danaderp
/
ds4se
mirror of https://github.com/WM-SEMERU/ds4se.git


  
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            ---

title: ds4se

keywords: fastai
sidebar: home_sidebar

summary: "Data Science for Software Engieering (ds4se) is an academic initiative to perform exploratory analysis on software engineering artifacts (e.g., requirements, issues, source code, or test cases) and metadata (e.g., repository logs, databases logs, or binaries). This EDA involves the following components:"
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<p>This file will become your README and also the index of your documentation.</p>

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<h2 id="Install">Install<a class="anchor-link" href="#Install">&#182;</a></h2>
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<p><code>pip install ds4se</code></p>

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<h2 id="How-to-use">How to use<a class="anchor-link" href="#How-to-use">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">ds4se.facade</span> <span class="k">as</span> <span class="nn">facade</span>
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<h2 id="Traceability">Traceability<a class="anchor-link" href="#Traceability">&#182;</a></h2>
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<p>To use the ds4se library to calculate trace link value of proposed trace link with given.</p>

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<pre><code>Supported technique model:
    VSM
    LDA
    orthogonal 
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    JS
    word2vec
    doc2vec</code></pre>

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<p>The function returns a tuple of two integers, with the first element as distance between two artifacts and the second element be the similarity between two artifacts, which is the traceability value.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">facade</span><span class="o">.</span><span class="n">TraceLinkValue</span><span class="p">(</span><span class="s2">&quot;source_string&quot;</span><span class="p">,</span><span class="s2">&quot;target_string&quot;</span><span class="p">,</span><span class="s2">&quot;LDA&quot;</span><span class="p">)</span>
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<pre>0.01</pre>
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<p>word2vec_metric is an optional parameter when using word2vec as technique, available metrics are: 
   <br> WMD
  <br>  SCM</p>

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<h2 id="Analysis">Analysis<a class="anchor-link" href="#Analysis">&#182;</a></h2>
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<h3 id="Usage-of-ds4se-model-to-calculate-the-number-of-documents-of-either-source-or-target-class">Usage of ds4se model to calculate the number of documents of either source or target class<a class="anchor-link" href="#Usage-of-ds4se-model-to-calculate-the-number-of-documents-of-either-source-or-target-class">&#182;</a></h3>
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<pre><code>The method takes in two parameters, a pandas dataframe for source artifacts and a pandas data frame for 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 (difference between previous two results);
4: target difference (same as above, but opposite).</code></pre>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">facade</span><span class="o">.</span><span class="n">NumDoc</span><span class="p">(</span><span class="s2">&quot;source&quot;</span><span class="p">,</span><span class="s2">&quot;target&quot;</span><span class="p">)</span>
<span class="n">source_doc</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">target_doc</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">difference_source</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="n">difference_target</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The number of documents for source is </span><span class="si">{}</span><span class="s2"> , with </span><span class="si">{}</span><span class="s2"> source difference&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">source_doc</span><span class="p">,</span> <span class="n">difference_source</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The number of documents for target is </span><span class="si">{}</span><span class="s2"> , with </span><span class="si">{}</span><span class="s2"> target difference&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">target_doc</span><span class="p">,</span> <span class="n">difference_target</span><span class="p">))</span>
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<pre>The number of documents for source is 160 , with 32 source difference
The number of documents for target is 128 , with -32 target difference
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<p>For all functions in analysis part, input should be pandas dataframe with following structure</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;contents&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;hello world&quot;</span><span class="p">,</span> <span class="s2">&quot;this is a content of another file&quot;</span><span class="p">]}</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">d</span><span class="p">)</span>
<span class="n">df</span>
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<h3 id="Usage-of-ds4se-model-to-calculate-the-vocabulary-size-of-either-source-or-target-class">Usage of ds4se model to calculate the vocabulary size of either source or target class<a class="anchor-link" href="#Usage-of-ds4se-model-to-calculate-the-vocabulary-size-of-either-source-or-target-class">&#182;</a></h3>
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<pre><code>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.</code></pre>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vocab_result</span> <span class="o">=</span> <span class="n">facade</span><span class="o">.</span><span class="n">VocabSize</span><span class="p">(</span><span class="n">source_df</span><span class="p">,</span> <span class="n">target_df</span><span class="p">)</span>
<span class="n">source</span> <span class="o">=</span> <span class="n">vocab_result</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">vocab_result</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">difference_source</span> <span class="o">=</span> <span class="n">vocab_result</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="n">difference_target</span> <span class="o">=</span> <span class="n">vocab_result</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The vocabulary size for source is </span><span class="si">{}</span><span class="s2"> , with </span><span class="si">{}</span><span class="s2"> target difference&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">difference_source</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The vocabulary size for target is </span><span class="si">{}</span><span class="s2"> , with </span><span class="si">{}</span><span class="s2"> target difference&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">difference_target</span><span class="p">))</span>
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<pre>The vocabulary size for source is 179 , with 35 target difference
The vocabulary size for target is 144 , with -35 target difference
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<h3 id="Usage-of-ds4se-model-to-calculate-the-average-number-of-token-of-either-source-or-target-class">Usage of ds4se model to calculate the average number of token of either source or target class<a class="anchor-link" href="#Usage-of-ds4se-model-to-calculate-the-average-number-of-token-of-either-source-or-target-class">&#182;</a></h3>
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<pre><code>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.</code></pre>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">token_result</span> <span class="o">=</span> <span class="n">facade</span><span class="o">.</span><span class="n">AverageToken</span><span class="p">(</span><span class="n">source_df</span><span class="p">,</span> <span class="n">target_df</span><span class="p">)</span>
<span class="n">source</span> <span class="o">=</span> <span class="n">token_result</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">token_result</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">difference_source</span> <span class="o">=</span> <span class="n">vocab_result</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="n">difference_target</span> <span class="o">=</span> <span class="n">vocab_result</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The number of average token for source is </span><span class="si">{}</span><span class="s2"> , with </span><span class="si">{}</span><span class="s2"> source difference&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">difference_source</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The number of average token for target is </span><span class="si">{}</span><span class="s2"> , with </span><span class="si">{}</span><span class="s2"> target difference&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">difference_target</span><span class="p">))</span>
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<pre>The number of average token for source is 107 , with 35 source difference
The number of average token for target is 143 , with -35 target difference
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<h3 id="Usage-of-ds4se-model-to-retriev-term-frequency">Usage of ds4se model to retriev term frequency<a class="anchor-link" href="#Usage-of-ds4se-model-to-retriev-term-frequency">&#182;</a></h3>
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<pre><code>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</code></pre>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">facade</span><span class="o">.</span><span class="n">VocabShared</span><span class="p">(</span><span class="n">source_df</span><span class="p">,</span><span class="n">target_df</span><span class="p">)</span>
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<pre>{&#39;est&#39;: [160, 0.16], &#39;http&#39;: [136, 0.136], &#39;frequnecy&#39;: [124, 0.124]}</pre>
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<p>If we only need the term frequency of one of two classes, we can use Vocab() function</p>
<p><strong>The filename should be the path to the file</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">facade</span><span class="o">.</span><span class="n">Vocab</span><span class="p">(</span><span class="n">artifacts_df</span><span class="p">)</span>
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<pre>{&#39;est&#39;: [141, 0.141], &#39;http&#39;: [136, 0.136], &#39;frequnecy&#39;: [156, 0.156]}</pre>
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<h3 id="For-Shared-Metrics">For Shared Metrics<a class="anchor-link" href="#For-Shared-Metrics">&#182;</a></h3>
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<p>Using the following metrics to compute using both source and target artifacts, use the following funtions.</p>
<p>They all require two parameters: source and target artifacts.</p>
<p>And return one int value</p>

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<p>Shared vocabulary size</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">facade</span><span class="o">.</span><span class="n">SharedVocabSize</span><span class="p">(</span><span class="n">source_df</span><span class="p">,</span> <span class="n">target_df</span><span class="p">)</span>
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<pre>112</pre>
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<p>Mutual information</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">facade</span><span class="o">.</span><span class="n">MutualInformation</span><span class="p">(</span><span class="n">source_df</span><span class="p">,</span> <span class="n">target_df</span><span class="p">)</span>
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<pre>127</pre>
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<p>Corss Entropy</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">facade</span><span class="o">.</span><span class="n">CrossEntropy</span><span class="p">(</span><span class="n">source_df</span><span class="p">,</span> <span class="n">target_df</span><span class="p">)</span>
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<pre>171</pre>
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<p>KL Divergence</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">facade</span><span class="o">.</span><span class="n">KLDivergence</span><span class="p">(</span><span class="n">source_df</span><span class="p">,</span> <span class="n">target_df</span><span class="p">)</span>
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<pre>152</pre>
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