| --- |
| annotations_creators: |
| - found |
| language_creators: |
| - found |
| language: |
| - code |
| license: |
| - c-uda |
| multilinguality: |
| - monolingual |
| size_categories: |
| - 1K<n<10K |
| - n<1K |
| source_datasets: |
| - original |
| task_categories: |
| - text-generation |
| - fill-mask |
| task_ids: |
| - slot-filling |
| pretty_name: CodeXGlueCcCodeCompletionLine |
| dataset_info: |
| - config_name: java |
| features: |
| - name: id |
| dtype: int32 |
| - name: input |
| dtype: string |
| - name: gt |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 5454783 |
| num_examples: 3000 |
| download_size: 5523586 |
| dataset_size: 5454783 |
| - config_name: python |
| features: |
| - name: id |
| dtype: int32 |
| - name: input |
| dtype: string |
| - name: gt |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 24021562 |
| num_examples: 10000 |
| download_size: 24266715 |
| dataset_size: 24021562 |
| config_names: |
| - go |
| - java |
| - javascript |
| - php |
| - python |
| - ruby |
| --- |
| # Dataset Card for "code_x_glue_cc_code_completion_line" |
|
|
| ## Table of Contents |
| - [Dataset Description](#dataset-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Supported Tasks and Leaderboards](#supported-tasks) |
| - [Languages](#languages) |
| - [Dataset Structure](#dataset-structure) |
| - [Data Instances](#data-instances) |
| - [Data Fields](#data-fields) |
| - [Data Splits](#data-splits-sample-size) |
| - [Dataset Creation](#dataset-creation) |
| - [Curation Rationale](#curation-rationale) |
| - [Source Data](#source-data) |
| - [Annotations](#annotations) |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) |
| - [Considerations for Using the Data](#considerations-for-using-the-data) |
| - [Social Impact of Dataset](#social-impact-of-dataset) |
| - [Discussion of Biases](#discussion-of-biases) |
| - [Other Known Limitations](#other-known-limitations) |
| - [Additional Information](#additional-information) |
| - [Dataset Curators](#dataset-curators) |
| - [Licensing Information](#licensing-information) |
| - [Citation Information](#citation-information) |
| - [Contributions](#contributions) |
|
|
| ## Dataset Description |
|
|
| - **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-line |
|
|
| ### Dataset Summary |
|
|
| CodeXGLUE CodeCompletion-line dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-line |
|
|
| Complete the unfinished line given previous context. Models are evaluated by exact match and edit similarity. |
| We propose line completion task to test model's ability to autocomplete a line. Majority code completion systems behave well in token level completion, but fail in completing an unfinished line like a method call with specific parameters, a function signature, a loop condition, a variable definition and so on. When a software develop finish one or more tokens of the current line, the line level completion model is expected to generate the entire line of syntactically correct code. |
| Line level code completion task shares the train/dev dataset with token level completion. After training a model on CodeCompletion-token, you could directly use it to test on line-level completion. |
|
|
| ### Supported Tasks and Leaderboards |
|
|
| - `slot-filling`: The dataset can be used to train a model for completing entire code lines. |
|
|
| ### Languages |
|
|
| - Java **programming** language |
| - Python **programming** language |
|
|
| ## Dataset Structure |
|
|
| ### Data Instances |
|
|
| #### java |
|
|
| An example of 'train' looks as follows. |
| ``` |
| { |
| "gt": "", |
| "id": 0, |
| "input": "<s> package org . rubypeople . rdt . internal . ui . rubyeditor ; import java . util . Iterator ; import org . eclipse . core . resources . IMarker ; import org . eclipse . ui . texteditor . MarkerAnnotation ; import org . eclipse . ui . texteditor . MarkerUtilities ; import org . rubypeople . rdt . core . IRubyElement ; import org . rubypeople . rdt . core . IRubyModelMarker ; import org . rubypeople . rdt . core . IRubyScript ; import org . rubypeople . rdt . core . RubyCore ; public class RubyMarkerAnnotation extends MarkerAnnotation implements IRubyAnnotation { public static final String RUBY_MARKER_TYPE_PREFIX = \"\" ; public static final String ERROR_ANNOTATION_TYPE = \"\" ; public static final String WARNING_ANNOTATION_TYPE = \"\" ; public static final String INFO_ANNOTATION_TYPE = \"\" ; public static final String TASK_ANNOTATION_TYPE = \"\" ; private IRubyAnnotation fOverlay ; public RubyMarkerAnnotation ( IMarker marker ) { super ( marker ) ; } public String [ ] getArguments ( ) { return null ; } public int getId ( ) { IMarker marker = getMarker ( ) ; if ( marker == null || ! marker . exists ( ) ) return - 1 ; if ( isProblem ( ) ) return marker . getAttribute ( IRubyModelMarker . ID , - 1 ) ; return - 1 ; } public boolean isProblem ( ) { String type = getType ( ) ; return WARNING_ANNOTATION_TYPE . equals ( type ) || ERROR_ANNOTATION_TYPE . equals" |
| } |
| ``` |
|
|
| #### python |
|
|
| An example of 'train' looks as follows. |
| ``` |
| { |
| "gt": "", |
| "id": 0, |
| "input": "<s> from __future__ import absolute_import <EOL> import weakref <EOL> import operator <EOL> from . compat import threading , itertools_filterfalse <EOL> from . import py2k <EOL> import types <EOL> EMPTY_SET = frozenset ( ) <EOL> class KeyedTuple ( tuple ) : <EOL> def __new__ ( cls , vals , labels = None ) : <EOL> t = tuple . __new__ ( cls , vals ) <EOL> t . _labels = [ ] <EOL> if labels : <EOL> t . __dict__ . update ( zip ( labels , vals ) ) <EOL> t . _labels = labels <EOL> return t <EOL> def keys ( self ) : <EOL> return [ l for l in self . _labels if l is not None ] <EOL> @ property <EOL> def _fields ( self ) : <EOL> return tuple ( self . keys ( ) ) <EOL> def _asdict ( self ) : <EOL> return dict ( ( key , self . __dict__ [ key ] ) for key in self . keys ( ) ) <EOL> class ImmutableContainer ( object ) : <EOL> def _immutable ( self , * arg , ** kw ) : <EOL> raise TypeError ( \"\" % self . __class__ . __name__ ) <EOL> __delitem__ = __setitem__ = __setattr__ = _immutable <EOL> class immutabledict ( ImmutableContainer , dict ) : <EOL> clear = pop = popitem = setdefault = update = ImmutableContainer . _immutable <EOL> def __new__ ( cls , * args ) : <EOL> new = dict . __new__ ( cls ) <EOL> dict . __init__ ( new , * args ) <EOL> return new <EOL> def __init__ ( self , * args ) : <EOL> pass <EOL> def __reduce__ ( self ) : <EOL> return immutabledict , ( dict ( self ) , ) <EOL> def union ( self , d ) : <EOL> if not self : <EOL> return immutabledict ( d ) <EOL> else : <EOL> d2 = immutabledict ( self ) <EOL> dict . update ( d2 , d ) <EOL> return d2 <EOL> def __repr__ ( self ) : <EOL> return \"\" % dict . __repr__ ( self ) <EOL> class Properties ( object ) : <EOL> def __init__ ( self , data ) : <EOL> self . __dict__ [ '_data' ] = data <EOL> def __len__ ( self ) : <EOL> return len ( self . _data ) <EOL> def __iter__ ( self ) : <EOL> return iter ( list ( self . _data . values ( ) ) ) <EOL> def __add__ ( self , other ) : <EOL> return list ( self ) + list ( other ) <EOL> def __setitem__ ( self , key , object ) : <EOL> self . _data [ key ] = object <EOL> def __getitem__ ( self , key ) : <EOL> return self . _data [ key ] <EOL> def __delitem__ ( self , key ) : <EOL> del self . _data [ key ] <EOL> def __setattr__ ( self , key , object ) : <EOL> self . _data [ key ] = object <EOL> def __getstate__ ( self ) : <EOL> return { '_data' : self . __dict__ [ '_data' ] } <EOL> def __setstate__ ( self , state ) : <EOL> self . __dict__ [ '_data' ] = state [ '_data' ] <EOL> def __getattr__ ( self , key ) : <EOL> try : <EOL> return self . _data [ key ] <EOL> except KeyError : <EOL> raise AttributeError ( key ) <EOL> def __contains__ ( self , key ) : <EOL> return key in self . _data <EOL> def as_immutable ( self ) : <EOL> return ImmutableProperties ( self . _data ) <EOL> def update ( self , value ) : <EOL> self . _data . update ( value ) <EOL> def get ( self , key , default = None ) : <EOL> if key in self : <EOL> return self [ key ] <EOL> else : <EOL> return default <EOL> def keys ( self ) : <EOL> return list ( self . _data ) <EOL> def values ( self ) : <EOL> return list ( self . _data . values ( ) ) <EOL> def items ( self ) : <EOL> return list ( self . _data . items ( ) ) <EOL> def has_key ( self , key ) : <EOL> return key in self . _data <EOL> def clear ( self ) : <EOL> self . _data . clear ( ) <EOL> class OrderedProperties ( Properties ) : <EOL> def __init__ ( self ) : <EOL> Properties . __init__ ( self , OrderedDict ( ) ) <EOL> class ImmutableProperties ( ImmutableContainer , Properties ) : <EOL> class OrderedDict ( dict ) : <EOL> def __init__ ( self , ____sequence = None , ** kwargs ) : <EOL> self . _list = [ ] <EOL> if ____sequence is None : <EOL> if kwargs : <EOL> self . update ( ** kwargs ) <EOL> else : <EOL> self . update ( ____sequence , ** kwargs ) <EOL> def clear ( self ) : <EOL> self . _list = [ ] <EOL> dict . clear ( self ) <EOL> def copy ( self ) : <EOL> return self . __copy__ ( ) <EOL> def __copy__ ( self ) : <EOL> return OrderedDict ( self ) <EOL> def sort ( self , * arg , ** kw ) : <EOL> self . _list . sort ( * arg , ** kw ) <EOL> def update ( self , ____sequence = None , ** kwargs ) : <EOL> if ____sequence is not None : <EOL> if hasattr ( ____sequence , 'keys' ) : <EOL> for key in ____sequence . keys ( ) : <EOL> self . __setitem__ ( key , ____sequence [ key ] ) <EOL> else : <EOL> for key , value in ____sequence : <EOL> self [ key ] = value <EOL> if kwargs : <EOL> self . update ( kwargs ) <EOL> def setdefault ( self , key , value ) : <EOL> if key not in self : <EOL> self . __setitem__ ( key , value ) <EOL> return value <EOL> else : <EOL> return self . __getitem__ ( key ) <EOL> def __iter__ ( self ) : <EOL> return iter ( self . _list ) <EOL> def keys ( self ) : <EOL> return list ( self ) <EOL> def values ( self ) : <EOL> return [ self [ key ] for key in self . _list ] <EOL> def items ( self ) : <EOL> return [ ( key , self [ key ] ) for key in self . _list ] <EOL> if py2k : <EOL> def itervalues ( self ) : <EOL> return iter ( self . values ( ) ) <EOL> def iterkeys ( self ) : <EOL> return iter ( self ) <EOL> def iteritems ( self ) : <EOL> return iter ( self . items ( ) ) <EOL> def __setitem__ ( self , key , object ) : <EOL> if key not in self : <EOL> try : <EOL> self . _list . append ( key ) <EOL> except AttributeError : <EOL> self . _list = [ key ] <EOL> dict . __setitem__ ( self , key , object ) <EOL> def __delitem__ ( self , key ) : <EOL> dict . __delitem__ ( self , key ) <EOL> self . _list . remove ( key ) <EOL> def pop ( self , key , * default ) : <EOL> present = key in self <EOL> value = dict . pop ( self , key , * default ) <EOL> if present : <EOL> self . _list . remove ( key ) <EOL> return value <EOL> def popitem ( self ) : <EOL> item = dict . popitem ( self ) <EOL> self . _list . remove ( item [ 0 ] ) <EOL> return item <EOL> class OrderedSet ( set ) : <EOL> def __init__ ( self , d = None ) : <EOL> set . __init__ ( self ) <EOL> self . _list = [ ] <EOL> if d is not None : <EOL>" |
| } |
| ``` |
|
|
| ### Data Fields |
|
|
| In the following each data field in go is explained for each config. The data fields are the same among all splits. |
|
|
| #### java, python |
|
|
| |field name| type | description | |
| |----------|------|----------------------------| |
| |id |int32 | Index of the sample | |
| |input |string| Input code string | |
| |gt |string| Code string to be predicted| |
|
|
| ### Data Splits |
|
|
| | name |train| |
| |------|----:| |
| |java | 3000| |
| |python|10000| |
|
|
| ## Dataset Creation |
|
|
| ### Curation Rationale |
|
|
| [More Information Needed] |
|
|
| ### Source Data |
|
|
| #### Initial Data Collection and Normalization |
|
|
| [More Information Needed] |
|
|
| #### Who are the source language producers? |
|
|
| [More Information Needed] |
|
|
| ### Annotations |
|
|
| #### Annotation process |
|
|
| [More Information Needed] |
|
|
| #### Who are the annotators? |
|
|
| [More Information Needed] |
|
|
| ### Personal and Sensitive Information |
|
|
| [More Information Needed] |
|
|
| ## Considerations for Using the Data |
|
|
| ### Social Impact of Dataset |
|
|
| [More Information Needed] |
|
|
| ### Discussion of Biases |
|
|
| [More Information Needed] |
|
|
| ### Other Known Limitations |
|
|
| [More Information Needed] |
|
|
| ## Additional Information |
|
|
| ### Dataset Curators |
|
|
| https://github.com/microsoft, https://github.com/madlag |
|
|
| ### Licensing Information |
|
|
| Computational Use of Data Agreement (C-UDA) License. |
|
|
| ### Citation Information |
|
|
| ``` |
| @article{raychev2016probabilistic, |
| title={Probabilistic Model for Code with Decision Trees}, |
| author={Raychev, Veselin and Bielik, Pavol and Vechev, Martin}, |
| journal={ACM SIGPLAN Notices}, |
| pages={731--747}, |
| year={2016}, |
| publisher={ACM New York, NY, USA} |
| } |
| @inproceedings{allamanis2013mining, |
| title={Mining Source Code Repositories at Massive Scale using Language Modeling}, |
| author={Allamanis, Miltiadis and Sutton, Charles}, |
| booktitle={2013 10th Working Conference on Mining Software Repositories (MSR)}, |
| pages={207--216}, |
| year={2013}, |
| organization={IEEE} |
| } |
| ``` |
|
|
| ### Contributions |
|
|
| Thanks to @madlag (and partly also @ncoop57) for adding this dataset. |
