirect indicators of negation)
     elsif(is_negative_descriptor(tokens[j]) == NEGATED)
       returned_type = NEGATIVE_DESCRIPTOR
     #2-gram phrases of negative phrases
     elsif(j+1 < count && !tokens[j].nil? && !tokens[j+1].nil? && 
       is_negative_phrase(tokens[j]+" "+tokens[j+1]) == NEGATED)
       returned_type = NEGATIVE_PHRASE
       j = j+1      
     #if suggestion word is found
     elsif(is_suggestive(tokens[j]) == SUGGESTIVE)
       returned_type = SUGGESTIVE
     #2-gram phrases suggestion phrases
     elsif(j+1 < count && !tokens[j].nil? && !tokens[j+1].nil? &&
        is_suggestive_phrase(tokens[j]+" "+tokens[j+1]) == SUGGESTIVE)
       returned_type = SUGGESTIVE
       j = j+1
     #else set to positive
     else
       returned_type = POSITIVE
     end

Problems with this code included a nested if else statement, bad variable names, and duplicated code in the methods it was calling.

Finally, there were some other simple refactorings which I did across the code. This included changing for loops into iterations over objects, and changing variable names to make them more readable for other programmers, and finally removing "return" from the end of methods, because this is implied in ruby code.

• • Future note. The TaggedSentence class could be refactored by allowing a sentence to be either broken into sentence clauses or into sentence clause arrays of parsed sentence tokens.

plagiarism_check.rb

To see the original code please go to this link.

Main Responsibility

The main responsibility of Plagiarism_Check is to determine whether the reviews are just copied from other sources.

Basically, there are four kinds of plagiarism need to be check :

1. whether the review is copied from the submissions of the assignment

2. whether the review is copied from the review questions

3. whether the review is copied from other reviews

4. whether the review is copied from the Internet or other sources, this may be detected through google search

For example, in the test file: expertiza/test/unit/automated_metareview/plagiarism_check_test.rb,

The 1st test shows:

test "check for plagiarism true match" do
   review_text = ["The sweet potatoes in the vegetable bin are green with mold. These sweet potatoes in the vegetable bin are fresh."]
   subm_text = ["The sweet potatoes in the vegetable bin are green with mold. These sweet potatoes in the vegetable bin are fresh."]
  
   instance = PlagiarismChecker.new
   assert_equal(true, instance.check_for_plagiarism(review_text, subm_text))
end

The check_for_plagiarism method compares the review text with submission text. In this case, the review text does not quote the words as well as sentences properly and the reviewer just copies what the author says, which cause a plagiarism.

Design Ideas

From above point of view, the refactoring needs to be done with 4 fundamental methods and each method only does one thing correctly. So as the initial file Plagiarism_check.rb indicates, the compare_reviews_with_questions_responses method has roughly 2 functions : compare reviews with review questions as well as compare reviews with others’ responses, which makes us confused. As the refactoring goes, we need to split the two functions up, and make sure such bad smells disappear.

The first thing to do is based on the above statement, we need to define 4 methods with different functions.

They are: compare_reviews_with_submissions,

compare_reviews_with_questions,

compare_reviews_with_responses, and

compare_reviews_with_google_search, each method has its specific functions.

As showed above, we have to split the method compare_reviews_with_questions _responses up to 2 methods:

def compare_reviews_with_questions(auto_metareview, map_id)
…
end

def compare_reviews_with_responses(auto_metareview, map_id)
…
end

Refactor Steps

Next we need to extract the same part from the long method and make the part a individual method which can be called in class. For example in the method compare_reviews_with_questions and compare_reviews_with_responses they have the common parts: to check whether the reviews are copied fully from the responses/questions,

if(count_copies > 0) #resetting review_array only when plagiarism was found
      auto_metareview.review_array = rev_array
   end
   
   if(count_copies > 0 and count_copies == scores.length)
     return ALL_RESPONSES_PLAGIARISED #plagiarism, with all other metrics 0
   elsif(count_copies > 0)
     return SOME_RESPONSES_PLAGIARISED #plagiarism, while evaluating other metrics
end

To avoid such things to happen, we extract this part and let it be a method to check the state of plagiarism :

def check_plagiarism_state(auto_metareview, count_copies, rev_array, scores)
  if count_copies > 0 #resetting review_array only when plagiarism was found
    auto_metareview.review_array = rev_array
    if count_copies == scores.length
      return ALL_RESPONSES_PLAGIARISED #plagiarism, with all other metrics 0
    else
      return SOME_RESPONSES_PLAGIARISED #plagiarism, while evaluating other metrics
    end
  end
end

Next thing to do is extract the long loop or if-else sentence to a individual method in order to make the initial method too long or confused for others.

Take the 1st method compare_reviews_with_submissions as example, we noticed that the this part:

if(array[rev_len] == " ") #skipping empty
         rev_len+=1
         next
       end
       
       #generating the sentence segment you'd like to compare
rev_phrase = array[rev_len]

can be extracted and made a new method skip_empty_array, since these lines focus on the function of generating the array without backspaces to make comparisons. Once we extract the method, the initial code of the compare_reviews_with_submissions changed:

expertiza/app/models/automated_metareview/plagiarism_check.rb

...
review_text.each do |review_arr| #iterating through the review's sentences
   
   review = review_arr.to_s
   subm_text.each do |subm_arr|
     
     #iterating though the submission's sentences
     submission = subm_arr.to_s
     rev_len = 0
     #review's tokens, taking 'n' at a time
     array = review.split(" ")
     while(rev_len < array.length) do
       rev_len, rev_phrase = skip_empty_array(array, rev_len)
     ...

def skip_empty_array(array, rev_len)
 if (array[rev_len] == " ") #skipping empty
   rev_len+=1
end
 
#generating the sentence segment you'd like to compare
 rev_phrase = array[rev_len]
 return rev_len, rev_phrase
end

Please see code after refactoring in detail on this page.

All the tests have been passed without failures since refactoring.

Test Our Code

Link to VCL

The purpose of running the VCL server is to let you make sure that expertiza is still working properly using our refactored code. The first VCL link is seeded with the expertiza-scrubbed.sql file which includes questionnaires and courses and assignments so that it is easy to verify that reviews work. You only need to make users and then have them do reviews on one another. The second link is only using the test.sql file but you can still verify that the functionality of expertiza works. If neither of these links work, please do not do your review in a hurry, shoot us an email, we will fix it as soon as possible. (yhuang25@ncsu.edu, ysun6@ncsu.edu, grimes.caroline@gmail.com). Thank you so much!

1. http://152.46.20.30:3000/ Username: admin, password:password

2. http://vclv99-129.hpc.ncsu.edu:3000 Username: admin, password: admin

Git Forked Repository URL

https://github.com/shanfangshuiyuan/expertiza <ref> Expertiza fork</ref>

Test Our Code

1. Set up the project following the steps above

2. Command line: db:test:prepare

3. Run plagiarism_check_test.rb and sentence_state_test.rb, they are under /test/unit/automated_metareview. After refactoring, all tests passed without error.

4. Review the refactored files: sentence_state.rb and plagiarism_check.rb are under /app/models/automated_metareview. Other changed files are shown below.

Files Changed

1. text_preprocessing.rb

2. plagiarism_check.rb

3. sentence_state.rb

4. tagged_sentence.rb

5. constants.rb

6. negations.rb

7. plagiarism_check_test.rb

Future Work

Through refactoring we've made the code easier to understand with design patterns involved, which meets the requirements of this project. But from our perspective, there should be more work to do in order to improve the whole performance of the code, which includes:

1. There are some bugs in the initial method compare_reviews_with_questions_responses and google_search_response, which can not be implemented so far. We hope that people who are responsible for this project can fix it and make the method do the expected function well.

2. Based on 1, we can do more tests regarding plagiarism, which makes the code development better.

3. Through running tests, we've found there are some errors within the method of text_preprocssing.rb file, which may cause a conflict with the function of plagiarism-check. Bug-fixing is needed.

References

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