Merge branch 'main' of https://github.com/fhdsl/AI_for_software into …

…main

docs/04-refactoring.md

@@ -6,7 +6,7 @@
 ## Learning Objectives
 
 - Describe how refactoring code involves optimization for maintainability, efficiency, and reuse
-- Explain why refactoring code is important for developers in the longterm 
+- Explain why refactoring code is important for developers in the long-term 
 - Recognize the benefits and limitations of using AI tools to refactor code, as well as why AI tools are uniquely poised to be beneficial
 - Implement prompt strategies that can be used to assist with refactoring code for correcting syntax, for adopting more consistent styling, for making code more concise, for making code easier to maintain, and for making code more efficient
 
@@ -547,7 +547,7 @@ proc.time() - start_time
 
 ```
 ##    user  system elapsed 
-##  11.986   0.003  11.988
+##  14.389   0.000  14.398
 ```
 
 :::{.query}
@@ -575,7 +575,7 @@ proc.time() - start_time
 
 ```
 ##    user  system elapsed 
-##   0.745   0.504   0.832
+##   0.670   0.392   0.698
 ```
 
 The `outer()` function performs the same calculation as the nested loop in the original code, but more efficiently. It returns a matrix of all possible combinations of x and y values, with each element of the matrix being the product of the corresponding x and y values. The `rowSums()` function is then used to sum the elements of each row of the matrix, which is equivalent to summing the products of x and y for each index `i` in the original loop. This method avoids the need for the nested loop, resulting in a faster and more efficient computation.
@@ -603,7 +603,7 @@ proc.time() - start_time
 
 ```
 ##    user  system elapsed 
-##   0.394   0.452   0.536
+##   0.283   0.344   0.394
 ```
 
 One optimized way to perform the same calculation is by using the `%*%` operator to perform matrix multiplication. This can be done by converting x and y to matrices and transposing one of them so that their dimensions align for matrix multiplication. This code should be much faster than the original implementation because it takes advantage of highly optimized matrix multiplication algorithms in R.

docs/no_toc/04-refactoring.md

@@ -6,7 +6,7 @@
 ## Learning Objectives
 
 - Describe how refactoring code involves optimization for maintainability, efficiency, and reuse
-- Explain why refactoring code is important for developers in the longterm 
+- Explain why refactoring code is important for developers in the long-term 
 - Recognize the benefits and limitations of using AI tools to refactor code, as well as why AI tools are uniquely poised to be beneficial
 - Implement prompt strategies that can be used to assist with refactoring code for correcting syntax, for adopting more consistent styling, for making code more concise, for making code easier to maintain, and for making code more efficient
 
@@ -547,7 +547,7 @@ proc.time() - start_time
 
 ```
 ##    user  system elapsed 
-## 231.734   0.076 231.807
+## 233.008   0.055 233.087
 ```
 
 :::{.query}
@@ -575,7 +575,7 @@ proc.time() - start_time
 
 ```
 ##    user  system elapsed 
-##   0.770   0.263   0.878
+##   0.551   0.348   0.701
 ```
 
 The `outer()` function performs the same calculation as the nested loop in the original code, but more efficiently. It returns a matrix of all possible combinations of x and y values, with each element of the matrix being the product of the corresponding x and y values. The `rowSums()` function is then used to sum the elements of each row of the matrix, which is equivalent to summing the products of x and y for each index `i` in the original loop. This method avoids the need for the nested loop, resulting in a faster and more efficient computation.
@@ -603,7 +603,7 @@ proc.time() - start_time
 
 ```
 ##    user  system elapsed 
-##   0.336   0.236   0.353
+##   0.287   0.296   0.370
 ```
 
 One optimized way to perform the same calculation is by using the `%*%` operator to perform matrix multiplication. This can be done by converting x and y to matrices and transposing one of them so that their dimensions align for matrix multiplication. This code should be much faster than the original implementation because it takes advantage of highly optimized matrix multiplication algorithms in R.

docs/no_toc/refactoring-code.html

@@ -374,7 +374,7 @@ <h1><span class="header-section-number">Chapter 4</span> Refactoring Code</h1>
 <h2><span class="header-section-number">4.1</span> Learning Objectives</h2>
 <ul>
 <li>Describe how refactoring code involves optimization for maintainability, efficiency, and reuse</li>
-<li>Explain why refactoring code is important for developers in the longterm</li>
+<li>Explain why refactoring code is important for developers in the long-term</li>
 <li>Recognize the benefits and limitations of using AI tools to refactor code, as well as why AI tools are uniquely poised to be beneficial</li>
 <li>Implement prompt strategies that can be used to assist with refactoring code for correcting syntax, for adopting more consistent styling, for making code more concise, for making code easier to maintain, and for making code more efficient</li>
 </ul>
@@ -746,7 +746,7 @@ <h2><span class="header-section-number">4.10</span> Code optimization</h2>
 <span id="cb49-16"><a href="refactoring-code.html#cb49-16" aria-hidden="true" tabindex="-1"></a><span class="co"># End timer</span></span>
 <span id="cb49-17"><a href="refactoring-code.html#cb49-17" aria-hidden="true" tabindex="-1"></a><span class="fu">proc.time</span>() <span class="sc">-</span> start_time</span></code></pre></div>
 <pre><code>##    user  system elapsed 
-## 231.734   0.076 231.807</code></pre>
+## 233.008   0.055 233.087</code></pre>
 <div class="query">
 <p>What is a faster, more optimized way of running the following R code?</p>
 </div>
@@ -765,7 +765,7 @@ <h2><span class="header-section-number">4.10</span> Code optimization</h2>
 <span id="cb51-12"><a href="refactoring-code.html#cb51-12" aria-hidden="true" tabindex="-1"></a><span class="co"># End timer</span></span>
 <span id="cb51-13"><a href="refactoring-code.html#cb51-13" aria-hidden="true" tabindex="-1"></a><span class="fu">proc.time</span>() <span class="sc">-</span> start_time</span></code></pre></div>
 <pre><code>##    user  system elapsed 
-##   0.770   0.263   0.878</code></pre>
+##   0.551   0.348   0.701</code></pre>
 <p>The <code>outer()</code> function performs the same calculation as the nested loop in the original code, but more efficiently. It returns a matrix of all possible combinations of x and y values, with each element of the matrix being the product of the corresponding x and y values. The <code>rowSums()</code> function is then used to sum the elements of each row of the matrix, which is equivalent to summing the products of x and y for each index <code>i</code> in the original loop. This method avoids the need for the nested loop, resulting in a faster and more efficient computation.</p>
 </div>
 <p>So much faster! We can look at another option by regenerating the response:</p>
@@ -784,7 +784,7 @@ <h2><span class="header-section-number">4.10</span> Code optimization</h2>
 <span id="cb53-12"><a href="refactoring-code.html#cb53-12" aria-hidden="true" tabindex="-1"></a><span class="co"># End timer</span></span>
 <span id="cb53-13"><a href="refactoring-code.html#cb53-13" aria-hidden="true" tabindex="-1"></a><span class="fu">proc.time</span>() <span class="sc">-</span> start_time</span></code></pre></div>
 <pre><code>##    user  system elapsed 
-##   0.336   0.236   0.353</code></pre>
+##   0.287   0.296   0.370</code></pre>
 <p>One optimized way to perform the same calculation is by using the <code>%*%</code> operator to perform matrix multiplication. This can be done by converting x and y to matrices and transposing one of them so that their dimensions align for matrix multiplication. This code should be much faster than the original implementation because it takes advantage of highly optimized matrix multiplication algorithms in R.</p>
 </div>
 <p>While this second suggestion is faster, you will need to consider what aspects of the codebase are most important in each instance. For example, this code runs more quickly, but <a href="https://stat.ethz.ch/R-manual/R-patched/library/base/html/matmult.html">the <code>%*%</code> operator</a> might be unfamiliar to some R programmers. In cases where efficiency is less important, or the data are not large, you might consider maximizing readability.</p>

docs/refactoring-code.html

@@ -374,7 +374,7 @@ <h1><span class="header-section-number">Chapter 4</span> Refactoring Code</h1>
 <h2><span class="header-section-number">4.1</span> Learning Objectives</h2>
 <ul>
 <li>Describe how refactoring code involves optimization for maintainability, efficiency, and reuse</li>
-<li>Explain why refactoring code is important for developers in the longterm</li>
+<li>Explain why refactoring code is important for developers in the long-term</li>
 <li>Recognize the benefits and limitations of using AI tools to refactor code, as well as why AI tools are uniquely poised to be beneficial</li>
 <li>Implement prompt strategies that can be used to assist with refactoring code for correcting syntax, for adopting more consistent styling, for making code more concise, for making code easier to maintain, and for making code more efficient</li>
 </ul>
@@ -746,7 +746,7 @@ <h2><span class="header-section-number">4.10</span> Code optimization</h2>
 <span id="cb49-16"><a href="refactoring-code.html#cb49-16" aria-hidden="true" tabindex="-1"></a><span class="co"># End timer</span></span>
 <span id="cb49-17"><a href="refactoring-code.html#cb49-17" aria-hidden="true" tabindex="-1"></a><span class="fu">proc.time</span>() <span class="sc">-</span> start_time</span></code></pre></div>
 <pre><code>##    user  system elapsed 
-##  11.986   0.003  11.988</code></pre>
+##  14.389   0.000  14.398</code></pre>
 <div class="query">
 <p>What is a faster, more optimized way of running the following R code?</p>
 </div>
@@ -765,7 +765,7 @@ <h2><span class="header-section-number">4.10</span> Code optimization</h2>
 <span id="cb51-12"><a href="refactoring-code.html#cb51-12" aria-hidden="true" tabindex="-1"></a><span class="co"># End timer</span></span>
 <span id="cb51-13"><a href="refactoring-code.html#cb51-13" aria-hidden="true" tabindex="-1"></a><span class="fu">proc.time</span>() <span class="sc">-</span> start_time</span></code></pre></div>
 <pre><code>##    user  system elapsed 
-##   0.745   0.504   0.832</code></pre>
+##   0.670   0.392   0.698</code></pre>
 <p>The <code>outer()</code> function performs the same calculation as the nested loop in the original code, but more efficiently. It returns a matrix of all possible combinations of x and y values, with each element of the matrix being the product of the corresponding x and y values. The <code>rowSums()</code> function is then used to sum the elements of each row of the matrix, which is equivalent to summing the products of x and y for each index <code>i</code> in the original loop. This method avoids the need for the nested loop, resulting in a faster and more efficient computation.</p>
 </div>
 <p>So much faster! We can look at another option by regenerating the response:</p>
@@ -784,7 +784,7 @@ <h2><span class="header-section-number">4.10</span> Code optimization</h2>
 <span id="cb53-12"><a href="refactoring-code.html#cb53-12" aria-hidden="true" tabindex="-1"></a><span class="co"># End timer</span></span>
 <span id="cb53-13"><a href="refactoring-code.html#cb53-13" aria-hidden="true" tabindex="-1"></a><span class="fu">proc.time</span>() <span class="sc">-</span> start_time</span></code></pre></div>
 <pre><code>##    user  system elapsed 
-##   0.394   0.452   0.536</code></pre>
+##   0.283   0.344   0.394</code></pre>
 <p>One optimized way to perform the same calculation is by using the <code>%*%</code> operator to perform matrix multiplication. This can be done by converting x and y to matrices and transposing one of them so that their dimensions align for matrix multiplication. This code should be much faster than the original implementation because it takes advantage of highly optimized matrix multiplication algorithms in R.</p>
 </div>
 <p>While this second suggestion is faster, you will need to consider what aspects of the codebase are most important in each instance. For example, this code runs more quickly, but <a href="https://stat.ethz.ch/R-manual/R-patched/library/base/html/matmult.html">the <code>%*%</code> operator</a> might be unfamiliar to some R programmers. In cases where efficiency is less important, or the data are not large, you might consider maximizing readability.</p>