ds4se 0.0.1

Data Science for Software Engieering (ds4se) is an academic initiative to perform exploratory analysis on software engieering artifact and metadata. Data Management, Analysis, and Benchmarking for DL and Traceability

ds4se

Data Science for Software Engieering (ds4se) is an academic initiative to perform exploratory analysis on software engineering artifacts and metadata. Data Management, Analysis, and Benchmarking for DL and Traceability.

import ds4se.facade as facade

This file will become your README and also the index of your documentation.

Install

pip install ds4se

How to use

Traceability

To use the ds4se library to calculate trace link value of proposed trace link with given.

Supported technique model:
    VSM
    LDA
    orthogonal 
    LSA
    JS
    word2vec
    doc2vec

facade.TraceLinkValue("textfile.txt","source","techinque")

0.32

Analysis

Usage of ds4se model to calculate the number of documents of either source or target class

The method takes in two parameters, source artifacts and target artifacts, and it will do calculation for both classes.

The method returns a list of 4 integers:
1: number of documents for source artifacts;
2: number of documents for target artifacts;
3: source difference;
4: target difference.

result = facade.NumDoc("source","target")
source_doc = result[0]
target_doc = result[1]
difference_source = result[2]
difference_target = result[3]
print("The number of documents for source is {} , with {} source difference".format(source_doc, difference_source))
print("The number of documents for target is {} , with {} target difference".format(target_doc, difference_target))

The number of documents for source is 148 , with -17 source difference
The number of documents for target is 165 , with 17 target difference

Usage of ds4se model to calculate the vocabulary size of either source or target class

The method takes in two parameters, source artifacts and target artifacts, and it will do calculation for both classes.

The method returns a list of 4 integers:
1: vocabulary size for source artifacts;
2: vocabulary size for target artifacts;
3: source difference;
4: target difference.

vocab_result = facade.VocabSize("source", "target")
source = vocab_result[0]
target = vocab_result[1]
difference_source = vocab_result[2]
difference_target = vocab_result[3]
print("The vocabulary size for source is {} , with {} target difference".format(source, difference_source))
print("The vocabulary size for target is {} , with {} target difference".format(target, difference_target))

The vocabulary size for source is 103 , with -16 target difference
The vocabulary size for target is 119 , with 16 target difference

Usage of ds4se model to calculate the average number of token of either source or target class

The method takes in two parameters, source artifacts and target artifacts, and it will do calculation for both classes.

The method returns a list of 4 integers:
1: average number of token for source artifacts;
2: average number of token for target artifacts;
3: source difference;
4: target difference.

token_result = facade.AverageToken("source", "target")
source = token_result[0]
target = token_result[1]
difference_source = vocab_result[2]
difference_target = vocab_result[3]
print("The number of average token for source is {} , with {} source difference".format(source, difference_source))
print("The number of average token for target is {} , with {} target difference".format(target, difference_target))

The number of average token for source is 150 , with -16 source difference
The number of average token for target is 100 , with 16 target difference

Usage of ds4se model to retriev term frequency

The method takes in two parameters, 1: source artifacts, 2: target artifacts. and it will do calculation for both classes.

The method returns a dictonary with key: token; value: a list of count and frequency.

facade.VocabShared("source","target")

{'est': [111, 0.111], 'http': [139, 0.139], 'frequnecy': [178, 0.178]}

If we only need the term frequency of one of two classes, we can use Vocab() function

The filename should be the path to the file

facade.Vocab("filename")

{'est': [188, 0.188], 'http': [106, 0.106], 'frequnecy': [159, 0.159]}

For Shared Metrics

Using the following metrics to compute using both source and target artifacts, use the following funtions.

They all require two parameters: source and target artifacts.

And return one int value

Shared vocabulary size

facade.SharedVocabSize("source", "target")

189

Mutual information

facade.MutualInformation("source", "target")

104

Corss Entropy

facade.CrossEntropy("source", "target")

173

KL Divergence

facade.KLDivergence("source", "target")

188