├── .gitignore ├── conda_environment_linux.yml ├── linsen ├── TestLinsen │ └── TestSetDictionary │ │ ├── dim.txt │ │ ├── _0.cfs │ │ ├── segments_2 │ │ ├── AcronymsFile │ │ ├── segments.gen │ │ └── AbbreviationsFile ├── ConfigurationFiles │ ├── Structure │ │ ├── COFFEEMAKER │ │ │ └── Dictionary │ │ │ │ ├── dim.txt │ │ │ │ ├── _0.cfs │ │ │ │ ├── _1.cfs │ │ │ │ ├── _2.cfs │ │ │ │ ├── segments_4 │ │ │ │ ├── AcronymsFile │ │ │ │ ├── segments.gen │ │ │ │ └── AbbreviationsFile │ │ └── JFREECHART │ │ │ └── Dictionary │ │ │ ├── dim.txt │ │ │ ├── _0.cfs │ │ │ ├── _1.cfs │ │ │ ├── _2.cfs │ │ │ ├── AcronymsFile │ │ │ ├── segments.gen │ │ │ ├── segments_4 │ │ │ └── AbbreviationsFile │ ├── java_keywords.txt │ ├── utils │ │ └── exportClassPath.sh │ ├── StopWord.txt │ └── Acronyms.txt └── Linsen │ ├── hmm │ ├── HMM.class │ ├── HMMWriter.class │ ├── ISmoothing.class │ ├── Vocabulary.class │ ├── OpdfWriterHMM.class │ ├── LaplaceSmoothing.class │ └── exceptions │ │ ├── InconsistencyHMM.class │ │ └── InconsistencyLaplaceSmoothing.class │ ├── jars │ ├── asdlab.jar │ ├── jgraph.jar │ ├── neobio.jar │ ├── AhoCorasick.jar │ ├── asdlab-mdr.jar │ ├── jahmm-0.6.1.jar │ ├── modifyNeobio.jar │ ├── jgrapht-jdk1.6.jar │ ├── lingpipe-4.0.1.jar │ ├── commons-io-2.0.1.jar │ ├── lucene-core-3.0.2.jar │ ├── modifyNeoBioOrig.jar │ ├── google-collect-1.0-rc1.jar │ ├── jakarta-bcel-20050813.jar │ ├── lucene-snowball-2.4.0.jar │ ├── lucene-wordnet-3.0.2.jar │ ├── commons-io-2.0.1-javadoc.jar │ ├── commons-io-2.0.1-sources.jar │ ├── commons-collections-3.2.1.jar │ └── lucene-spellchecker-2.4.1.jar │ ├── NormalizeMain.class │ ├── indexing │ ├── index │ │ ├── info │ │ ├── IIndex.class │ │ ├── IndexFactory.class │ │ ├── IndexLucene.class │ │ ├── analyzer │ │ │ ├── AllAnalyzer.class │ │ │ ├── WordAnalyzer.class │ │ │ ├── DictionaryAnalyzer.class │ │ │ ├── IdentifierAnalyzer.class │ │ │ ├── AllAnalyzerNormalizer.class │ │ │ ├── IdentifierStemAnalyzer.class │ │ │ └── IdentifierAnalyzerNormalizer.class │ │ ├── tokenizer │ │ │ ├── AllTokenizer.class │ │ │ ├── IndexTokenizer.class │ │ │ ├── SnowballFilter.class │ │ │ ├── WordTokenizer.class │ │ │ └── IdentifierTokenizer.class │ │ └── exceptions │ │ │ ├── IndexException.class │ │ │ ├── WordNotFound.class │ │ │ ├── AnalyzeCommentException.class │ │ │ ├── InconsistencyFileToken.class │ │ │ ├── ProjectNotFoundException.class │ │ │ └── NoSourceFileFoundException.class │ ├── DictionaryHMM.class │ ├── IProbabilisticModel.class │ ├── building │ │ ├── IBuildingHMM.class │ │ ├── BuildingHMMModule.class │ │ ├── BuildingHMMFactory.class │ │ └── exceptions │ │ │ └── BuildingHMMException.class │ ├── learning │ │ ├── ILearningHMM.class │ │ ├── LearningHMMModule.class │ │ ├── LearningHMMFactory.class │ │ └── exceptions │ │ │ └── LearningHMMModuleException.class │ ├── exceptions │ │ ├── DictionaryException.class │ │ └── WordGeneratesTokenWithProbabilityException.class │ ├── IDictionaryWithProbabilisticModel.class │ └── WordGeneratesTokenWithProbability.class │ ├── splitting │ ├── CompositeSplit.class │ ├── IAdjustOutput.class │ ├── NoAdjustOutput.class │ ├── NormalizeSplit.class │ ├── CamelCaseFullScan.class │ ├── AbstractSplitModule.class │ ├── AlternateCaseSplit.class │ ├── AbstractCompositeSplit.class │ ├── AbstractMixedCaseSplit.class │ ├── AbstractSameCaseSplit.class │ ├── DictionaryAdjustOutput.class │ ├── MarkersSplittingModule.class │ ├── SplitTokenModuleFactory.class │ ├── exceptions │ │ └── SplittingError.class │ ├── PatternMatchingSplittingModule.class │ ├── SameCaseSplitWithProbabilisticModel.class │ ├── CamelCaseLowerToUpperSplittingModule.class │ ├── CamelCaseUpperToLowerSplittingModule.class │ └── MixedCaseSplitWithProbabilisticModel.class │ ├── multiple_pattern_matching │ ├── result │ │ ├── MatchResult.class │ │ └── MatchResultWithOperation.class │ ├── path_matches │ │ ├── TokenEdge.class │ │ ├── TokenGraph.class │ │ ├── TokenGraph$1.class │ │ ├── TokenGraph$tolerance.class │ │ ├── strategy_weight │ │ │ ├── Score.class │ │ │ ├── InverseLength.class │ │ │ ├── ICalculatorWeight.class │ │ │ ├── PrefixScoreSingleWord.class │ │ │ ├── BoundaryScoreSingleWord.class │ │ │ ├── FactualErrorAndInverseLength.class │ │ │ ├── FactualScoreAndInverseLengthSingleWord.class │ │ │ └── FactualScoreAndLogInverseLengthSingleWord.class │ │ ├── TokenGraph$weightMatch.class │ │ └── strategy_error │ │ │ ├── LogError.class │ │ │ ├── NoError.class │ │ │ └── ICalculatorError.class │ ├── exceptions │ │ ├── ExceedLimitCost.class │ │ ├── SubStringError.class │ │ ├── TokenEdgeException.class │ │ ├── PatternMatchingError.class │ │ └── InconsistencyTokenGraph.class │ ├── MultiplePatternMatchingFactory.class │ ├── alignment │ │ ├── IAlignmentWithOperations.class │ │ ├── SmithWatermanCalculator.class │ │ └── SmithWatermanCalculator$SmithWaterman.class │ ├── exact_pattern_matching │ │ ├── AhoCorasickCalculator.class │ │ └── IMultipleExactPatternMatching.class │ └── approximate_pattern_matching │ │ ├── BaezaYatesCalculator.class │ │ ├── ICalculatesSubstring.class │ │ ├── CalculatesSubstringModule.class │ │ └── IMultipleApproximatePatternMatching.class │ ├── ConfigurationDictionaryforSimilarSplit.properties │ └── ConfigurationTestSet.properties ├── data ├── dataset │ ├── coherence_data.pkz │ ├── coherence_meta.pkz │ ├── coherence_data.pkz_01.npy.z │ └── coherence.rst └── distributions_per_rate_tfidf │ ├── NC_Coffeemaker_(1.0)_20.txt │ ├── DK_Jfreechart_(0.6.0)_24.txt │ ├── CO_Coffeemaker_(1.0)_27.txt │ ├── DK_Jfreechart_(0.7.1)_36.txt │ ├── NC_Jfreechart_(0.6.0)_55.txt │ ├── NC_Jfreechart_(0.7.1)_68.txt │ ├── CO_Jfreechart_(0.6.0)_406.txt │ ├── CO_Jfreechart_(0.7.1)_520.txt │ ├── DK_Jhotdraw_(7.4.1)_693.txt │ └── CO_Jhotdraw_(7.4.1)_760.txt ├── README.md ├── preamble_directives.py ├── requirements.txt ├── LICENSE ├── conda_environment_osx.yml ├── load_distribution_data.py ├── Create Labeled Dataset.ipynb ├── Benchmark Data.ipynb ├── evaluations.py └── coherence.py /.gitignore: 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-------------------------------------------------------------------------------- /data/distributions_per_rate_tfidf/NC_Coffeemaker_(1.0)_20.txt: -------------------------------------------------------------------------------- 1 | 5.647359770392865741e-01 2 | 1.639657871763961494e-01 3 | 3.086932733812327023e-01 4 | 6.091197260172849237e-01 5 | 2.073242878100461650e-01 6 | 2.643792307765249761e-01 7 | 2.570178676700096698e-01 8 | 5.221587043975048914e-01 9 | 1.771790400781452224e-01 10 | 4.093343037757758984e-01 11 | 6.275923134892538169e-01 12 | 6.091197260172847017e-01 13 | 6.091197260172849237e-01 14 | 4.806999110979645051e-01 15 | 5.712308245690576047e-01 16 | 5.647359770392865741e-01 17 | 5.834073652697191470e-01 18 | 5.647359770392865741e-01 19 | 5.143762754789020786e-01 20 | 8.180288236246369138e-01 21 | -------------------------------------------------------------------------------- /data/distributions_per_rate_tfidf/DK_Jfreechart_(0.6.0)_24.txt: -------------------------------------------------------------------------------- 1 | 0.000000000000000000e+00 2 | 3.098305996133788320e-01 3 | 6.499206241254443794e-01 4 | 0.000000000000000000e+00 5 | 3.506927824584908926e-01 6 | 4.861270692975826480e-01 7 | 6.557095471428091571e-01 8 | 3.852848603869074573e-01 9 | 6.462139132379847162e-01 10 | 5.498566493709017289e-01 11 | 4.861270692975826480e-01 12 | 2.242981280967078495e-01 13 | 4.302519221172870778e-01 14 | 4.483734364843494635e-01 15 | 2.868328522697924932e-01 16 | 0.000000000000000000e+00 17 | 0.000000000000000000e+00 18 | 3.696390407973592085e-01 19 | 3.906341991675876812e-01 20 | 3.506494649340016201e-01 21 | 3.000262326843236016e-01 22 | 5.512102847771828129e-01 23 | 4.568169658574767200e-01 24 | 4.985031588570064676e-01 25 | -------------------------------------------------------------------------------- /data/distributions_per_rate_tfidf/CO_Coffeemaker_(1.0)_27.txt: -------------------------------------------------------------------------------- 1 | 4.020709505262495265e-01 2 | 7.671377914256212183e-01 3 | 6.849548296591644325e-01 4 | 4.290018543828315600e-01 5 | 4.114092226339227665e-01 6 | 2.915278803997383705e-01 7 | 7.135074669567048611e-01 8 | 7.107099513019521364e-01 9 | 2.635614167147561915e-01 10 | 2.943141570832158438e-01 11 | 7.092498206316042442e-01 12 | 6.541606832592468113e-01 13 | 7.025035914249555535e-01 14 | 7.025035914249555535e-01 15 | 6.366683854211623306e-01 16 | 7.025035914249555535e-01 17 | 1.618742347178380170e-01 18 | 6.275923134892538169e-01 19 | 4.267153243010550767e-01 20 | 4.267153243010550767e-01 21 | 3.301611266763173069e-01 22 | 5.738585594316729654e-01 23 | 6.275923134892537059e-01 24 | 3.714589132082121714e-01 25 | 4.267153243010550767e-01 26 | 3.750161365423088089e-01 27 | 3.745381883005248769e-01 28 | -------------------------------------------------------------------------------- /preamble_directives.py: -------------------------------------------------------------------------------- 1 | """Some imports and path settings to make notebook code 2 | running smoothly. 3 | """ 4 | # Author: Valerio Maggio 5 | # Copyright (c) 2015 Valerio Maggio 6 | # License: BSD 3 clause 7 | 8 | import sys, os 9 | # Extending PYTHONPATH to allow relative import! 10 | sys.path.append(os.path.join(os.path.abspath(os.path.curdir), '..')) 11 | sys.warnoptions.append('ignore::DeprecationWarning') # Avoid DeprecationWarnings 12 | sys.warnoptions.append('ignore::FutureWarning') # Avoid DeprecationWarnings 13 | 14 | # Import Django Settings 15 | from django.conf import settings 16 | # Import Comments_Classification (Django) Project Settings 17 | from code_comments_coherence import settings as coherence_settings 18 | 19 | settings.configure(**coherence_settings.__dict__) 20 | 21 | import numpy as np -------------------------------------------------------------------------------- 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6.425970600245125564e-01 30 | 2.200806002627895275e-01 31 | 3.005963015317481557e-01 32 | 5.725831302029875669e-01 33 | 2.318558667954098151e-01 34 | 6.087104307155559724e-01 35 | 0.000000000000000000e+00 36 | 0.000000000000000000e+00 37 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | appnope==0.1.0 2 | backports.shutil-get-terminal-size==1.0.0 3 | bokeh==0.10.0 4 | certifi==14.5.14 5 | cycler==0.9.0 6 | decorator==4.0.4 7 | Django==1.11.29 8 | Flask==1.0 9 | gnureadline==6.3.3 10 | ipykernel==4.1.0 11 | ipyparallel==4.0.2 12 | ipython==4.0.0 13 | ipython-genutils==0.1.0 14 | ipywidgets==4.0.3 15 | itsdangerous==0.24 16 | Jinja2==2.10.1 17 | jsonschema==2.4.0 18 | jupyter==1.0.0 19 | jupyter-client==4.1.1 20 | jupyter-console==4.0.3 21 | jupyter-core==4.0.6 22 | MarkupSafe==0.23 23 | matplotlib==1.5.0 24 | mistune==0.8.1 25 | nbconvert==4.0.0 26 | nbformat==4.0.1 27 | nltk==3.4.5 28 | nose==1.3.7 29 | notebook==6.1.5 30 | numpy==1.10.1 31 | pandas==0.17.0 32 | path.py 33 | pexpect==3.3 34 | pickleshare==0.5 35 | psycopg2==2.6.1 36 | ptyprocess==0.5 37 | Pygments==2.0.2 38 | pyparsing==2.0.3 39 | python-dateutil==2.4.2 40 | PyTrie==0.2 41 | pytz==2015.7 42 | PyYAML==5.1 43 | pyzmq==14.7.0 44 | qtconsole==4.0.1 45 | requests==2.20.0 46 | scikit-learn==0.16.1 47 | scipy==0.16.0 48 | simplegeneric==0.8.1 49 | six==1.10.0 50 | terminado==0.5 51 | tornado==4.2.1 52 | traitlets==4.0.0 53 | Werkzeug==0.15.3 54 | -------------------------------------------------------------------------------- /linsen/Linsen/ConfigurationDictionaryforSimilarSplit.properties: -------------------------------------------------------------------------------- 1 | output_dictionary=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/notebooks_data/ConfigurationFiles/Structure/JFREECHART/Dictionary/ 2 | path_token_file_dictionary=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/notebooks_data/ConfigurationFiles/Informatics.txt;/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/notebooks_data/ConfigurationFiles/English.txt 3 | root_project_dictionary=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/media/jfreechart/0.6.0/jfreechart-060zip/extracted/ 4 | acronyms_file=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/notebooks_data/ConfigurationFiles/Acronyms.txt 5 | abbreviations_file=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/notebooks_data/ConfigurationFiles/Abbreviations.txt 6 | extension_indexing_file=java 7 | path_stopwords_dictionary=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/notebooks_data/ConfigurationFiles/StopWord.txt -------------------------------------------------------------------------------- /linsen/ConfigurationFiles/utils/exportClassPath.sh: -------------------------------------------------------------------------------- 1 | export CLASSPATH=$CLASSPATH:/Users/valerio/Research/Code/String-Matching/Linsen/jars/lucene-core-3.0.2.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/lucene-snowball-2.4.0.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/lucene-spellchecker-2.4.1.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/lucene-wordnet-3.0.2.jar::/Users/valerio/Research/Code/String-Matching/Linsen/jars/AhoCorasick.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/commons-collections-3.2.1.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/commons-io-2.0.1.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/google-collect-1.0-rc1.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/jahmm-0.6.1.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/jakarta-bcel-20050813.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/jgraph.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/jgrapht-jdk1.6.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/lingpipe-4.0.1.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/modifyNeoBio.jar:/Users/valerio/Research/Code/String-Matching/Linsen/jars/neobio.jar: 2 | -------------------------------------------------------------------------------- /linsen/Linsen/ConfigurationTestSet.properties: -------------------------------------------------------------------------------- 1 | root_project=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/media/jfreechart/0.6.0/jfreechart-060zip/extracted/target_sourcefiles/ 2 | output_test_set=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/notebooks_data/TestLinsen/TestSetDictionary/ 3 | path_token_file_test_set= 4 | path_stopwords_test_set= 5 | acronyms_file=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/notebooks_data/ConfigurationFiles/Acronyms.txt 6 | abbreviations_file=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/notebooks_data/ConfigurationFiles/Abbreviations.txt 7 | root_report=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/media/jfreechart/0.6.0/jfreechart-060zip/extracted/LINSEN_report_files/ 8 | oracle_path= 9 | extension_indexing_file=java 10 | target_file_path=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/media/jfreechart/0.6.0/jfreechart-060zip/extracted/target_sourcefiles/getnotify.java 11 | target_identifiers_list=/Users/valerio/Research/Code/Source-Code-Comments-Classification/comments_classification/media/jfreechart/0.6.0/jfreechart-060zip/extracted/target_identifiers.txt -------------------------------------------------------------------------------- /data/dataset/coherence.rst: -------------------------------------------------------------------------------- 1 | The Coherence dataset contains information about the 2 | coherence between the head comment and the implementation 3 | of a source code methods. 4 | 5 | ================= ============== 6 | Classes 2 7 | Samples per class (Pos) 1713 8 | (Neg) 1168 9 | Samples total (Tot) 2881 10 | Dimensionality 5642 11 | Unique Terms 2821 12 | Features real, positive 13 | ================= ============== 14 | 15 | Note: 16 | ----- 17 | Since methods are gathered from different software projects, to ease data 18 | analysis (e.g. slicing, splitting or extracting data of a single software project), 19 | data are stored according to classes and projects, respectively. 20 | In particular, data are primarily grouped by class (all positive instances, first), and then 21 | further organized per project. 22 | 23 | So far, these are the distribution of examples per single project: 24 | ====================== =========================== 25 | Project Positive | Negative | Total 26 | CoffeeMaker (1.0) 27 | 20 | 47 27 | JFreeChart (0.6.0) 406 | 55 | 461 28 | JFreeChart (0.7.1) 520 | 68 | 588 29 | JHotDraw (7.4.1) 760 | 1025 | 1785 30 | ====================== =========================== 31 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright (c) 2015, Valerio Maggio 2 | All rights reserved. 3 | 4 | Redistribution and use in source and binary forms, with or without 5 | modification, are permitted provided that the following conditions are met: 6 | 7 | * Redistributions of source code must retain the above copyright notice, this 8 | list of conditions and the following disclaimer. 9 | 10 | * Redistributions in binary form must reproduce the above copyright notice, 11 | this list of conditions and the following disclaimer in the documentation 12 | and/or other materials provided with the distribution. 13 | 14 | * Neither the name of Comments-and-Code-Coherence-Analysis nor the names of its 15 | contributors may be used to endorse or promote products derived from 16 | this software without specific prior written permission. 17 | 18 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 19 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 20 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 21 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 22 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 23 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 24 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 25 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 26 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 27 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 28 | 29 | -------------------------------------------------------------------------------- /data/distributions_per_rate_tfidf/NC_Jfreechart_(0.6.0)_55.txt: -------------------------------------------------------------------------------- 1 | 3.532163187613085453e-01 2 | 6.496412821559673922e-01 3 | 0.000000000000000000e+00 4 | 2.942466594887417353e-01 5 | 7.311692171272803353e-01 6 | 3.034260460913379975e-01 7 | 2.988897186567160968e-01 8 | 2.490017305835438566e-01 9 | 1.685039685526924513e-01 10 | 2.668434755990364837e-01 11 | 0.000000000000000000e+00 12 | 3.532163187613085453e-01 13 | 2.549498351633366355e-01 14 | 2.092368669043484786e-01 15 | 6.063942170938547704e-01 16 | 2.690116834791969613e-01 17 | 4.183149012361092045e-01 18 | 0.000000000000000000e+00 19 | 4.540745986554853841e-01 20 | 5.477231215713923573e-01 21 | 1.736826921752631647e-01 22 | 2.220175480449269323e-01 23 | 2.183422408990737806e-01 24 | 3.532163187613085453e-01 25 | 2.899326399413493238e-01 26 | 2.090253583387550185e-01 27 | 4.329247161726880178e-01 28 | 4.329247161726880178e-01 29 | 2.090253583387550185e-01 30 | 6.401919594517220258e-01 31 | 5.820274977960424589e-01 32 | 0.000000000000000000e+00 33 | 4.370997312465895179e-01 34 | 5.143678700380820423e-01 35 | 3.232636225732827828e-01 36 | 1.875987054173097468e-01 37 | 4.965965828913281865e-01 38 | 3.988644952005614419e-01 39 | 2.743318447018270878e-01 40 | 4.226433198499125465e-01 41 | 0.000000000000000000e+00 42 | 5.162540629160614625e-01 43 | 3.551295152193115756e-01 44 | 7.312937112808381990e-02 45 | 2.205768668752627060e-01 46 | 3.391394482216815720e-01 47 | 1.661943499005159863e-01 48 | 1.629626100729904825e-01 49 | 4.265146035121316626e-01 50 | 3.470596326594702630e-01 51 | 1.510476691342684807e-01 52 | 6.328579906500728658e-01 53 | 7.267460339234002586e-02 54 | 2.054645693121484040e-01 55 | 5.071348587794051177e-01 56 | -------------------------------------------------------------------------------- /data/distributions_per_rate_tfidf/NC_Jfreechart_(0.7.1)_68.txt: -------------------------------------------------------------------------------- 1 | 3.937253305547182003e-01 2 | 5.018755939687975509e-01 3 | 6.328243698868077916e-01 4 | 2.161530927586563233e-01 5 | 4.985485794196012899e-01 6 | 5.588015508041783708e-01 7 | 2.460387453196655749e-01 8 | 1.911148584647642534e-01 9 | 4.117896796160847028e-01 10 | 4.420831280319120205e-01 11 | 8.120071235636429074e-01 12 | 0.000000000000000000e+00 13 | 3.392571461723042359e-01 14 | 1.247321469092982915e-01 15 | 2.042711216033437083e-01 16 | 1.952714946703080767e-01 17 | 4.962431452647604324e-01 18 | 0.000000000000000000e+00 19 | 2.411839945217746362e-01 20 | 4.248000616485415049e-01 21 | 2.120339945305761142e-01 22 | 3.291713348639663250e-01 23 | 3.482996753040483728e-01 24 | 5.027064578273188200e-02 25 | 7.333010708582408943e-02 26 | 7.219351118452187188e-02 27 | 5.936355102151580354e-01 28 | 5.978168386875640228e-02 29 | 6.390560701916111475e-01 30 | 7.398474334054182255e-02 31 | 6.643669905611266913e-01 32 | 4.942216162944538804e-01 33 | 1.522322018705306867e-01 34 | 4.134213287999156661e-01 35 | 3.459498508073158840e-01 36 | 2.884003548430281172e-01 37 | 2.490433786806853234e-01 38 | 1.345194919323771665e-01 39 | 1.540570595459104242e-01 40 | 2.576401093799781661e-01 41 | 0.000000000000000000e+00 42 | 4.088737374976328942e-01 43 | 4.994728131874759947e-01 44 | 5.846001644048926665e-02 45 | 5.864223773792968941e-02 46 | 3.149280691972914936e-01 47 | 2.623800367980387849e-01 48 | 6.805717400788627858e-01 49 | 3.974282027617793700e-01 50 | 5.118604939427165990e-01 51 | 3.425311441301827076e-01 52 | 6.849635647590135434e-01 53 | 4.031534694411986197e-01 54 | 5.055963146804952624e-01 55 | 7.809087732874391286e-01 56 | 7.749020756413776967e-02 57 | 0.000000000000000000e+00 58 | 5.637769393063203349e-01 59 | 0.000000000000000000e+00 60 | 5.574174737034165439e-01 61 | 4.966983819276339918e-01 62 | 2.005888162345958459e-01 63 | 4.036044134366515435e-01 64 | 6.214808525242766502e-01 65 | 4.048059153577812652e-01 66 | 2.804419493503746419e-01 67 | 7.923881786466433486e-01 68 | 0.000000000000000000e+00 69 | -------------------------------------------------------------------------------- /conda_environment_osx.yml: -------------------------------------------------------------------------------- 1 | name: coherence_analysis 2 | dependencies: 3 | - bokeh=0.10.0=py35_0 4 | - certifi=14.05.14=py34_0 5 | - cycler=0.9.0=py34_0 6 | - decorator=4.0.4=py34_0 7 | - flask=0.10.1=py35_1 8 | - freetype=2.5.5=0 9 | - ipykernel=4.1.0=py34_0 10 | - ipyparallel=4.0.2=py35_0 11 | - ipython=4.0.0=py34_0 12 | - ipython_genutils=0.1.0=py34_0 13 | - ipywidgets=4.0.3=py34_0 14 | - itsdangerous=0.24=py35_0 15 | - jinja2=2.8=py34_0 16 | - jsonschema=2.4.0=py34_0 17 | - jupyter=1.0.0=py34_2 18 | - jupyter_client=4.1.1=py34_0 19 | - jupyter_console=4.0.3=py34_0 20 | - jupyter_core=4.0.6=py34_0 21 | - libpng=1.6.17=0 22 | - libsodium=1.0.3=0 23 | - libxml2=2.9.2=0 24 | - markupsafe=0.23=py34_0 25 | - matplotlib=1.5.1=np110py34_0 26 | - mistune=0.7.1=py34_0 27 | - nbconvert=4.0.0=py34_0 28 | - nbformat=4.0.1=py34_0 29 | - nltk=3.0.5=py35_0 30 | - nose=1.3.7=py34_0 31 | - notebook=4.0.5=py34_0 32 | - numpy=1.10.1=py34_0 33 | - openssl=1.0.2d=0 34 | - pandas=0.17.0=np110py35_0 35 | - path.py=8.1.2=py34_0 36 | - pexpect=3.3=py34_0 37 | - pickleshare=0.5=py34_0 38 | - pip=7.1.2=py34_0 39 | - psycopg2=2.6.1=py34_1 40 | - ptyprocess=0.5=py34_0 41 | - pygments=2.0.2=py34_0 42 | - pyparsing=2.0.3=py34_0 43 | - pyqt=4.11.4=py34_0 44 | - python=3.4.4=0 45 | - python-dateutil=2.4.2=py34_0 46 | - python.app=1.2=py34_4 47 | - pytz=2015.7=py34_0 48 | - pyyaml=3.11=py35_1 49 | - pyzmq=14.7.0=py34_1 50 | - qt=4.8.7=1 51 | - qtconsole=4.0.1=py34_0 52 | - readline=6.2=2 53 | - requests=2.8.1=py35_0 54 | - scikit-learn=0.16.1=np110py34_0 55 | - scipy=0.16.0=np110py34_1 56 | - setuptools=18.4=py34_0 57 | - simplegeneric=0.8.1=py34_0 58 | - sip=4.16.9=py34_0 59 | - six=1.10.0=py34_0 60 | - sqlite=3.8.4.1=1 61 | - terminado=0.5=py34_1 62 | - tk=8.5.18=0 63 | - tornado=4.2.1=py34_0 64 | - traitlets=4.0.0=py34_0 65 | - werkzeug=0.10.4=py35_0 66 | - wheel=0.26.0=py34_1 67 | - xz=5.0.5=0 68 | - yaml=0.1.6=0 69 | - zeromq=4.1.3=0 70 | - zlib=1.2.8=0 71 | - pip: 72 | - appnope==0.1.0 73 | - django==1.6 74 | - gnureadline==6.3.3 75 | - ipython-genutils==0.1.0 76 | - jupyter-client==4.1.1 77 | - jupyter-console==4.0.3 78 | - jupyter-core==4.0.6 79 | - pytrie==0.2 80 | 81 | -------------------------------------------------------------------------------- /load_distribution_data.py: -------------------------------------------------------------------------------- 1 | """Processing data of terms distributions stored in textual files 2 | per single project, and coherence evaluation. 3 | """ 4 | # Author: Valerio Maggio 5 | # License: BSD 3 clause 6 | 7 | import numpy as np 8 | import os 9 | from collections import defaultdict 10 | 11 | BASE_DIR = os.path.abspath(os.path.dirname(__file__)) 12 | TARGET_FOLDER_NAME = 'distributions_per_rate_tfidf' 13 | TARGET_FOLDER_PATH = os.path.join(BASE_DIR, 'data', TARGET_FOLDER_NAME) 14 | 15 | # Easy to use Constants access keys to the first layer of `evaluations_info` dictionary 16 | COHERENCE_KEY = 'CO' 17 | NON_COHERENCE_KEY = 'NC' 18 | DONT_KNOW_KEY = 'DK' 19 | 20 | 21 | def get_distributions_data(target_folder=TARGET_FOLDER_PATH): 22 | """ 23 | Process all the (.txt) files contained in `target_folder` and gets 24 | all the distributions data corresponding to analysed projects. 25 | 26 | This function assumes each files contained in the `target_folder` 27 | is named according to the following pattern: 28 | `EVAL_PROJECTNAME_VERSION_COUNT`.txt such as 29 | 30 | * `EVAL` corresponds to a Coherence Evaluation Abbreviation 31 | (i.e., `CO` for *Coherent*, `NC` for *Non Coherent*, `DK` for *Don't Know*) 32 | * `PROJECTNAME: the name of the target project 33 | * `VER`: the release number of the target project 34 | * `COUNT`: the total number of methods' data contained in the file 35 | (i.e., the total number of rows) 36 | 37 | Gathered information are stored (and returned) in a multi-layer dictionary 38 | that is structured as follows: 39 | 40 | EVAL_1 : { '' : dict --> { 'methods_count': int, 41 | 'data': numpy.array # loaded from file 42 | }, 43 | '' : dict --> { 'methods_count': int, 44 | 'data': numpy.array # loaded from file 45 | }, 46 | ... 47 | }, 48 | 49 | EVAL_2 : ... 50 | 51 | Thus to extract the **data** for a specific Coherence Evaluation (e.g., 'CO', 52 | and a specific project (e.g., 'Jfreechart 0.6.0'), the accessor 53 | path to this information is: 54 | 55 | `evaluations_info[COHERENCE_KEY]['Jfreechart-0.6.0']['data']` 56 | 57 | Parameters 58 | ---------- 59 | target_folder : string, optional 60 | Path to the target folder containing .txt files to load 61 | 62 | Returns 63 | ------- 64 | evaluations_info : dictionary 65 | The (multi-layered) dictionary embedding (per-evaluation, per-project) 66 | information. 67 | """ 68 | 69 | evaluations_info = defaultdict(dict) 70 | for root, _, filenames in os.walk(target_folder): 71 | for filename in filenames: 72 | if filename.endswith('.txt'): 73 | fname, ext = os.path.splitext(filename) 74 | evaluation, project_name, version, methods_count = fname.split('_') 75 | project_key = '-'.join([project_name, version[1:-1]]) 76 | project_info = dict() 77 | project_info['methods_count'] = int(methods_count) 78 | project_info['data'] = np.loadtxt(os.path.join(root, filename)) 79 | evaluations_info[evaluation][project_key] = project_info 80 | 81 | return evaluations_info 82 | 83 | if __name__ == '__main__': 84 | # Test Runner code (sample usage) 85 | evaluations_info = get_distributions_data() 86 | print(evaluations_info) 87 | # Get the distributions of jfreechart-0.6.0 88 | print(evaluations_info[COHERENCE_KEY]['Jfreechart-0.6.0']) 89 | 90 | 91 | 92 | 93 | 94 | -------------------------------------------------------------------------------- /linsen/ConfigurationFiles/StopWord.txt: -------------------------------------------------------------------------------- 1 | ability 2 | abl 3 | able 4 | ably 5 | ac 6 | acea 7 | aceae 8 | acean 9 | aceous 10 | ad 11 | ade 12 | aemia 13 | age 14 | agog 15 | agogue 16 | aholic 17 | al 18 | ales 19 | algia 20 | amine 21 | an 22 | ana 23 | anae 24 | ance 25 | ancy 26 | androus 27 | andry 28 | ane 29 | ant 30 | ar 31 | arch 32 | archy 33 | ard 34 | aria 35 | arian 36 | arium 37 | art 38 | ary 39 | ase 40 | ate 41 | athon 42 | ation 43 | ative 44 | ator 45 | atory 46 | biont 47 | biosis 48 | blast 49 | cade 50 | caine 51 | carp 52 | carpic 53 | carpous 54 | cele 55 | cene 56 | centric 57 | cephalic 58 | cephalous 59 | cephaly 60 | chore 61 | chory 62 | chrome 63 | cide 64 | clast 65 | clinal 66 | cline 67 | clinic 68 | coccus 69 | coel 70 | coele 71 | colous 72 | cracy 73 | crat 74 | cratic 75 | cratical 76 | cy 77 | cyte 78 | dale 79 | deco 80 | derm 81 | derma 82 | dermatous 83 | dom 84 | drome 85 | dromous 86 | eae 87 | eaux 88 | ectomy 89 | ed 90 | ee 91 | eer 92 | ein 93 | eme 94 | emia 95 | en 96 | ence 97 | enchyma 98 | ency 99 | ene 100 | ent 101 | eous 102 | er 103 | ergic 104 | ergy 105 | es 106 | escence 107 | escent 108 | ese 109 | esque 110 | ess 111 | est 112 | et 113 | eth 114 | etic 115 | ette 116 | ey 117 | facient 118 | faction 119 | fer 120 | ferous 121 | fic 122 | fication 123 | fid 124 | florous 125 | fold 126 | foliate 127 | foliolate 128 | fuge 129 | ful 130 | fy 131 | gamous 132 | gamy 133 | gate 134 | gengene 135 | genesis 136 | genetic 137 | genic 138 | genous 139 | geny 140 | gnathous 141 | gon 142 | gony 143 | gram 144 | grapher 145 | graphy 146 | gyne 147 | gynous 148 | gyny 149 | hood 150 | hashtabl 151 | ia 152 | ial 153 | ian 154 | iana 155 | iasis 156 | iatric 157 | iatrics 158 | iatry 159 | ibility 160 | ible 161 | ic 162 | icide 163 | ician 164 | ickobsolete 165 | ics 166 | id 167 | idae 168 | ide 169 | ie 170 | ify 171 | ile 172 | in 173 | ina 174 | inae 175 | ine 176 | ineae 177 | ing 178 | ini 179 | ion 180 | ious 181 | isation 182 | ise 183 | ish 184 | ism 185 | ist 186 | istic 187 | istical 188 | istically 189 | it 190 | its 191 | ite 192 | itious 193 | itis 194 | ity 195 | ium 196 | ive 197 | ix 198 | ization 199 | ize 200 | kinesis 201 | kins 202 | latry 203 | late 204 | lepry 205 | les 206 | less 207 | let 208 | LinkedList 209 | like 210 | ling 211 | lite 212 | liti 213 | lith 214 | lithic 215 | logue 216 | logical 217 | logist 218 | logy 219 | ly 220 | lyse 221 | lysis 222 | lyte 223 | lytic 224 | lyze 225 | mancy 226 | mania 227 | meister 228 | ment 229 | mer 230 | mere 231 | merous 232 | meter 233 | metry 234 | mire 235 | mo 236 | morph 237 | morphic 238 | morphism 239 | morphous 240 | most 241 | mycete 242 | mycetes 243 | mycetidae 244 | mycin 245 | mycota 246 | mycotina 247 | nasty 248 | ness 249 | nik 250 | nomy 251 | nomics 252 | ode 253 | odon 254 | odont 255 | odontia 256 | oholic 257 | oic 258 | oid 259 | oidea 260 | oideae 261 | ol 262 | ole 263 | oma 264 | ome 265 | omics 266 | one 267 | on 268 | ont 269 | onym 270 | onymy 271 | onto 272 | opia 273 | opsida 274 | opsis 275 | opsy 276 | or 277 | orama 278 | ory 279 | ose 280 | osis 281 | otic 282 | otomy 283 | ous 284 | outof 285 | para 286 | parous 287 | pathy 288 | ped 289 | pede 290 | penia 291 | petal 292 | phage 293 | phagia 294 | phagous 295 | phagy 296 | phane 297 | phasia 298 | phil 299 | phile 300 | philia 301 | philiac 302 | philic 303 | philous 304 | phobe 305 | phobia 306 | phobic 307 | phone 308 | phony 309 | phore 310 | phoresis 311 | phorous 312 | phrenia 313 | phyll 314 | phyllous 315 | phyceae 316 | phycidae 317 | phyta 318 | phyte 319 | phytina 320 | plasia 321 | plasm 322 | plast 323 | plastic 324 | plasty 325 | plegia 326 | plex 327 | ploid 328 | pod 329 | pode 330 | podous 331 | poieses 332 | poietic 333 | pter 334 | ratio 335 | rrhagia 336 | rrhea 337 | ric 338 | ry 339 | scape 340 | scope 341 | scopy 342 | scribe 343 | sect 344 | sepalous 345 | ship 346 | some 347 | speak 348 | sperm 349 | sphere 350 | sporous 351 | st 352 | stasis 353 | stat 354 | ster 355 | stome 356 | stomy 357 | taxis 358 | taxy 359 | tend 360 | th 361 | therm 362 | thermal 363 | thermy 364 | thon 365 | thymia 366 | tion 367 | tome 368 | tomy 369 | tonia 370 | trichous 371 | trix 372 | tron 373 | trophic 374 | trophy 375 | tropic 376 | tropism 377 | tropous 378 | tropy 379 | tude 380 | ture 381 | ty 382 | ular 383 | ule 384 | ure 385 | urgy 386 | uria 387 | uronic 388 | urous 389 | valent 390 | virile 391 | vorous 392 | wards 393 | ware 394 | ways 395 | wear 396 | wise 397 | worthy 398 | yl 399 | yne 400 | zoic 401 | zoon 402 | zygous 403 | zyme -------------------------------------------------------------------------------- /Create Labeled Dataset.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Create the dataset of labeled Examples" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "The main goal of this notebook is to get all the evaluations about method coherence where\n", 15 | "evaluator got a (*perfect*) agreement and store those data in the Database as \n", 16 | "`coherence_dataset.models.Example` instances.\n", 17 | "\n", 18 | "These instances are just used to ease the operations of getting the data from the database \n", 19 | "to get statistics and/or training *Machine Learning* models.\n", 20 | "In fact, querying directly `Example` instances, allows to avoid getting (**each time**) Judges\n", 21 | "evaluations, getting the intersection of their agreement, and finally injecting the data \n", 22 | "to create the **training set**." 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": {}, 28 | "source": [ 29 | "**Note:** : this notebook assumes the use of **Python 3**" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "## Preamble: Set Django Envinronment and Imports" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": null, 42 | "metadata": { 43 | "collapsed": true 44 | }, 45 | "outputs": [], 46 | "source": [ 47 | "%load preamble_directives.py" 48 | ] 49 | }, 50 | { 51 | "cell_type": "markdown", 52 | "metadata": {}, 53 | "source": [ 54 | "### Get Evaluations\n", 55 | "\n", 56 | "Get methods from `SoftwareProjects` so far analysed by evaluators" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": 11, 62 | "metadata": { 63 | "collapsed": false 64 | }, 65 | "outputs": [ 66 | { 67 | "name": "stdout", 68 | "output_type": "stream", 69 | "text": [ 70 | "[, , , ]\n" 71 | ] 72 | } 73 | ], 74 | "source": [ 75 | "from source_code_analysis.models import SoftwareProject\n", 76 | "projects = list()\n", 77 | "projects.append(SoftwareProject.objects.get(name__iexact='CoffeeMaker', version__exact='1.0'))\n", 78 | "projects.append(SoftwareProject.objects.get(name__iexact='Jfreechart', version__exact='0.6.0'))\n", 79 | "projects.append(SoftwareProject.objects.get(name__iexact='Jfreechart', version__exact='0.7.1'))\n", 80 | "projects.append(SoftwareProject.objects.get(name__iexact='JHotDraw', version__exact='7.4.1'))\n", 81 | "print(projects)" 82 | ] 83 | }, 84 | { 85 | "cell_type": "markdown", 86 | "metadata": {}, 87 | "source": [ 88 | "### Settings the environment" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": null, 94 | "metadata": { 95 | "collapsed": true 96 | }, 97 | "outputs": [], 98 | "source": [ 99 | "judges_combinations = (('leonardo.nole', 'rossella.linsalata'),\n", 100 | " ('leonardo.nole', 'antonio.petrone'),\n", 101 | " ('leonardo.nole', 'antonio.petrone'),\n", 102 | " ('leonardo.nole', 'rossella.linsalata'),)\n", 103 | "\n", 104 | "from coherence_dataset.settings import NOT_COHERENT, COHERENT\n", 105 | "CODES_Labels = (NOT_COHERENT, COHERENT)\n", 106 | "from collections import defaultdict\n", 107 | "stats_results = defaultdict(list)" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": null, 113 | "metadata": { 114 | "collapsed": false 115 | }, 116 | "outputs": [], 117 | "source": [ 118 | "from evaluations import Judge\n", 119 | "from coherence_dataset.models import Example\n", 120 | "\n", 121 | "for pno, project in enumerate(projects): \n", 122 | " \n", 123 | " print('Processing Evaluation for ', str(project), ' Project')\n", 124 | " \n", 125 | " # Get Methods\n", 126 | " code_methods = project.code_methods.all()\n", 127 | " method_ids_map = dict() \n", 128 | " for method in code_methods:\n", 129 | " method_ids_map[method.id] = method\n", 130 | " \n", 131 | " print('Gathered ', len(method_ids_map.keys()), ' Methods')\n", 132 | "\n", 133 | " j1_usrname, j2_usrname = judges_combinations[pno]\n", 134 | " j1 = Judge(j1_usrname, project.name, project.version)\n", 135 | " j2 = Judge(j2_usrname, project.name, project.version)\n", 136 | " \n", 137 | " #getting just NC and CO evaluations \n", 138 | " j1_evals = j1.two_codes_evaluations \n", 139 | " j2_evals = j2.two_codes_evaluations\n", 140 | " \n", 141 | " project_stats = list()\n", 142 | " for i, label in enumerate(CODES_Labels):\n", 143 | " j1_evals_code = j1_evals[i]\n", 144 | " j2_evals_code = j2_evals[i]\n", 145 | " \n", 146 | " method_ids = j1_evals_code.intersection(j2_evals_code)\n", 147 | " \n", 148 | " print('Gathered ', len(method_ids), \n", 149 | " ' for {0} examples'.format('Positive' if i == 0 else 'Negative'))\n", 150 | " \n", 151 | " saved_instances_counter = 0\n", 152 | " for mid in method_ids:\n", 153 | " method = method_ids_map[mid] \n", 154 | " try: \n", 155 | " _ = method.example\n", 156 | " except Example.DoesNotExist:\n", 157 | " example = Example()\n", 158 | " example.method = method\n", 159 | " example.target = label\n", 160 | " example.save()\n", 161 | " saved_instances_counter += 1\n", 162 | " \n", 163 | " print('Saved ', saved_instances_counter, \n", 164 | " ' for {0} examples'.format('Positive' if i == 0 else 'Negative'))\n", 165 | " " 166 | ] 167 | }, 168 | { 169 | "cell_type": "markdown", 170 | "metadata": {}, 171 | "source": [ 172 | "## Test " 173 | ] 174 | }, 175 | { 176 | "cell_type": "markdown", 177 | "metadata": {}, 178 | "source": [ 179 | "Actually verify that instances have been saved into the DB and that querying the dataset will work as expected." 180 | ] 181 | }, 182 | { 183 | "cell_type": "code", 184 | "execution_count": null, 185 | "metadata": { 186 | "collapsed": false 187 | }, 188 | "outputs": [], 189 | "source": [ 190 | "from coherence_dataset.models import Example" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 5, 196 | "metadata": { 197 | "collapsed": false 198 | }, 199 | "outputs": [ 200 | { 201 | "name": "stdout", 202 | "output_type": "stream", 203 | "text": [ 204 | "Total examples in Dataset: 2881\n" 205 | ] 206 | } 207 | ], 208 | "source": [ 209 | "examples = Example.objects.all()\n", 210 | "print(\"Total examples in Dataset: \", examples.count())" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 7, 216 | "metadata": { 217 | "collapsed": true 218 | }, 219 | "outputs": [], 220 | "source": [ 221 | "from coherence_dataset.settings import COHERENT, NOT_COHERENT" 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": 9, 227 | "metadata": { 228 | "collapsed": false 229 | }, 230 | "outputs": [ 231 | { 232 | "name": "stdout", 233 | "output_type": "stream", 234 | "text": [ 235 | "Positive examples: 1713\n" 236 | ] 237 | } 238 | ], 239 | "source": [ 240 | "print(\"Positive examples: \", examples.filter(target=COHERENT).count())" 241 | ] 242 | }, 243 | { 244 | "cell_type": "code", 245 | "execution_count": 10, 246 | "metadata": { 247 | "collapsed": false 248 | }, 249 | "outputs": [ 250 | { 251 | "name": "stdout", 252 | "output_type": "stream", 253 | "text": [ 254 | "Positive examples: 1168\n" 255 | ] 256 | } 257 | ], 258 | "source": [ 259 | "print(\"Positive examples: \", examples.filter(target=NOT_COHERENT).count())" 260 | ] 261 | }, 262 | { 263 | "cell_type": "markdown", 264 | "metadata": {}, 265 | "source": [ 266 | "### Get Statistics per-Project" 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": 15, 272 | "metadata": { 273 | "collapsed": false 274 | }, 275 | "outputs": [ 276 | { 277 | "name": "stdout", 278 | "output_type": "stream", 279 | "text": [ 280 | "\t\t\t Positive \t Negative\n", 281 | "CoffeeMaker (1.0) \t 27 \t\t 20\n", 282 | "JFreeChart (0.6.0) \t 406 \t\t 55\n", 283 | "JFreeChart (0.7.1) \t 520 \t\t 68\n", 284 | "JHotDraw (7.4.1) \t 760 \t\t 1025\n" 285 | ] 286 | } 287 | ], 288 | "source": [ 289 | "print('\\t\\t\\t Positive \\t Negative')\n", 290 | "for project in projects:\n", 291 | " data = examples.filter(method__project__id=project.id)\n", 292 | " print('{0} \\t {1} \\t\\t {2}'.format(str(project),\n", 293 | " data.filter(target=COHERENT).count(),\n", 294 | " data.filter(target=NOT_COHERENT).count()))" 295 | ] 296 | }, 297 | { 298 | "cell_type": "code", 299 | "execution_count": null, 300 | "metadata": { 301 | "collapsed": true 302 | }, 303 | "outputs": [], 304 | "source": [] 305 | } 306 | ], 307 | "metadata": { 308 | "kernelspec": { 309 | "display_name": "Python 3", 310 | "language": "python", 311 | "name": "python3" 312 | }, 313 | "language_info": { 314 | "codemirror_mode": { 315 | "name": "ipython", 316 | "version": 3 317 | }, 318 | "file_extension": ".py", 319 | "mimetype": "text/x-python", 320 | "name": "python", 321 | "nbconvert_exporter": "python", 322 | "pygments_lexer": "ipython3", 323 | "version": "3.4.3" 324 | } 325 | }, 326 | "nbformat": 4, 327 | "nbformat_minor": 0 328 | } 329 | -------------------------------------------------------------------------------- /Benchmark Data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Benchmark Creation\n", 8 | "\n", 9 | "Notebook to create the report file to export Benchmark data (to be released)\n", 10 | "\n", 11 | "**Note:** : this notebook assumes the use of **Python 3**" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "## Preamble: Settings Django Environment" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": null, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "%load preamble_directives.py" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "## Benchmark Report (per Project)" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 2, 40 | "metadata": {}, 41 | "outputs": [], 42 | "source": [ 43 | "from source_code_analysis.models import SoftwareProject" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 3, 49 | "metadata": {}, 50 | "outputs": [], 51 | "source": [ 52 | "projects = SoftwareProject.objects.all()" 53 | ] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "metadata": {}, 58 | "source": [ 59 | "The **Replication Dataset** contains the following report files:\n", 60 | "\n", 61 | "* (`Benchmark_Coherence_Data.txt`, `Benchmark_Raw_Data.txt`):\n", 62 | " Report files containing information about the Coherence and the raw data of methods from all the 4 considered \n", 63 | " Software Systems.\n", 64 | "* (`CoffeeMaker_Coherence_Data.txt`, `CoffeeMaker_Raw_Data.txt`):\n", 65 | " Report files providing the Coherence and the raw data of methods gathered from the **CoffeeMaker** Software System.\n", 66 | "* (`JFreeChart060_Coherence_Data.txt`, `JFreeChart060_Raw_Data.txt`):\n", 67 | " Report files providing the Coherence and the raw data of methods gathered from the **JFreeChart 0.6.0** Software System.\n", 68 | "* (`JFreeChart071_Coherence_Data.txt`, `JFreeChart071_Raw_Data.txt`):\n", 69 | " Report files providing the Coherence and the raw data of methods gathered from the **JFreeChart 0.7.1** Software System.\n", 70 | "* (`JHotDraw741_Coherence_Data.txt`, `JHotDraw741_Raw_Data.txt`):\n", 71 | " Report files providing the Coherence and the raw data of methods gathered from the **JHotDraw 7.4.1** Software System." 72 | ] 73 | }, 74 | { 75 | "cell_type": "markdown", 76 | "metadata": {}, 77 | "source": [ 78 | "### Coherence Data Report Structure\n", 79 | "\n", 80 | "Report files providing information about the *Coherence* of methods are structured according to the CSV \n", 81 | "(i.e., *Comma Separated Values*) format.\n", 82 | "Each line of the file contains the following information:\n", 83 | "\n", 84 | " method_id, coherence\n", 85 | " \n", 86 | "* `method_id`: the unique identifier of the corresponding method\n", 87 | "* `coherence` : the coherence value associated to the comment and the implementation of the referred method. \n", 88 | " Allowed Coherence Values are: `NOT_COHERENT` and `COHERENT`. \n", 89 | " In case, it would be more than straightforward to translate these values into `0`, `1` values, respectively." 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 4, 95 | "metadata": {}, 96 | "outputs": [], 97 | "source": [ 98 | "# Write Coherence Report\n", 99 | "def write_coherence_report(coherence_report_filepath, target_methods):\n", 100 | " with open(coherence_report_filepath, 'w') as coherence_report:\n", 101 | " for method in target_methods:\n", 102 | " evaluation = method.agreement_evaluations.all()[0]\n", 103 | " coherence_value = 'COHERENT' if evaluation.agreement_vote in [3, 4] else 'NOT_COHERENT'\n", 104 | " coherence_report.write('{0}, {1}\\n'.format(method.pk, coherence_value))" 105 | ] 106 | }, 107 | { 108 | "cell_type": "markdown", 109 | "metadata": {}, 110 | "source": [ 111 | "### Raw Data Report Structure\n", 112 | "\n", 113 | "All the report files containing the raw data of the methods share exactly the **same** *multiline* structure. \n", 114 | "That is (*for each method*): \n", 115 | "\n", 116 | " method_id, method_name, class_name, software_system\n", 117 | " filepath, start_line, end_line, \n", 118 | " Length of the Head Comments\n", 119 | " Head Comment\n", 120 | " Length of the Implementation\n", 121 | " Method Implementation\n", 122 | " ###" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": 9, 128 | "metadata": {}, 129 | "outputs": [], 130 | "source": [ 131 | "# Write Raw Data Report\n", 132 | "def write_raw_data_report(raw_report_filepath, target_methods):\n", 133 | " with open(raw_report_filepath, 'w') as raw_report:\n", 134 | " for method in target_methods:\n", 135 | " software_system_name = method.project.name + method.project.version.replace('.', '')\n", 136 | " raw_report.write('{mid}, {method_name}, {class_name}, {software_system}\\n'.format(\n", 137 | " mid=method.id, method_name=method.method_name, class_name=method.code_class.class_name,\n", 138 | " software_system=software_system_name))\n", 139 | " \n", 140 | " method_fp = method.file_path\n", 141 | " relative_filepath = method_fp[method_fp.find('extracted')+len('extracted')+1:]\n", 142 | " raw_report.write('{filepath}, {start_line}, {end_line}\\n'.format(filepath=relative_filepath, \n", 143 | " start_line=method.start_line, \n", 144 | " end_line=method.end_line))\n", 145 | " \n", 146 | " raw_report.write('{comment_len}\\n'.format(comment_len=len(method.comment.splitlines())))\n", 147 | " raw_report.write('{comment}'.format(comment=method.comment))\n", 148 | " if not method.comment.endswith('\\n'):\n", 149 | " raw_report.write('\\n')\n", 150 | " \n", 151 | " raw_report.write('{code_len}\\n'.format(code_len=len(method.code_fragment.splitlines())))\n", 152 | " raw_report.write('{code}'.format(code=method.code_fragment))\n", 153 | " if not method.code_fragment.endswith('\\n'):\n", 154 | " raw_report.write('\\n')\n", 155 | " \n", 156 | " # Last Line of this method\n", 157 | " raw_report.write('###\\n')" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": 10, 163 | "metadata": {}, 164 | "outputs": [], 165 | "source": [ 166 | "RAW_DATA_SUFFIX = 'Raw_Data.txt'\n", 167 | "COHERENCE_DATA_SUFFIX = 'Coherence_Data.txt'" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": 11, 173 | "metadata": {}, 174 | "outputs": [], 175 | "source": [ 176 | "import os\n", 177 | "\n", 178 | "# Create Report Folder\n", 179 | "report_folderpath = os.path.join(os.path.abspath(os.path.curdir), 'report_files')\n", 180 | "if not os.path.exists(report_folderpath):\n", 181 | " os.makedirs(report_folderpath)" 182 | ] 183 | }, 184 | { 185 | "cell_type": "code", 186 | "execution_count": 15, 187 | "metadata": {}, 188 | "outputs": [], 189 | "source": [ 190 | "all_methods_list = list()\n", 191 | "\n", 192 | "# Project-Specific Reports\n", 193 | "\n", 194 | "for project in projects:\n", 195 | " software_system_name = project.name + project.version.replace('.', '')\n", 196 | " target_methods = list()\n", 197 | " project_methods = project.code_methods.order_by('pk')\n", 198 | " # Collect Project Methods whose evaluations are Coherent|Not Coherent\n", 199 | " for method in project_methods:\n", 200 | " evaluation = method.agreement_evaluations.all()[0]\n", 201 | " if not evaluation.wrong_association and evaluation.agreement_vote != 2:\n", 202 | " target_methods.append(method)\n", 203 | " \n", 204 | " all_methods_list.extend(target_methods)\n", 205 | " \n", 206 | " # Coherence Data Report\n", 207 | " coherence_report_filename = '{0}_{1}'.format(software_system_name, COHERENCE_DATA_SUFFIX)\n", 208 | " coherence_report_filepath = os.path.join(report_folderpath, coherence_report_filename)\n", 209 | " \n", 210 | " write_coherence_report(coherence_report_filepath, target_methods)\n", 211 | " \n", 212 | " # Raw Data Report\n", 213 | " raw_report_filename = '{0}_{1}'.format(software_system_name, RAW_DATA_SUFFIX)\n", 214 | " raw_report_filepath = os.path.join(report_folderpath, raw_report_filename)\n", 215 | " \n", 216 | " write_raw_data_report(raw_report_filepath, target_methods)\n", 217 | " \n", 218 | "\n", 219 | "# -- Entire Benchmark Reports\n", 220 | "\n", 221 | "# Coherence Data Report\n", 222 | "coherence_report_filename = '{0}_{1}'.format('Benchmark', COHERENCE_DATA_SUFFIX)\n", 223 | "coherence_report_filepath = os.path.join(report_folderpath, coherence_report_filename)\n", 224 | "\n", 225 | "write_coherence_report(coherence_report_filepath, all_methods_list)\n", 226 | "\n", 227 | "# Raw Data Report\n", 228 | "raw_report_filename = '{0}_{1}'.format('Benchmark', RAW_DATA_SUFFIX)\n", 229 | "raw_report_filepath = os.path.join(report_folderpath, raw_report_filename)\n", 230 | "\n", 231 | "write_raw_data_report(raw_report_filepath, all_methods_list)" 232 | ] 233 | }, 234 | { 235 | "cell_type": "code", 236 | "execution_count": null, 237 | "metadata": {}, 238 | "outputs": [], 239 | "source": [ 240 | "" 241 | ] 242 | } 243 | ], 244 | "metadata": { 245 | "kernelspec": { 246 | "display_name": "Python 3", 247 | "language": "python", 248 | "name": "python3" 249 | }, 250 | "language_info": { 251 | "codemirror_mode": { 252 | "name": "ipython", 253 | "version": 3.0 254 | }, 255 | "file_extension": ".py", 256 | "mimetype": "text/x-python", 257 | "name": "python", 258 | "nbconvert_exporter": "python", 259 | "pygments_lexer": "ipython3", 260 | "version": "3.4.3" 261 | } 262 | }, 263 | "nbformat": 4, 264 | "nbformat_minor": 0 265 | } -------------------------------------------------------------------------------- /evaluations.py: -------------------------------------------------------------------------------- 1 | """This module contains the code to perform the analysis 2 | on the evaluations about the coherence of methods 3 | made by involved annotators. 4 | 5 | In more details, the module provides: 6 | `Judge`: class that embeds annotations of a single user/evaluator 7 | 8 | `calculate_agreement_scores`: function to compute the *Agreement Score Matrix* 9 | according to the evaluations of two input Judges. 10 | 11 | `cohense_kappa`: function to compute the (weighted) Cohen's Kappa Index according 12 | to input agreement rates. 13 | 14 | `mean_precision`: function to calculate the *Precision Index* among judges 15 | on a given set of evaluations. 16 | """ 17 | # Author: Valerio Maggio 18 | # License: BSD 3 clause 19 | 20 | from django.contrib.auth.models import User 21 | from source_code_analysis.models import SoftwareProject 22 | import numpy as np 23 | 24 | 25 | class Judge: 26 | """ 27 | This class embeds information about a single Judge. 28 | """ 29 | 30 | CODES = 5 31 | 32 | def __init__(self, username, project_name, project_version=None): 33 | """ 34 | Parameters: 35 | ---------- 36 | username: str 37 | Username of the Judge used to retrieve it from the Database 38 | 39 | project_name: str 40 | The name of the project to get the data for 41 | 42 | project_version: str, optional (default: None) 43 | The specific version of the project to fetch from the database. 44 | """ 45 | try: 46 | self.user_model = User.objects.get(username__iexact=username) 47 | except User.DoesNotExist: 48 | print('Warning: No User Found with the input username: %s' % username) 49 | self.user_model = None 50 | 51 | try: 52 | if project_version: 53 | project = SoftwareProject.objects.get(name__iexact=project_name, version__iexact=project_version) 54 | else: 55 | project = SoftwareProject.objects.get(name__iexact=project_name) 56 | except SoftwareProject.DoesNotExist: 57 | print('Warning: No SoftwareProject Found with the input name: %s' % project_name) 58 | except SoftwareProject.MultipleObjectsReturned: 59 | print('Warning: Multiple SoftwareProject Found with the given name: %s' % project_name) 60 | else: 61 | self.project = project 62 | 63 | 64 | if self.user_model and self.project: 65 | # This will be a list of sets containing the ids of reference methods, for every evaluation Code. 66 | self._evaluations = list() 67 | self.fetch_agreement_evaluations() 68 | 69 | 70 | def fetch_agreement_evaluations(self): 71 | """ 72 | Fetches and stores the agreement evaluations of the associated 73 | user on the selected Project 74 | """ 75 | evaluations = self.user_model.evaluations.filter(reference_method__project__id= 76 | self.project.id).exclude(evaluated=False).exclude(wrong_association=True) 77 | 78 | # Judgements have been mapped on a Nominal Scale of values ranging from 0 to 4 (see CODES) 79 | # namely:"Strong Disagreement, Disagreement, Don't Know, Agreement, Strong Agreement" 80 | for i in range(self.CODES): 81 | self._evaluations.append(set([eval.reference_method.id for eval in evaluations.filter(agreement_vote=i)])) 82 | 83 | @property 84 | def five_codes_evaluations(self): 85 | """ 86 | Returns the whole set of evaluations with all the CODES (i.e., 5) 87 | """ 88 | return self._evaluations 89 | 90 | @property 91 | def three_codes_evaluations(self): 92 | """ 93 | Returns the set of evaluations corresponding to "only" three Codes, 94 | namely "No Coherence", "Don't Know", "Coherence". 95 | """ 96 | evaluations = list() 97 | evaluations.append(self._evaluations[0].union(self._evaluations[1])) 98 | evaluations.append(self._evaluations[2]) 99 | evaluations.append(self._evaluations[3].union(self._evaluations[4])) 100 | return evaluations 101 | 102 | @property 103 | def two_codes_evaluations(self): 104 | """ 105 | Returns the set of evaluations corresponding to "only" two Codes, 106 | namely "No Coherence", "Coherence", respectively. 107 | """ 108 | evaluations = list() 109 | evaluations.append(self._evaluations[0].union(self._evaluations[1])) 110 | evaluations.append(self._evaluations[3].union(self._evaluations[4])) 111 | return evaluations 112 | 113 | 114 | def calculate_agreement_scores(judge1, judge2, k=3): 115 | """ 116 | Calculate the Agreement Score Matrix according to the evaluations of the 117 | two input Judges. 118 | 119 | Parameters: 120 | ----------- 121 | judge1 : The first judge (instance of Judge class) 122 | judge2 : The second judge (instance of Judge class) 123 | k : The number of codes to consider choosing from 3 or 5 codes. 124 | If None, other values, or no value will be given, the default number 125 | of codes will be considered, namely 3. 126 | 127 | Returns: 128 | -------- 129 | J : the k x k agreement score matrix (to be passed to the cohens_kappa function) 130 | """ 131 | 132 | if not k or not k in (2, 3, 5): 133 | k = 3 134 | 135 | if k == 2: 136 | j1_evals = judge1.two_codes_evaluations 137 | j2_evals = judge2.two_codes_evaluations 138 | elif k == 3: 139 | j1_evals = judge1.three_codes_evaluations 140 | j2_evals = judge2.three_codes_evaluations 141 | else: 142 | j1_evals = judge1.five_codes_evaluations 143 | j2_evals = judge2.five_codes_evaluations 144 | 145 | # Purge inconsistencies, i.e., evaluations appearing only in one judge set 146 | def merge_all_evaluations(evaluations): 147 | jall = set() 148 | for i in range(k): 149 | jall = jall.union(evaluations[i]) 150 | return jall 151 | 152 | j1_all = merge_all_evaluations(j1_evals) 153 | j2_all = merge_all_evaluations(j2_evals) 154 | 155 | # Calculate Differences 156 | j1_j2_diff = j1_all.difference(j2_all) 157 | j2_j1_diff = j2_all.difference(j1_all) 158 | 159 | # Remove Differences 160 | if j1_j2_diff: 161 | j1_all = j1_all.difference(j1_j2_diff) 162 | if j2_j1_diff: 163 | j2_all = j2_all.difference(j2_j1_diff) 164 | 165 | assert j1_all == j2_all # verify that so far the two sets are equal 166 | 167 | # Now iterate over all the sets for each code and purge all the methods not appearing in the 168 | # intersection among each score set and the global (i.e., "_all") one. 169 | for i in range(k): 170 | j1_evals[i] = j1_evals[i].intersection(j1_all) 171 | j2_evals[i] = j2_evals[i].intersection(j2_all) 172 | 173 | J = list() 174 | for i in range(k): 175 | J.append(list()) 176 | for j in range(k): 177 | J[i].append(len(j1_evals[i].intersection(j2_evals[j]))) 178 | 179 | J = np.array(J) 180 | return J 181 | 182 | 183 | def cohens_kappa(J, weighted=False, log=False): 184 | """ 185 | Calculate the Cohen's Kappa Index. 186 | 187 | $kappa = 1 - \frac{\sum W*X}{\sum W*M}$ 188 | 189 | where $*$ indicates the element-wise matrix multiplication. 190 | 191 | $X$: Is the matrix of Observed Scores 192 | 193 | $M$: Is the matrix of Score Agreement by Chance 194 | 195 | $W$: Is the Weight Matrix. 196 | 197 | Note: So far, the implementation considers only two judges 198 | 199 | Parameters: 200 | ----------- 201 | 202 | J : K^N numpy array containing the evaluation scores of Judges, 203 | where K is the number of codes and N (i.e., J.ndim) is the number of 204 | considered Judges. 205 | (see `calculate_agreement_scores` for further details). 206 | 207 | weighted : bool indicating if the Weighted Formulation of the Cohen's Kappa should be calculated 208 | (default=False and the unweighted index is computed.) 209 | """ 210 | 211 | k = J.shape[0] # number of codes 212 | n = J.ndim # number of judges 213 | 214 | # Calculate X: The Matrix of Observed Scores 215 | X = J / np.sum(J) 216 | 217 | if log: 218 | print('J: \n', J) 219 | print('X: \n', X) 220 | 221 | # Determine the Weight Matrix 222 | if not weighted: 223 | # if unweighted, the Weight Matrix W has diagonal cells 224 | # containing zeros and all off-diagonal cells weights of one 225 | W = np.ones((k,k)) 226 | np.fill_diagonal(W, 0) 227 | else: 228 | # In case Weighted Cohens' Kappa should be calculated, the 229 | # weighted matrix W is initialized according to the Stevens Scaling 230 | # Formula. In more details, in case of a 3x3 Matrix, W is euqal to 231 | # W = [[ 0., 1., 4.], 232 | # [ 1., 0., 1.], 233 | # [ 4., 1., 0.]] 234 | # In other words, zero elements on the diagonal, one off diagonal elements are 1^2, 235 | # those two off diagonal elements are 2^2 236 | W = np.zeros((k,k)) 237 | for i in range(1, k): 238 | W += (np.eye(k, k, i) * (i**2)) + (np.eye(k, k, -i) * (i**2)) 239 | if log: 240 | print('W: \n', W) 241 | 242 | # Calculate M: the Matrix of Agreement by Chance 243 | J_sum = np.zeros((n,k)) 244 | AG = np.zeros((n,k)) 245 | for i in range(n): 246 | AG[i] = np.sum(J, axis=1-i) 247 | J_sum = AG / np.sum(J) # Calculate Total Probabilities 248 | 249 | if log: 250 | print('AG: \n', AG) 251 | print('J_sum: \n', J_sum) 252 | 253 | M = np.zeros((k,k)) 254 | for i in range(k): 255 | M[i] = np.multiply(J_sum[0,i], J_sum[1]) 256 | 257 | if log: 258 | print('M: \n', M) 259 | 260 | kappa = 1 - (np.sum(W*X)/np.sum(W*M)) 261 | 262 | return kappa 263 | 264 | 265 | def mean_precision(j1, j2): 266 | """ 267 | Calculate the Precision Index among judges for a given set of evaluations. 268 | In particular, the function takes in input two sets of evaluations (one for each judges) 269 | referring to a single score for a given systems. 270 | For *each* judge, the function computes $P(ji) = P(j1 \cap j2) / P(ji)$, namely the precision 271 | of evaluations of considered judge $ji$. 272 | The two values, $P(j1)$ and P(j2), are then combined by an Harmonic Mean, that is 273 | finally returned by the function. 274 | 275 | Parameters: 276 | ----------- 277 | 278 | j1: set of evaluations of the first judge, corresponding to a **single** score for a **single** 279 | system (e.g., "Method-Comment Aggreement" in Project "X") 280 | j2: set of evaluations of the second judge (see above for further details) 281 | 282 | Returns: 283 | -------- 284 | 285 | pj1: Precision associated to the first judge 286 | pj2: Precision associated to the second judge 287 | F: Armonic Mean between pj1 and pj2 288 | """ 289 | 290 | pj1 = 0.0 if not len(j1) else len(j1.intersection(j2))/len(j1) 291 | pj2 = 0.0 if not len(j2) else len(j2.intersection(j1))/len(j2) 292 | if (pj1 == pj2 == 0.0): 293 | return 0.0, 0.0, 0.0 294 | f = 2 * ((pj1 * pj2) / (pj1 + pj2)) 295 | return pj1, pj2, f 296 | -------------------------------------------------------------------------------- /data/distributions_per_rate_tfidf/CO_Jfreechart_(0.6.0)_406.txt: -------------------------------------------------------------------------------- 1 | 3.071160219600082009e-01 2 | 5.540411606569014547e-01 3 | 4.308930645122245329e-01 4 | 4.796439986074575446e-01 5 | 3.886720642800363668e-01 6 | 4.837978514157987853e-01 7 | 4.583196942433600540e-01 8 | 3.861693689523600925e-01 9 | 4.533213640439720526e-01 10 | 2.942218844864885607e-01 11 | 2.766528050363520896e-01 12 | 5.071982975745743527e-01 13 | 4.871572773093195696e-01 14 | 4.194335965422987256e-01 15 | 4.553919930765042423e-01 16 | 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1.780143554468658074e-01 404 | 3.373083690095743381e-01 405 | 4.733200769421063159e-01 406 | 2.154411883823025287e-01 407 | -------------------------------------------------------------------------------- /coherence.py: -------------------------------------------------------------------------------- 1 | """ 2 | Caching and Loading the Coherence Dataset 3 | """ 4 | # Author: Valerio Maggio 5 | # Copyright (c) 2015 Valerio Maggio 6 | # License: BSD 3 clause 7 | 8 | from os.path import join, abspath, curdir, exists 9 | from operator import or_ 10 | from functools import reduce 11 | from itertools import chain 12 | 13 | from sklearn.pipeline import Pipeline 14 | from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer 15 | from sklearn.datasets.base import Bunch 16 | from scipy.sparse import csr_matrix 17 | import numpy as np 18 | 19 | try: 20 | from coherence_dataset.settings import COHERENT, NOT_COHERENT 21 | from coherence_dataset.models import Example 22 | from source_code_analysis.models import SoftwareProject 23 | from django.db.models import Q 24 | except ImportError: 25 | pass 26 | 27 | import codecs 28 | import pickle 29 | from sklearn.externals import joblib 30 | 31 | 32 | TARGET_PROJECTS = ( 33 | ('CoffeeMaker', '1.0'), 34 | ('Jfreechart', '0.6.0'), 35 | ('Jfreechart', '0.7.1'), 36 | ('JHotDraw', '7.4.1'), 37 | ) 38 | 39 | TARGET_FOLDER = join(abspath(curdir), 'data', 'dataset') 40 | TARGET_METADATA_FILENAME = 'coherence_meta.pkz' 41 | TARGET_DATA_FILENAME = 'coherence_data.pkz' 42 | DATASET_DESCRIPTION_FILENAME = 'coherence.rst' 43 | 44 | 45 | def fetch_coherence_dataset(): 46 | """Fetch the Coherence dataset from the database 47 | (leveraging on Django ORM settings and connection) and 48 | returns data ready to be stored locally on cache files. 49 | Additional *metadata* about the dataset are collected, and 50 | returned as well. 51 | 52 | Returns 53 | ------- 54 | data: numpy ndarray of shape (2881, 5642) 55 | The (dense) array structure containing all the representation of 56 | methods (i.e. code and comment) in the feature space (VSM). 57 | Methods are gathered from all the considered software projects and 58 | are stored in "(class, project)" order - positive ("COHERENT") 59 | examples first. 60 | 61 | targets: numpy ndarray of shape (2881, ) 62 | The array of targets, stored according to the organization 63 | of documents/methods in the `data` matrix, namely 64 | "Coherent" method first, and then "Non Coherent" ones. 65 | 66 | metadata: Bunch (`sklearn.datasets.base.Bunch`) 67 | Dictionary-like object containing dataset metadata. 68 | Stored information (key/value pairs) are: 69 | 'N' : integer 70 | number of samples (methods) 71 | 'D' : integer 72 | number of features 73 | 'code' : list 74 | list of terms extracted from body of methods 75 | 'comments' : list 76 | list of terms extracted from comments of methods 77 | 'features' : list 78 | list of feature names (terms) / IR vocabulary 79 | 'target_names' : tuple 80 | tuple containing the label associated to each class 81 | 'examples_per_classes' : tuple 82 | tuple containing the numbers of examples in each class 83 | 'projects' : dictionary 84 | dictionary containing information about the analysed 85 | software projects (i.e. name, version, offset indices in 86 | the data matrix, number of examples per target class). 87 | """ 88 | # -------------------------------------- 89 | # Gather `Example` instances from the Db 90 | # -------------------------------------- 91 | per_project_filters = list() 92 | for name, version in TARGET_PROJECTS: 93 | try: 94 | project = SoftwareProject.objects.get(name__iexact=name, version__exact=version) 95 | per_project_filters.append(Q(method__project__id=project.id)) 96 | except SoftwareProject.DoesNotExist: 97 | continue 98 | projects_filters = reduce(or_, per_project_filters) 99 | positive_exs = Example.objects.filter(target=COHERENT).filter(projects_filters) 100 | negative_exs = Example.objects.filter(target=NOT_COHERENT).filter(projects_filters) 101 | # Create the Document Collection 102 | # NOTE: (All) documents of *positive* examples first. 103 | doc_collection = list() 104 | for example in chain(positive_exs, negative_exs): 105 | doc_collection.append(example.method.lexical_info.normalized_comment) 106 | doc_collection.append(example.method.lexical_info.normalized_code) 107 | 108 | # (META) Textual Data 109 | comments = doc_collection[::2] # odd elements correspond to comments 110 | code = doc_collection[1::2] # even elements correspond to code 111 | 112 | # Set up the processing pipeline to `fit_transform` data 113 | pipeline = Pipeline([ 114 | ('vect', CountVectorizer(input='content', lowercase=False)), 115 | ('tfidf', TfidfTransformer(sublinear_tf=True, norm='l2', use_idf=True)), 116 | ]) 117 | all_data = pipeline.fit_transform(doc_collection) 118 | nrows, ncols = all_data.shape 119 | 120 | # Compacting data to (methods, (comment, code)) features 121 | data = all_data.toarray().reshape(nrows // 2, -1) 122 | 123 | # (META) Textual Feature representation 124 | # The list of indexed terms 125 | vectorizer = pipeline.named_steps['vect'] 126 | features = vectorizer.get_feature_names() 127 | 128 | # (META) Count Examples per Classes 129 | positive_ex_count = positive_exs.count() 130 | negative_ex_count = negative_exs.count() 131 | 132 | # Create target array, according to examples distribution 133 | targets = np.hstack((np.ones(positive_ex_count), 134 | np.zeros(negative_ex_count))) 135 | 136 | # ----------------------------------- 137 | # Get and Save additional (Meta)data 138 | # ----------------------------------- 139 | # Per-project information: 140 | # - project name 141 | # - project version 142 | # - Offset of projects entries in dataset 143 | # - Examples count (positive and negative) 144 | # - "names" : list of keys (name-version) to access 145 | # single project data 146 | projects_info = dict() 147 | projects_info.setdefault('names', list()) 148 | 149 | pos_start = pos_end = 0 150 | neg_start = neg_end = positive_exs.count() 151 | for name, version in TARGET_PROJECTS: 152 | try: 153 | # 0. Set project key (i.e. `pkey`) 154 | pkey = '{0}-{1}'.format(name, version) 155 | projects_info['names'].append(pkey) 156 | # 1. get project examples 157 | project = SoftwareProject.objects.get(name__iexact=name, version__exact=version) 158 | project_examples = Example.objects.filter(method__project__id=project.id) 159 | # 2. count positive and negative ones, 160 | # and update the ending coordinates of offsets (positive, and negative) 161 | pos_count = project_examples.filter(target=COHERENT).count() 162 | neg_count = project_examples.filter(target=NOT_COHERENT).count() 163 | pos_end += pos_count 164 | neg_end += neg_count 165 | # 3. Store metadata for current project 166 | projects_info[pkey] = {'name': name, 'version': version, 167 | 'positive_examples': (pos_start, pos_end), 168 | 'negative_examples': (neg_start, neg_end), 169 | 'positive_count': pos_count, 170 | 'negative_count': neg_count} 171 | # 4. Update the starting coordinates for the next offsets count 172 | pos_start = pos_end 173 | neg_start = neg_end 174 | except SoftwareProject.DoesNotExist: 175 | continue 176 | 177 | # *. Dataset (meta)data descriptors 178 | # - N, D --> no. of examples, and no. of features 179 | # - Class names and no. of instances per classes 180 | # - Project names and versions, along with corresponding offsets 181 | # - Target (class) names. 182 | metadata = Bunch(code=code, comments=comments, features=features, 183 | N=nrows, D=ncols, target_names=('COHERENT', 'NOT COHERENT'), 184 | examples_per_classes=(positive_ex_count, negative_ex_count), 185 | projects=projects_info) 186 | return data, targets, metadata 187 | 188 | 189 | def load_coherence_dataset(): 190 | """Load and returns the coherence dataset (classification) 191 | 192 | The Coherence dataset contains information about the 193 | coherence between the head comment and the implementation 194 | of a source code methods. 195 | 196 | ================= ============== 197 | Classes 2 198 | Samples per class (Pos) 1713 199 | (Neg) 1168 200 | Samples total (Tot) 2881 201 | Dimensionality 5642 202 | Unique Terms 2821 203 | Features real, positive 204 | ================= ============== 205 | 206 | Note: 207 | ----- 208 | Since methods are gathered from different software projects, to ease data 209 | analysis (e.g. slicing, splitting or extracting data of a single software project), 210 | data are stored according to class and project, respectively. 211 | In particular, data are primarily grouped by classes (all positive instances, first), and then 212 | further organized per project. 213 | 214 | So far, these are the distribution of examples per software project: 215 | ====================== =========================== 216 | Project Positive | Negative | Total 217 | CoffeeMaker (1.0) 27 | 20 | 47 218 | JFreeChart (0.6.0) 406 | 55 | 461 219 | JFreeChart (0.7.1) 520 | 68 | 588 220 | JHotDraw (7.4.1) 760 | 1025 | 1785 221 | ====================== =========================== 222 | 223 | 224 | Returns 225 | ------- 226 | data: Bunch 227 | Dictionary-like object, the intersting attributes (keys) are: 228 | 'data', the actual data to learn, 'target', 'the classification labels, 229 | 'target_names', the meaning of the labels, 'feature_names', the 230 | meaning of features (i.e. the `n_features` terms extracted), and 231 | 'DESCR', the full description of the dataset. 232 | """ 233 | 234 | # Setup target (cache) path(s) 235 | cache_data_path = join(TARGET_FOLDER, TARGET_DATA_FILENAME) 236 | cache_metadata_path = join(TARGET_FOLDER, TARGET_METADATA_FILENAME) 237 | 238 | if not exists(cache_data_path): 239 | # get data from db and save files 240 | 241 | data, target, metadata = fetch_coherence_dataset() 242 | data_sparse = csr_matrix(data, shape=data.shape, dtype=np.float64) 243 | 244 | # Cache Dataset to disk, namely: 245 | # 1. Dump Data (numpy ndarray file) 246 | 247 | joblib.dump(data, cache_data_path, compress=6) 248 | 249 | # 2. Dump Metadata 250 | compressed_meta = codecs.encode(pickle.dumps(metadata), 'zlib_codec') 251 | 252 | with open(cache_metadata_path, 'wb') as cache_metadata_file: 253 | cache_metadata_file.write(compressed_meta) 254 | else: 255 | # load data from file 256 | data = joblib.load(cache_data_path) 257 | data_sparse = csr_matrix(data, shape=data.shape, dtype=np.float64) 258 | 259 | # load metadata from file 260 | with open(cache_metadata_path, 'rb') as f: 261 | compressed_content = f.read() 262 | uncompressed_content = codecs.decode( 263 | compressed_content, 'zlib_codec') 264 | cache_metadata = pickle.loads(uncompressed_content) 265 | # Store metadata in a Bunch object 266 | metadata = Bunch() 267 | metadata.update(cache_metadata) 268 | 269 | # return the Bunch object 270 | 271 | # load Dataset description 272 | with open(join(TARGET_FOLDER, DATASET_DESCRIPTION_FILENAME)) as rst_file: 273 | fdescr = rst_file.read() 274 | 275 | # Create target array, according to examples distribution 276 | positive_ex_count, negative_ex_count = metadata.examples_per_classes 277 | target = np.hstack((np.ones(positive_ex_count), 278 | np.zeros(negative_ex_count))) 279 | 280 | return Bunch(data=data_sparse, target=target, 281 | target_names=metadata.target_names, 282 | N=metadata.N, D=metadata.D, 283 | features=metadata.features, code=metadata.code, 284 | comments=metadata.comments, DESCR=fdescr, 285 | target_counts = (positive_ex_count, negative_ex_count), 286 | projects=metadata.projects) 287 | 288 | 289 | -------------------------------------------------------------------------------- /data/distributions_per_rate_tfidf/CO_Jfreechart_(0.7.1)_520.txt: -------------------------------------------------------------------------------- 1 | 6.729899133216447060e-01 2 | 7.660611675989728031e-01 3 | 6.997287822722633166e-01 4 | 7.026342122565983894e-01 5 | 7.226556645125487011e-01 6 | 6.986667125092953512e-01 7 | 7.695781611603645933e-01 8 | 6.136850930111125146e-01 9 | 3.885034840768329945e-01 10 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| aalc:= atm adapt layer connect 5 | aarp:= appletalk address resolut protocol 6 | abi:= applic binari interfac 7 | abs:= absolute 8 | acf:= advanc communic function 9 | acid:= atom consist isol durabl 10 | acct:= accept 11 | ack:= acknowledg 12 | acl:= access control list 13 | acp:= airlin control program 14 | aka:= a kind of 15 | adc:= appl display connector 16 | addr:=address 17 | adb:= appl desktop bus 18 | adccp:= advanc data communic control procedur 19 | ado:= activex data object 20 | adsl:= asymmetr digit subscrib line 21 | adt:= abstract data type 22 | afp:= appl file protocol 23 | agp:= acceler graphic port 24 | aix:= advanc interact execut 25 | alac:= appl lossless audio codec 26 | alsa:= advanc linux sound architectur 27 | amd:= advanc micro devic 28 | amr:= audio modem riser 29 | ann:= artifici neural network 30 | ansi:= american nation standard institut 31 | aoe:= ata over ethernet 32 | aop:= aspect orient program 33 | api:= applic program interfac 34 | apic:= advanc programm interrupt control 35 | apl:= program languag 36 | apr:= apach portabl runtim 37 | app:=applic 38 | arm:= advanc risc machin 39 | arp:= address resolut protocol 40 | arg:= argument 41 | argn:= argument 42 | arpa:= advanc research project agenc 43 | asg:= abstract semant graph 44 | aslr:= address space layout random 45 | asm:= algorithm state machin 46 | asp:= activ server page 47 | asp:= applic servic provid 48 | asr:= asynchron signal routin 49 | ast:= abstract syntax tree 50 | assp:= applic specif standard product 51 | ata:= advanc technolog attach 52 | atexit:= exit 53 | atag:= author tool access guidelin 54 | atapi:= advanc technolog attach packet interfac 55 | atm:= asynchron transfer mode 56 | attr:=attribut 57 | awt:= abstract window toolkit 58 | auth:= authent 59 | bal:= basic assembl languag 60 | bash:= bourn again shell 61 | backw:= backward 62 | bcnf:= boyc codd normal form 63 | beep:= block extens exchang protocol 64 | bfd:= binari file descriptor 65 | bin:= binari 66 | bib:= bibliographi 67 | biblio:= bibliographi 68 | bjt:= bipolar junction transistor 69 | blob:= binari larg object 70 | bmp:= basic multilingu plane 71 | bnc:= bayonet neill concelman 72 | bom:= byte order mark 73 | bpdu:= bridg protocol data unit 74 | bpel:= busi process execut languag 75 | bsa:= busi softwar allianc 76 | bsd:= based 77 | bt:= bittorr 78 | bt:= bluetooth 79 | bw:= bandwidth 80 | cad:= comput aid design 81 | cae:= comput aid engin 82 | caid:= comput aid industri design 83 | cai:= comput aid instruct 84 | cam:= comput aid manufactur 85 | cc:= compil 86 | cdma:= code divis multipl access 87 | cdn:= content deliveri network 88 | cdp:= continu data protect 89 | cdsa:= common data secur architectur 90 | cert:= comput emerg respons team 91 | cfd:= comput fluid dynam 92 | cfg:= context free grammar 93 | cfg:= control flow graph 94 | cg:= comput graphic 95 | cga:= color graphic array 96 | cgi:= common gateway interfac 97 | cgi:= comput generat imageri 98 | cgt:= comput graph theori 99 | chap:= challeng handshak authent protocol 100 | cidr:= classless inter domain rout 101 | cim:= common inform model 102 | cisc:= complex instruct set comput 103 | cli:= command line interfac 104 | clr:= common languag runtim 105 | cmmi:= capabl matur model integr 106 | cmd:= command 107 | cnc:= comput numer control 108 | cntrl:= control 109 | corba:= common object request broker architectur 110 | col:=column 111 | coord:=coordin 112 | cpa:= cell processor architectur 113 | cpa:= converg packet access 114 | cpan:= comprehens perl archiv network 115 | cpri:= common public radio interfac 116 | cpu:= central process unit 117 | cpi:=copi 118 | cr:= carriag return 119 | crc:= cyclic redund check 120 | crlf:= carriag return line feed 121 | crm:= custom relationship manag 122 | crt:= cathod ray tube 123 | csi:= common system interfac 124 | csp:= communic sequenti process 125 | csrf:= cross site request forgeri 126 | css:= cascad style sheet 127 | csv:= comma separ valu 128 | ctan:= comprehens tex archiv network 129 | ctcp:= client client protocol 130 | cti:= comput telephoni integr 131 | ctl:= comput tree logic 132 | ctss:= compat time share system 133 | cua:= common user access 134 | cur:=current 135 | cvs:=control version system 136 | cwd:=current work directori 137 | dac:= discretionari access control 138 | dao:= data access object 139 | dao:= disk onc 140 | dap:= directori access protocol 141 | darpa:= defens advanc research project agenc 142 | db:= databas 143 | dccp:= datagram congest control protocol 144 | dcca:= debian common core allianc 145 | dcl:= data control languag 146 | dcmi:= dublin core metadata initi 147 | dcom:= distribut compon object model 148 | ddl:= data definit languag 149 | dev:= develop 150 | dfa:= determinist finit automaton 151 | dfd:= data flow diagram 152 | dfl:=default 153 | dhcp:= dynam host configur protocol 154 | dimm:= dual inlin memori modul 155 | did:=direct inward dialing 156 | dir:=directori 157 | dkim:= domain key identifi mail 158 | dl:= download 159 | dll:= dynam link librari 160 | dlna:= digit live network allianc 161 | dlp:= digit light process 162 | dma:= direct memori access 163 | dmca:= digit millennium copyright act 164 | dmi:= direct media interfac 165 | dml:= data manipul languag 166 | dn:= distinguish name 167 | dom:= document object model 168 | doc:=document 169 | dp:= dot pitch 170 | dpc:= defer procedur call 171 | dpi:= deep packet inspect 172 | dpi:= dot per inch 173 | dram:= dynam random access memori 174 | dri:= direct render infrastructur 175 | drm:= digit right manag 176 | drm:= direct render manag 177 | dsdl:= document schema definit languag 178 | dsdm:= dynam system develop method 179 | dsl:= digit subscrib line 180 | dsl:= domain specif languag 181 | dslam:= digit subscrib line access multiplex 182 | dsn:= databas sourc name 183 | dsn:= data set name 184 | dsp:= digit signal processor 185 | dtd:= document type definit 186 | dte:= data termin equip 187 | dtr:= data termin readi 188 | dvd:= digit versatil disc 189 | dvd:= digit video disc 190 | dvd:= dvd record 191 | dvd:= rw dvd rewrit 192 | dvi:= digit visual interfac 193 | dvr:= digit video record 194 | eap:= extens authent protocol 195 | ebcdic:= extend binari code decim interchang code 196 | ebml:= extens binari meta languag 197 | ecc:= ellipt curv cryptographi 198 | ecma:= european comput manufactur associ 199 | ecn:= explicit congest notif 200 | eda:= electron design autom 201 | edi:= electron data interchang 202 | edo:= extend data out 203 | edsac:= electron delay storag automat calcul 204 | edvac:= electron discret variabl automat comput 205 | eeprom:= electron eras programm read onli memori 206 | eff:= electron frontier foundat 207 | efi:= extens firmwar interfac 208 | ega:= enhanc graphic array 209 | egp:= exterior gateway protocol 210 | eigrp:= enhanc interior gateway rout protocol 211 | eisa:= extend industri standard architectur 212 | elf:= extrem low frequenc 213 | env:=environ 214 | epic:= explicit parallel instruct comput 215 | eprom:= eras programm read onli memori 216 | erp:= enterpris resourc plan 217 | etl:= extract transform load 218 | euc:= extend unix code 219 | euid:=extent user ident 220 | eula:= end user licens agreement 221 | faq:= frequent ask question 222 | fbdimm:= fulli buffer dual inlin memori modul 223 | fcb:= file control block 224 | fdc:= floppi disk control 225 | fdd:= floppi disk drive 226 | fddi:= fiber distribut data interfac 227 | fdma:= frequenc divis multipl access 228 | fec:= forward error correct 229 | fet:= field effect transistor 230 | fifo:= first first out 231 | fld:=field 232 | flac:= free lossless audio codec 233 | fosi:= format output specif instanc 234 | forw:=forward 235 | fpu:= float point unit 236 | fru:= field replac unit 237 | fsb:= front side bus 238 | fsf:= free softwar foundat 239 | fsm:= finit state machin 240 | ftp:= file transfer protocol 241 | fqdn:= fulli qualifi domain name 242 | fud:= fear uncertainti doubt 243 | fxp:= file exchang protocol 244 | gb:= gigabit 245 | gb:= gigabyt 246 | gcc:= gnu compil collect 247 | gcr:= group code record 248 | gdi:= graphic devic interfac 249 | gfdl:= gnu free document licens 250 | gif:= graphic interchang format 251 | gid:=group identifi 252 | gimp:= gnu imag manipul program 253 | gml:= geographi markup languag 254 | gnu:= gnu unix 255 | gpg:= gnu privaci guard 256 | gpib:= general purpos instrument bus 257 | gpl:= general public licens 258 | gpt:= guid partit tabl 259 | gpu:= graphic process unit 260 | gui:= graphic user interfac 261 | gwt:= googl web toolkit 262 | hba:= host bus adapt 263 | hci:= human comput interact 264 | hdd:= hard disk drive 265 | hcl:= hardwar compat list 266 | hdl:= hardwar descript languag 267 | hdmi:= high definit multimedia interfac 268 | hf:= high frequenc 269 | hhd:= hybrid hard drive 270 | hid:= human interfac devic 271 | hig:= human interfac guidelin 272 | hist:=histori 273 | hma:= high memori area 274 | hpc:= high perform comput 275 | hsm:= hierarch storag manag 276 | ht:= hyper thread 277 | htm:= hierarch tempor memori 278 | html:=hyper text markup languag 279 | http:=hyper text transfer protocol 280 | hvd:= holograph versatil disc 281 | hz:= hertz 282 | ibm:= intern busi machin 283 | icmp:= internet control messag protocol 284 | icp:= internet cach protocol 285 | ict:= inform communic technolog 286 | id:= identifi 287 | idlen:=idlen 288 | iec:= intern electrotechn commiss 289 | ietf:= internet engin task forc 290 | ifdef:=definit 291 | ifndef:=definit 292 | ifblk:=block 293 | ifchr:=char 294 | ifsock:=socket 295 | igmp:= internet group manag protocol 296 | igrp:= interior gateway rout protocol 297 | imap:= internet messag access protocol 298 | io:= input output 299 | ipc:= inter process communic 300 | ipl:= initi program load 301 | ipo:= inter procedur optim 302 | ipx:= internetwork packet exchang 303 | irc:= internet relay chat 304 | irq:= interrupt request 305 | isa:= instruct set architectur 306 | isdir:=directori 307 | is:=is 308 | isam:= index sequenti access method 309 | isc:= internet storm center 310 | iscsi:= internet small comput system interfac 311 | isdn:= integr servic digit network 312 | isp:= internet servic provid 313 | ispf:= interact system product facil 314 | isv:= independ softwar vendor 315 | itl:= interv tempor logic 316 | itu:= intern telecommun union 317 | jcl:= job control languag 318 | jcp:= java communiti process 319 | jdk:= java develop kit 320 | jfc:= java foundat class 321 | jfet:= junction field effect transistor 322 | jit:= just time 323 | jmx:= java manag extens 324 | jni:= java nativ interfac 325 | jnz:= jump non zero 326 | jpeg:= joint photograph expert group 327 | jre:= java runtim environ 328 | js:= javascript 329 | jsp:= jackson structur program 330 | jtag:= joint test action group 331 | jug:= java user group 332 | jvm:= java virtual machin 333 | kb:= keyboard 334 | khz:= kilohertz 335 | km:= knowledg machin 336 | kvm:= keyboard video mous 337 | lamp:= linux apach mysql python 338 | lang:=languag 339 | lan:= local area network 340 | lba:= logic block address 341 | lcd:= liquid crystal display 342 | ldap:= lightweight directori access protocol 343 | le:= logic extent 344 | led:= light emit diod 345 | len:=length 346 | lex:= lexicon 347 | lf:= line feed 348 | lf:= low frequenc 349 | lgpl:= lesser general public licens 350 | lib:= librari 351 | lif:= low insert forc 352 | lifo:= last first out 353 | lkml:= linux kernel mail list 354 | lm:= lan manag 355 | lsi:= larg scale integr 356 | ltl:= linear tempor logic 357 | ltr:= left right 358 | lug:= linux user group 359 | lun:= logic unit number 360 | lv:= logic volum 361 | lvd:= low voltag differenti 362 | lvm:= logic volum manag 363 | lzw:= lempel ziv welch 364 | man:= metropolitan area network 365 | mapi:= messag applic program interfac 366 | mb:= megabit 367 | me:=me 368 | mbr:= master boot record 369 | mca:= micro channel architectur 370 | mca:= microsoft certifi architect 371 | mcad:= microsoft certifi applic develop 372 | mcdst:= microsoft certifi desktop support technician 373 | mcm:= microsoft certifi master 374 | mcsa:= microsoft certifi system administr 375 | mcsd:= microsoft certifi solut develop 376 | mcse:= microsoft certifi system engin 377 | mct:= microsoft certifi trainer 378 | mda:= model driven architectur 379 | mda:= monochrom display adapt 380 | mdf:= main distribut frame 381 | mdi:= multipl document interfac 382 | mem:=memori 383 | mf:= medium frequenc 384 | mfc:= microsoft foundat class 385 | mfm:= modifi frequenc modul 386 | mgcp:= media gateway control protocol 387 | mhz:= megahertz 388 | my:=my 389 | mib:= manag inform base 390 | micr:= magnet ink charact recognit 391 | midi:= music instrument digit interfac 392 | mimd:= multipl instruct multipl data 393 | mimo:= multipl input multipl output 394 | minix:= mini unix 395 | misd:= multipl instruct singl data 396 | mime:= multipurpos internet mail extens 397 | mmi:= man machin interfac 398 | mmio:= memori map input output 399 | mmorpg:= massiv multiplay onlin role play game 400 | mmu:= memori manag unit 401 | mmx:= multi media extens 402 | mobo:= motherboard 403 | mom:= messag orient middlewar 404 | moo:= mud object orient 405 | mosfet:= metal oxid semiconductor field effect transistor 406 | mpeg:= motion pictur expert group 407 | mt:= machin translat 408 | mta:= mail transfer agent 409 | mta:= microsoft technolog associ 410 | mtbf:= mean time between failur 411 | mtu:= maximum transmiss unit 412 | msa:= mail submiss agent 413 | msi:= medium scale integr 414 | mua:= mail user agent 415 | mud:= multi user dungeon 416 | mvc:= model view control 417 | mx:= mail exchang 418 | mxf:= materi exchang format 419 | nnack:= negat acknowledg 420 | nak:= negat acknowledg charact 421 | nan:= number 422 | nat:= network address translat 423 | ncq:= nativ command queu 424 | nep:= network equip provid 425 | nfa:= nondeterminist finit automaton 426 | ngl:= angel 427 | ngscb:= next generat secur comput base 428 | nlp:= natur languag process 429 | npl:= netscap public licens 430 | npu:= network process unit 431 | ns:= netscap 432 | nmi:= non maskabl interrupt 433 | noc:= network oper center 434 | nop:= oper 435 | nptl:= nativ posix thread librari 436 | ntp:= network time protocol 437 | numa:= non uniform memori access 438 | nvr:= network video record 439 | nvram:= non volatil random access memori 440 | oat:= oper accept test 441 | obsai:= open base station architectur initi 442 | omf:= object modul format 443 | omg:= object manag group 444 | oom:= out memori 445 | oop:= object orient program 446 | opml:= outlin processor markup languag 447 | opts:=option 448 | osdn:= open sourc develop network 449 | osi:= open sourc initi 450 | osi:= open system interconnect 451 | ospf:= open shortest path first 452 | ostg:= open sourc technolog group 453 | oui:= organiz uniqu identifi 454 | pac:= programm autom control 455 | pan:= person area network 456 | pap:= password authent protocol 457 | parc:= palo alto research center 458 | pc:= person comput 459 | pcl:= printer command languag 460 | pcm:= puls code modul 461 | pcre:= perl compat regular express 462 | pdf:= portabl document format 463 | pdp:= program data processor 464 | pe:= physic extent 465 | pgo:= profil guid optim 466 | pgp:= pretti good privaci 467 | php:= php hypertext preprocessor 468 | pid:= process identifi 469 | pio:= program input output 470 | pki:= public key infrastructur 471 | plc:= power line communic 472 | plc:= programm logic control 473 | pld:= programm logic devic 474 | plt:= power line telecom 475 | pmm:= post memori manag 476 | png:= portabl network graphic 477 | pntr:= pointer 478 | poe:= power over ethernet 479 | pos:= posit 480 | ppc:= powerpc 481 | ppi:= pixel per inch 482 | prm:= partner relationship manag 483 | prev:=previous 484 | ps:= postscript 485 | psa:= profession servic autom 486 | psu:= power suppli unit 487 | ptr:=pointer 488 | pvg:= physic volum group 489 | pvr:= person video record 490 | pxe:= preboot execut environ 491 | qa:= qualiti assur 492 | qfp:= quad flat packag 493 | qt:= quasar toolkit 494 | qtam:= queu teleprocess access method 495 | rad:= rapid applic develop 496 | ram:= random access memori 497 | rarp:= revers address resolut protocol 498 | rdf:= resourc descript framework 499 | redir:=redirect 500 | rdm:= relat data model 501 | rf:= radio frequenc 502 | ria:= rich internet applic 503 | rip:= rout inform protocol 504 | rir:= region internet registri 505 | risc:= reduc instruct set comput 506 | rle:= run length encod 507 | rll:= run length limit 508 | rmi:= remot method invoc 509 | rom:= read onli memori 510 | rpc:= remot procedur call 511 | rpg:= report program generat 512 | rpm:= rpm packag manag 513 | rsa:= rivest shamir adleman 514 | rsi:= repetit strain injuri 515 | rss:= radio servic softwar 516 | rtai:= real time applic interfac 517 | rtc:= real time clock 518 | rte:= real time enterpris 519 | rtl:= right left 520 | rtsp:= real time stream protocol 521 | san:= storag area network 522 | sbu:= standard build unit 523 | scada:= supervisori control data acquisit 524 | scid:= sourc code databas 525 | scm:= softwar configur manag 526 | scm:= sourc code manag 527 | scp:= secur copi 528 | scsi:= small comput system interfac 529 | sctp:= stream control transmiss protocol 530 | sd:= secur digit 531 | sddl:= secur descriptor definit languag 532 | sdk:= softwar develop kit 533 | sdl:= simpl directmedia layer 534 | sdn:= servic deliveri network 535 | sdp:= session descript protocol 536 | sdr:= softwar defin radio 537 | sdram:= synchron dynam random access memori 538 | sei:= softwar engin institut 539 | seo:= search engin optim 540 | sec:= second 541 | sha:= secur hash algorithm 542 | simd:= singl instruct multipl data 543 | simm:= singl inlin memori modul 544 | sip:= session initi protocol 545 | sip:= supplementari ideograph plane 546 | sisd:= singl instruct singl data 547 | sled:= suse linux enterpris desktop 548 | sli:= scalabl link interfac 549 | slip:= serial line internet protocol 550 | slm:= servic level manag 551 | sloc:= sourc line code 552 | spmd:= singl program multipl data 553 | smb:= server messag block 554 | smil:= synchron multimedia integr languag 555 | smp:= supplementari multilingu plane 556 | smp:= symmetr multi process 557 | smtp:= simpl mail transfer protocol 558 | sna:= system network architectur 559 | snmp:= simpl network manag protocol 560 | sntp:= simpl network time protocol 561 | soa:= servic orient architectur 562 | soe:= standard oper environ 563 | soap:= simpl object access protocol 564 | spa:= singl page applic 565 | spf:= sender polici framework 566 | sql:= structur queri languag 567 | sram:= static random access memori 568 | src:=source 569 | ssd:= softwar specif document 570 | ssh:= secur shell 571 | ssi:= server side includ 572 | ssi:= singl system imag 573 | ssi:= small scale integr 574 | ssid:= servic set identifi 575 | ssl:= secur socket layer 576 | std:= standard 577 | str:= string 578 | su:= superus 579 | subj:=subject 580 | suse:= softwar und system entwicklung 581 | svc:= scalabl video code 582 | svg:= scalabl vector graphic 583 | svga:= super video graphic array 584 | svd:= structur vlsi design 585 | swf:= shock wave flash 586 | swt:= standard widget toolkit 587 | tb:= terabyt 588 | tcl:= tool command languag 589 | tcp:= transmiss control protocol 590 | tcp:= transmiss control protocol 591 | ip:= internet protocol 592 | tcu:= telecommun control unit 593 | tdma:= time divis multipl access 594 | tft:= thin film transistor 595 | tla:= three letter acronym 596 | tld:= top level domain 597 | tmp:= temporari 598 | tnc:= termin node control 599 | tpf:= transact process facil 600 | trans:= transmitted 601 | tso:= time share option 602 | tsp:= travel salesman problem 603 | tta:= true tap audio 604 | ttl:= transistor transistor logic 605 | ttl:= time live 606 | tug:= tex user group 607 | uac:= user account control 608 | uart:= univers asynchron receiv transmitt 609 | uat:= user accept test 610 | udp:= user datagram protocol 611 | uefi:= unifi extens firmwar interfac 612 | ui:= user interfac 613 | uid:=user identifi 614 | ul:= upload 615 | ula:= uncommit logic array 616 | uma:= upper memori area 617 | umb:= upper memori block 618 | uml:= unifi model languag 619 | uml:= user mode linux 620 | umpc:= ultra mobil person comput 621 | uri:= uniform resourc identifi 622 | url:= uniform resourc locat 623 | urn:= uniform resourc name 624 | usb:= univers serial bus 625 | usr:= user 626 | utf:= unicod transform format 627 | utp:= unshield twist pair 628 | uuid:= univers uniqu identifi 629 | uun:= univers user name 630 | uvc:= univers virtual comput 631 | ux:= user experi 632 | var:= variabl 633 | vax:= virtual address extens 634 | vcpi:= virtual control program interfac 635 | vb:= visual basic 636 | vesa:= video electron standard associ 637 | vfat:= virtual fat 638 | vg:= volum group 639 | vga:= video graphic array 640 | vhf:= veri high frequenc 641 | vlan:= virtual local area network 642 | vlsm:= variabl length subnet mask 643 | vlb:= vesa local bus 644 | vlf:= veri low frequenc 645 | vliw:= veri long instruct word 646 | vlsi:= veri larg scale integr 647 | vnc:= virtual network comput 648 | voip:= voic over internet protocol 649 | vpn:= virtual privat network 650 | vpu:= visual process unit 651 | vsam:= virtual storag access method 652 | vsat:= veri small apertur termin 653 | vtam:= virtual telecommun access method 654 | wan:= wide area network 655 | wap:= wireless access point 656 | wap:= wireless applic protocol 657 | wbem:= web base enterpris manag 658 | wcag:= web content access guidelin 659 | wcf:= window communic foundat 660 | wdm:= wavelength divis multiplex 661 | wep:= wire equival privaci 662 | wid:=window identifi 663 | wlan:= wireless local area network 664 | wma:= window media audio 665 | wmv:= window media video 666 | wpan:= wireless person area network 667 | wsdl:= web servic descript languag 668 | wsfl:= web servic flow languag 669 | wusb:= wireless univers serial bus 670 | wwan:= wireless wide area network 671 | wwid:= world wide identifi 672 | wwn:= world wide name 673 | www:= world wide web 674 | wzc:= wireless zero configur 675 | xml:= extens markup languag 676 | xsl:= extens stylesheet languag 677 | xslt:= extens stylesheet languag transform 678 | xss:= cross site script 679 | xtf:= extens tag framework 680 | xtf:= extend triton format 681 | zif:= zero insert forc 682 | zisc:= zero instruct set comput 683 | zope:= object publish environ 684 | zma:= zone multicast address 685 | -------------------------------------------------------------------------------- /data/distributions_per_rate_tfidf/CO_Jhotdraw_(7.4.1)_760.txt: -------------------------------------------------------------------------------- 1 | 3.433526982965285357e-01 2 | 2.989896609897275681e-01 3 | 5.412044310765424804e-01 4 | 6.277290654703086004e-01 5 | 5.065321314654911422e-01 6 | 3.433526982965285357e-01 7 | 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