├── .gitignore ├── requirements.txt ├── cd4py ├── __init__.py ├── __main__.py ├── pytokenizer.py └── lib.py ├── CHANGELOG.md ├── setup.py ├── README.md └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | .idea/ 2 | __pycache__ 3 | *.pyc 4 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | dpu-utils 2 | tqdm 3 | joblib 4 | pandas 5 | scikit-learn 6 | numpy 7 | annoy -------------------------------------------------------------------------------- /cd4py/__init__.py: -------------------------------------------------------------------------------- 1 | # License: GNU General Public License v3.0 2 | 3 | __version__ = "0.1.0" 4 | 5 | def log_step(step_msg: str): 6 | no_astrisks = 50 7 | 8 | if len(step_msg) % 2: 9 | l = len(step_msg) // 2 10 | r = l 11 | else: 12 | l = len(step_msg) // 2 13 | r = l + 1 14 | 15 | print("*" * (no_astrisks - l) + step_msg + "*" * (no_astrisks - r)) 16 | 17 | 18 | def dummy_preprocessor(doc): 19 | return doc 20 | -------------------------------------------------------------------------------- /CHANGELOG.md: -------------------------------------------------------------------------------- 1 | # Changelog 2 | All notable changes to CD4Py tool will be documented in this file. The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html). 3 | 4 | ## [Unreleased] 5 | 6 | ## [0.1.0] - 2020-09-11 7 | 8 | ### Added 9 | - A parallel tokenizer for Python source code files. 10 | - A library module for pre-processing tokenized files, calculating TF-IDF, finding KNNs, and identifying duplicate files. 11 | - A command-line interface for detection of duplicate files in Python projects. 12 | 13 | 14 | -------------------------------------------------------------------------------- /cd4py/__main__.py: -------------------------------------------------------------------------------- 1 | # License: GNU General Public License v3.0 2 | 3 | from argparse import ArgumentParser 4 | from cd4py.lib import deduplicate_py_data 5 | 6 | 7 | def main(): 8 | 9 | arg_parser = ArgumentParser(description="Code De-Duplication for Python") 10 | arg_parser.add_argument("--p", required=True, type=str, help="Path to Python projects") 11 | arg_parser.add_argument("--od", required=True, type=str, help="Output folder to store detected duplicate files.") 12 | arg_parser.add_argument("--ot", required=True, type=str, help="Output folder to store tokenized files.") 13 | arg_parser.add_argument("--d", default=2048, type=int, help="Dimension of TF-IDF vectors [default: 2048].") 14 | arg_parser.add_argument("--th", default=0.95, type=float, 15 | help="Threshold to identify duplicate files [default: 0.95].") 16 | arg_parser.add_argument("--k", default=10, type=int, help="Number of nearest neighbor [default: 10].") 17 | arg_parser.add_argument("--tr", default=20, type=int, 18 | help="Number trees to build the index. More trees gives higher precision but" 19 | " slower [default: 20].") 20 | 21 | args = arg_parser.parse_args() 22 | 23 | deduplicate_py_data(args.p, args.ot, args.od, args.d, args.th, args.k, args.tr) 24 | 25 | 26 | if __name__ == '__main__': 27 | 28 | main() 29 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | # License: GNU General Public License v3.0 2 | 3 | from setuptools import setup 4 | from os import path 5 | from cd4py import __version__ 6 | 7 | here = path.abspath(path.dirname(__file__)) 8 | 9 | with open(path.join(here, 'README.md'), encoding='utf-8') as f: 10 | long_description = f.read() 11 | 12 | setup( 13 | name='cd4py', 14 | version=__version__, 15 | description='CD4Py: Code De-Duplication for Python', 16 | long_description=long_description, 17 | long_description_content_type='text/markdown', 18 | url='https://github.com/saltudelft/CD4Py', 19 | author='Amir M. Mir (TU Delft)', 20 | author_email='mir-am@hotmail.com', 21 | classifiers=[ 22 | 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)', 23 | 'Environment :: Console', 24 | 'Programming Language :: Python', 25 | 'Programming Language :: Python :: 3.5', 26 | 'Programming Language :: Python :: 3.6', 27 | 'Programming Language :: Python :: 3.7', 28 | 'Operating System :: Unix', 29 | 'Operating System :: MacOS', 30 | 'Operating System :: POSIX', 31 | 'Intended Audience :: Science/Research', 32 | 'Topic :: Scientific/Engineering :: Artificial Intelligence', 33 | 'Topic :: Software Development :: Libraries :: Python Modules' 34 | ], 35 | keywords='python source code de-duplication detection cd4py', 36 | packages=['cd4py'], 37 | python_requries='>=3.5', 38 | install_requires=['dpu-utils', 'tqdm', 'joblib', 'pandas', 'scikit-learn', 'numpy', 'annoy'], 39 | entry_points={ 40 | 'console_scripts': [ 41 | 'cd4py = cd4py.__main__:main', 42 | ], 43 | } 44 | ) 45 | -------------------------------------------------------------------------------- /cd4py/pytokenizer.py: -------------------------------------------------------------------------------- 1 | # This project is licensed under the terms of GNU GPLv3, with the exception of this file which is 2 | # licensed under the MIT license. This file's helper functions are taken from the project near-duplicate-code-detector 3 | # by Microsoft: https://github.com/microsoft/near-duplicate-code-detector/ and they are modified for this project. 4 | 5 | from tokenize import tokenize, NAME, STRING 6 | from typing import Iterator 7 | from dpu_utils.utils import save_jsonl_gz 8 | from joblib import Parallel, delayed 9 | from tqdm import tqdm 10 | import keyword 11 | import os 12 | import glob 13 | 14 | 15 | def tokenize_file(filepath: str, all_tokens: bool=False) -> Iterator[str]: 16 | tokens = [] 17 | try: 18 | with open(filepath, 'rb') as f: 19 | for toknum, tokval, _, _, _ in tokenize(f.readline): 20 | if all_tokens or toknum in {NAME, STRING}: 21 | if not keyword.iskeyword(tokval): 22 | tokens.append(tokval) 23 | except Exception as e: 24 | print('Error tokenizing %s because %s' % (filepath, e)) 25 | return dict(filename=filepath, tokens=tokens) 26 | 27 | 28 | def tokenize_all_files(directory: str, output_folder: str, only_ids: bool=False): 29 | #print('Tokenizing in folder %s.' % directory) 30 | 31 | def all_file_tokenizer(): 32 | for file in glob.iglob(os.path.join(directory, '**', '*.py'), recursive=True): 33 | if os.path.isdir(file): continue 34 | yield tokenize_file(file, only_ids) 35 | 36 | directory_name = os.path.basename(directory) 37 | save_jsonl_gz(all_file_tokenizer(), os.path.join(output_folder, directory_name + '-tokens.jsonl.gz')) 38 | 39 | 40 | def tokenize_all_project_folders(directory: str, output_folder: str, n_jobs: int=-1, only_ids: bool=False): 41 | os.makedirs(output_folder, exist_ok=True) 42 | all_dirs = [(os.path.join(directory, d), output_folder, only_ids) for d in os.listdir(directory) if os.path.isdir(os.path.join(directory, d))] 43 | Parallel(n_jobs)(delayed(tokenize_all_files)(*d) for d in tqdm(all_dirs, total=len(all_dirs))) 44 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Intro 2 | [![PyPI version](https://badge.fury.io/py/cd4py.svg)](https://badge.fury.io/py/cd4py) 3 | 4 | **CD4Py** is a code de-duplication tool for Python programming language. 5 | It detects **near** and **exact** duplicate source code files. To train a machine learning model on source code files, 6 | it is essential to identify and remove duplicate source code files from the dataset. Otherwise, code duplication 7 | significantly affects the practicality of machine learning-based tools, especially on unseen data. 8 | 9 | - [Quick Installation](#quick-installation) 10 | - [Usage](#usage) 11 | - [Examples](#examples) 12 | - [Approach](#approach) 13 | - [TODOs](#todos) 14 | 15 | # Quick Installation 16 | ``` 17 | $ pip install cd4py 18 | ``` 19 | 20 | # Usage 21 | ``` 22 | $ cd4py --help 23 | usage: cd4py [-h] --p P --od OD --ot OT [--d D] [--th TH] [--k K] [--tr TR] 24 | 25 | Code De-Duplication for Python 26 | 27 | optional arguments: 28 | -h, --help show this help message and exit 29 | --p P Path to Python projects 30 | --od OD Output folder to store detected duplicate files. 31 | --ot OT Output folder to store tokenized files. 32 | --d D Dimension of TF-IDF vectors [default: 2048]. 33 | --th TH Threshold to identify duplicate files [default: 0.95]. 34 | --k K Number of nearest neighbor [default: 10]. 35 | --tr TR Number trees to build the index. More trees gives higher 36 | precision but slower [default: 20]. 37 | ``` 38 | 39 | ## Examples 40 | - Run `CD4Py` to identify duplicate files for a Python dataset 41 | ``` 42 | $ cd4py --p $PYHON_DATASET --ot $TOKENS --od py_dataset_duplicates.jsonl.gz --d 1024 43 | ``` 44 | Replace `$PYHON_DATASET` with the path to the Python project folders and `$TOKENS` with the path to store 45 | tokenized project files. Also, note that detected duplicate files will be stored in 46 | the file `py_dataset_duplicates.jsonl.gz`. 47 | 48 | - The following code example shows the removal of duplicate files using the example file `py_dataset_duplicates.jsonl.gz`: 49 | ```python 50 | from dpu_utils.utils.dataloading import load_jsonl_gz 51 | import random 52 | # Selects randomly a file from each cluster of duplicate files 53 | clusters_rand_files = [l.pop(random.randrange(len(l))) for l in load_jsonl_gz('py_dataset_duplicates.jsonl.gz')] 54 | duplicate_files = [f for l in load_jsonl_gz('py_dataset_duplicates.jsonl.gz') for f in l] 55 | duplicate_files = set(duplicate_files).difference(set(clusters_rand_files)) 56 | ``` 57 | 58 | # Approach 59 | The `CD4Py` code de-duplication tool uses the following procedure to identify duplicate files in a Python code corpus: 60 | 61 | 1. Tokenize all the source code files in the code corpus using `tokenize` module of Python standard library. 62 | 2. Preprocess tokenized source files by only selecting identifier tokens and removing language keywords. 63 | 3. Convert pre-processed tokenized files to a vector representation using the TF-IDF method. 64 | 4. Perform `k`-nearest neighbor search to find `k` candidate duplicate files for each source code file. 65 | Next, filter out candidate duplicate files by considering the threshold `t`. 66 | 5. Find clusters of duplicate source code files while assuming that similarity is transitive. 67 | 68 | # TODOs 69 | To implement the following TODOs, external collaborations are very welcome. 70 | 71 | - Better logging for showing infos and errors using Python's `logging` module. 72 | - Unit tests for testing the tool's functionalities. `pytest` can be used. 73 | - Batch by files instead of directories in tokenization (to equalize workload for parallel tasks) 74 | -------------------------------------------------------------------------------- /cd4py/lib.py: -------------------------------------------------------------------------------- 1 | # License: GNU General Public License v3.0 2 | 3 | from cd4py import log_step, dummy_preprocessor 4 | from cd4py.pytokenizer import tokenize_all_project_folders 5 | from typing import List, Tuple, Dict, Set 6 | from dpu_utils.utils.dataloading import load_jsonl_gz, save_jsonl_gz 7 | from dpu_utils.codeutils import get_language_keywords, split_identifier_into_parts 8 | from sklearn.feature_extraction.text import TfidfVectorizer 9 | from tqdm import tqdm 10 | import os 11 | import re 12 | import time 13 | import pandas as pd 14 | import numpy as np 15 | import annoy 16 | 17 | # Files with fewer than the specified number of identifier tokens will be excluded 18 | NO_IDENTIFIER_TOKENS = 20 19 | IDENTIFIER_REGEX = re.compile('[_a-zA-Z][_a-zA-Z0-9]*') 20 | 21 | 22 | def get_tokenized_py_files(tokenized_files_path: str) -> List[Tuple[str, List[str]]]: 23 | """ 24 | Gathers all the tokenized Python source code files from a directory 25 | """ 26 | 27 | tokenized_files = os.listdir(tokenized_files_path) 28 | all_tokenized_py_files: List[Tuple[str, List[str]]] = [] 29 | 30 | for f in tqdm(tokenized_files, total=len(tokenized_files)): 31 | if f.endswith(".jsonl.gz"): 32 | for d in load_jsonl_gz(os.path.join(tokenized_files_path, f)): 33 | if len(d['tokens']) != 0: 34 | all_tokenized_py_files.append((d['filename'], d['tokens'])) 35 | # else: 36 | # print("Found a file without tokens: ", d['filename']) 37 | return all_tokenized_py_files 38 | 39 | 40 | def preprocess_tokenized_files(tokenized_py_files: List[Tuple[str, List[str]]]) -> Tuple[pd.DataFrame, 41 | List[List[str]]]: 42 | """ 43 | Applies the following preprocess steps to tokenized source code files: 44 | 1- Excludes source code files that have fewer identifiers tokens than the specified number 45 | 2- Selects only identifier tokens 46 | 3- Removes language keywords 47 | """ 48 | 49 | df_tokenized_files = pd.DataFrame(tokenized_py_files, columns=['filename', 'tokens']) 50 | df_tokenized_files = df_tokenized_files[df_tokenized_files['tokens'].map(lambda x: True if len(x) > \ 51 | NO_IDENTIFIER_TOKENS else False)].copy() 52 | print(f"Number of source code files: {df_tokenized_files.shape[0]:,}") 53 | print(f"Total number of tokens: {sum(df_tokenized_files['tokens'].apply(lambda x: len(x))):,}") 54 | 55 | all_tokens = df_tokenized_files['tokens'].tolist() 56 | # Select only identifiers and remove language keywords 57 | all_tokens = [[t for t in f_tks if IDENTIFIER_REGEX.match(t) and t not in \ 58 | get_language_keywords('python')] for f_tks in tqdm(all_tokens, total=len(all_tokens))] 59 | all_tokens = [[i for t in src for i in split_identifier_into_parts(t)] for src in tqdm(all_tokens, 60 | total=len(all_tokens))] 61 | return df_tokenized_files, all_tokens 62 | 63 | 64 | def vectorize_tokenized_files(preprocessed_file_tokens: List[List[str]], dim_tfidf_vec: int) -> np.array: 65 | """ 66 | Vectorize all the preprocessed source code files using TF-IDF approach 67 | """ 68 | 69 | tfidf = TfidfVectorizer(analyzer='word', tokenizer=dummy_preprocessor, preprocessor=dummy_preprocessor, 70 | token_pattern=None, max_features=dim_tfidf_vec) 71 | tfidf.fit(preprocessed_file_tokens) 72 | all_vectorized_py_files = np.float32(np.vstack([tfidf.transform([d]).toarray() for d in \ 73 | tqdm(preprocessed_file_tokens, total=len(preprocessed_file_tokens))])) 74 | 75 | return all_vectorized_py_files 76 | 77 | 78 | def build_knn_index(vectorized_files: np.array, vec_dim: int, knn_tree_size: int=20) -> annoy.AnnoyIndex: 79 | """ 80 | Builds an index trees for finding nearest neighbors 81 | """ 82 | 83 | annoy_idx = annoy.AnnoyIndex(vec_dim, 'dot') 84 | for i, v in enumerate(tqdm(vectorized_files, total=vectorized_files.shape[0])): 85 | annoy_idx.add_item(i, v) 86 | 87 | annoy_idx.build(knn_tree_size) 88 | return annoy_idx 89 | 90 | 91 | def find_knn(vectorized_files, knn_index: annoy.AnnoyIndex, k: int) -> Tuple[List[List[int]], List[List[float]]]: 92 | """ 93 | Find k-nearest neighbors of vectorized source code files for identifying duplicate files 94 | """ 95 | 96 | knn_idx, knn_dist = [], [] 97 | for v in tqdm(vectorized_files, total=vectorized_files.shape[0]): 98 | idx, dist = knn_index.get_nns_by_vector(v, k, include_distances=True) 99 | knn_idx.append(idx) 100 | knn_dist.append(dist) 101 | 102 | return knn_idx, knn_dist 103 | 104 | 105 | def find_duplicate_sets(df_tokenized_files: pd.DataFrame, t: int, k: int, files_knn_idx: List[List[int]], 106 | files_knn_dist: List[List[float]]) -> Dict[str, List[Tuple[str, float]]]: 107 | """ 108 | Finds duplicate sets of files for each source code file 109 | """ 110 | 111 | clone_sets = {} 112 | for i in tqdm(range(df_tokenized_files.shape[0])): 113 | clone_sets[df_tokenized_files['filename'].iloc[i]] = [] 114 | for j in range(1, k): 115 | # print(i,j) 116 | if files_knn_dist[i][j] > t: 117 | if df_tokenized_files['filename'].iloc[i] != df_tokenized_files['filename'].iloc[files_knn_idx[i][j]]: 118 | clone_sets[df_tokenized_files['filename'].iloc[i]].append( 119 | (df_tokenized_files['filename'].iloc[files_knn_idx[i][j]], 120 | files_knn_dist[i][j])) 121 | # print(f"Found a duplicate {df_tokenized_files['filename'].iloc[i]} -> ({df_tokenized_files['filename'].iloc[I[i][j]]}, {D[i][j]})") 122 | else: 123 | break 124 | 125 | return clone_sets 126 | 127 | 128 | def find_transitive_duplicate_sets(duplicate_files_set: Dict[str, List[Tuple[str, float]]]) -> Tuple[List[Set[str]], 129 | List[str]]: 130 | """ 131 | Finds clusters of near or exact duplicate files based on the assumption that similarity is transitive. 132 | """ 133 | 134 | duplicate_files_closure = [] 135 | files_clone_idx = {} 136 | # Compute the transitive closure of this relationship 137 | documents_to_visit = set(duplicate_files_set.keys()) 138 | while len(documents_to_visit) > 0: 139 | current_idx = documents_to_visit.pop() 140 | current_idx_closure = {current_idx} 141 | visit_queue = [] 142 | for f in duplicate_files_set[current_idx].copy(): 143 | if f[0] in files_clone_idx: 144 | duplicate_files_closure[files_clone_idx[f[0]]].add(current_idx) 145 | files_clone_idx[current_idx] = files_clone_idx[f[0]] 146 | visit_queue = [] 147 | break 148 | else: 149 | visit_queue.append(f[0]) 150 | 151 | if len(visit_queue) != 0: 152 | while len(visit_queue) > 0: 153 | other_idx = visit_queue.pop() 154 | current_idx_closure.add(other_idx) 155 | documents_to_visit.discard(other_idx) 156 | 157 | # Add to queue 158 | visit_queue.extend(next_idx[0] for next_idx in duplicate_files_set[other_idx] 159 | if next_idx[0] in documents_to_visit) 160 | 161 | duplicate_files_closure.append(set(f for f in current_idx_closure)) 162 | for f in current_idx_closure: 163 | files_clone_idx[f] = len(duplicate_files_closure) - 1 164 | 165 | else: 166 | continue 167 | 168 | return duplicate_files_closure, [f for c in duplicate_files_closure for f in c] 169 | 170 | 171 | def report_duplicate_files_stats(no_src_files: int, no_duplicate_files: int, 172 | duplicate_files_set_closure: List[Set[str]]): 173 | 174 | print(f"Number of duplicated files: {no_duplicate_files:,} ({no_duplicate_files / no_src_files * 100.0:.2f}%)") 175 | print(f"Number of detected clusters: {len(duplicate_files_set_closure):,}") 176 | print("Avg. number of files per clones: %.2f" % np.mean([len(c) for c in duplicate_files_set_closure])) 177 | print("Median number of files per clones: %.2f" % np.median([len(c) for c in duplicate_files_set_closure])) 178 | print("Duplication ratio: %.2f%%" % ( 179 | (no_duplicate_files - len(duplicate_files_set_closure)) / no_src_files * 100.0)) 180 | 181 | 182 | def deduplicate_py_data(py_projects_path: str, tokenized_files_path: str, detected_duplicate_f_path: str, 183 | dim_tfidf_vec: int, t: int, no_knn: int, knn_tree_size: int): 184 | """ 185 | Identifies near or exact duplicate files in a Python corpus. 186 | 187 | :param py_projects_path: Path to the Python project files 188 | :param tokenized_files_path: Path to store tokenized files 189 | :param detected_duplicate_f_path: Path to store detected duplicate files 190 | :param dim_tfidf_vec: Dimension of vectorized files 191 | :param t: Threshold to identify a file as duplicate 192 | :param no_knn: Number of nearest neighbors to find when performing KNN search 193 | :param knn_tree_size: Size of trees when building KNN index. Higher number gives more precision but slower. 194 | """ 195 | 196 | start_t = time.time() 197 | 198 | log_step("Tokenizing Python source code files") 199 | tokenize_all_project_folders(py_projects_path, tokenized_files_path) 200 | 201 | log_step("Loading all the tokenized Python source code files") 202 | all_tokenized_py_files = get_tokenized_py_files(tokenized_files_path) 203 | 204 | log_step("Preprocessing tokenized files") 205 | df_tokenized_files, all_preprocessed_tokens = preprocess_tokenized_files(all_tokenized_py_files) 206 | 207 | log_step("Vectorize pre-processed source code files using TF-IDF") 208 | vectorized_files = vectorize_tokenized_files(all_preprocessed_tokens, dim_tfidf_vec) 209 | 210 | log_step("Building KNN index and finding nearest neighbors") 211 | knn_index = build_knn_index(vectorized_files, dim_tfidf_vec, knn_tree_size) 212 | files_knn_idx, files_knn_dist = find_knn(vectorized_files, knn_index, no_knn) 213 | 214 | log_step("Finding exact and near duplicate files") 215 | duplicate_files_set = find_duplicate_sets(df_tokenized_files, t, no_knn, files_knn_idx, files_knn_dist) 216 | duplicate_files_set_closure, duplicate_files = find_transitive_duplicate_sets(duplicate_files_set) 217 | # A sanity check to make sure that there is no intersection between the clusters of duplicate files 218 | assert len(duplicate_files) == sum(len(c) for c in duplicate_files_set_closure) 219 | 220 | log_step("Report duplication stats & saving detected duplicate files") 221 | report_duplicate_files_stats(df_tokenized_files.shape[0], len(duplicate_files), duplicate_files_set_closure) 222 | save_jsonl_gz([list(c) for c in duplicate_files_set_closure], detected_duplicate_f_path) 223 | 224 | print("Finished duplicate files detection in %.2f minutes." % ((time.time() - start_t) / 60.0)) 225 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . --------------------------------------------------------------------------------